NHS Digital Data Release Register - reformatted

Iqvia Ltd projects

461 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).


🚩 Iqvia Ltd was sent multiple files from the same dataset, in the same month, both with optouts respected and with optouts ignored. Iqvia Ltd may not have compared the two files, but the identifiers are consistent between datasets, and outside of a good TRE NHS Digital can not know what recipients actually do.

NIC-373563 - IQVIA Solutions UK Limited & IQVIA Technology Services Ltd — DARS-NIC-373563-N8Z9J

Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant, No (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012, Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii)

Purposes: Yes (Supplier, Commercial)

Sensitive: Non Sensitive, and Sensitive, and Non-Sensitive

When:DSA runs 2019-03-01 — 2020-02-28 2017.06 — 2022.04.

Access method: Ongoing, One-Off

Data-controller type: IQVIA SOLUTIONS UK LIMITED, IQVIA TECHNOLOGY SERVICES LTD., IQVIA LTD, IQVIA SOLUTIONS UK LIMITED, IQVIA TECHNOLOGY SERVICES LTD., IQVIA LTD, IQVIA TECHNOLOGY SERVICES LTD.

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Outpatients
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Accident and Emergency
  4. Emergency Care Data Set (ECDS)
  5. HES:Civil Registration (Deaths) bridge
  6. Civil Registration - Deaths
  7. Summary Hospital-level Mortality Indicator
  8. Civil Registration (Deaths) - Secondary Care Cut
  9. HES-ID to MPS-ID HES Accident and Emergency
  10. HES-ID to MPS-ID HES Admitted Patient Care
  11. HES-ID to MPS-ID HES Outpatients

Objectives:

IMS Health is a brand comprised of a number of legal entities which provide technology and services to healthcare. This application/agreement is a request for pseudonymised record-level HES data which will be controlled by two legal entities:
• IMS Health TS
• IMS Health UK ltd
Hereafter, these two entities will be referred to collectively as IMS Health.

IMS Health will use the HES data to perform two types of service:
1. Data visualisation and benchmarking tools which includes:
i) Care Pathway Analyser (formerly visualise treatment pathways)
ii) Hospital Feedback services
iii) Visualise Healthcare Data
2) Advanced Statistical Analysis (formerly referred to as structured disease analysis)

1) The data visualisation and benchmarking tools are described below:
• Care Pathway Analyser (CPA). Presents users with simple views of aggregated care pathways. This allows investigation of the causes of variation in patient pathways and the subsequent impact on service delivery.
• Hospital Feedback Services (HFS). A dashboard allowing chief pharmacists to optimise their use of medicines. It will also allow them to monitor their own performance against internal targets and benchmark against similar hospitals. This service is still in development. NHS Trusts will be granted access to HFS in exchange for continued supply to non-identifiable prescription data and agreement that IMS Health Ltd can use the data for more further research.
• Visualise Healthcare Data (VHD). A suite of tools/reports that allows users to perform queries on aggregated HES data then view graphs and tables.

2) Advanced Statistical Analysis includes: diagnostic algorithm development, epidemiology, health economics and outcomes research studies.

Both services will only be provided to the following categories of types of organisation:
- Providers of healthcare services
• Clinical Commissioning Groups
• Commissioning Support Units (CSU’s)
• Hospital Trusts
• Private secondary care providers
• Mental Health trusts
• Community Provider Trusts
• Pharmacies
• NHS England
• Public Health England
• Health and Wellbeing Boards
- Universities
- Life science industry
• Pharmaceutical companies
• Medical Device companies
• Industry bodies – limited to the Association of the British Pharmaceutical Industry (ABPI), Ethical Medicines Industry Group (EMIG) and the Proprietary Associated of Great Britain (PAGB)

Third parties will only see aggregated and small number suppressed data.

The number of organisations to whom IMS Health provide products and services changes regularly. In the year to date IMS Health have worked on 31 Advanced Statistical Analysis projects and Data Visualisation and Benchmarking services using HES data held under this DSA. Of these projects, approximately half were for repeat customers (who had purchased at least one other tool or project from IMS Health within that period). When finalised, HFS will be given to all NHS Trusts.

IMS Health understands the importance of data minimisation and outline IMS Health’s requirement for national, timely HES data in the following paragraphs.

IMS Health requires national data to enable the end users of IMS Health’s tools to benchmark against organisations in their local area or with similar demographic characteristics. IMS Health also requires national data to inform economic analyses for inclusion in submissions to NICE, which makes decisions at a national level. HFS is intended for all chief pharmacists in NHS Trusts.

The requirement for timely data is because the commissioners and providers to whom IMS Health provide IMS Health’s tools need to make decisions based on the most up-to-date information. IMS Health won an open tender to perform a medicines optimisation study for a group of cancer treatment providers. More detail on this project is given in later sections.

Historic data is required to support Advanced Statistical Analysis projects, as historical data allows robust analysis of trends over time.

Yielded Benefits:

Examples of how previous projects have provided benefit to patient care are given below. Cancer Vanguard Medicines Optimisation Project IQVIA supported the NHS Cancer Vanguard (The Christie, UCLH and Royal Marsden NHS Foundation Trusts) to optimise the care and use of medicines for mCRC (Metastatic Colorectal Cancer) patients, whilst reducing the unnecessary variation in patient care. IQVIA used Advanced Statistical Analysis and the Care Pathway Analyser tools to deliver this project, involving a review of medicines usage at each centre and identification of avoidable variation in episodes of care. The output was a model to reduce the cost of treating cancer to ensure clarity around best practice processes. Patient reported outcomes also ensured that the relationship between best practice and improvement in patients’ quality of life is quantified. The analysis was developed alongside chief pharmacists and associated clinical teams with results shared with healthcare professionals in a way that allowed them to improve patients’ quality of life in a cost-effective manner. More information about the Cancer Vanguard can be found here - http://cancervanguard.nhs.uk/about/. Multi-Hospital Site DVT Treatment Variation Project Since their introduction novel oral anticoagulants (NOACS) have not experienced the uptake anticipated following their NICE recommendation. By utilising these new compounds, the NHS can alter the way Deep Vein Thrombosis (DVT) treatment pathways are delivered. The goals of this partnership project were: • Understand how DVT is currently treated (nationally utilising HES data assets) • Compare how individual Trusts treat patients with DVT Nationally and within Benchmark Trusts • Quantify the potential financial implications associated with a change in DVT treatment pathway • Outline potential change tactics and key messages that could be aligned to a shift in treatment approach The National analysis presented key observations associated with the current clinical management of DVT. DVT hospital treatment is resource intensive placing scarce hospital resources under further pressure with variation in current care pathways. Findings uncovered from the study identified key drivers that all impact on the financial sustainability of local DVT services. The choice of clinical pathway will impact on Trust financial performance, working with key stakeholders to implement alternative pathways can mitigate financial risk. A key outcome of the work presented to representatives from 90 UK Trusts was the need to understand how the clinical strategy adopted by a Trust can impact the level of financial risk the Hospital is exposed to. Greater Manchester Liver Disease Working with key NHS, academic and professional bodies from the Greater Manchester region IQVIA presented an overview of how liver disease, defined by a cohort of ICD.10 diagnosis codes, was currently being managed across the region. This overview of liver disease treatment pathways was delivered utilising National HES data assets with National Care Pathway Analytics technology deployed over the data. This provided IQVIA with treatment pathway visualisation at Clinical Commissioning Group level showing aggregated treatment pathway volumes, the cost associated with these pathways, the typical length of stay and the average number of treatment events per pathway. Isolating key performance indicators by each of the 12 Greater Manchester CCG’s highlighted the variation in both clinical management of the disease area, and the cost and capacity implications of that variation. A key outcome of the work will be the utilisation of the analysis to improve and standardise the management of patients’ within this disease cohort. Salford Royal Hospital – Unwarranted treatment pathway variation IQVIA presented the National Care Pathway Analytics solution (NCPA - based on care pathway technology applied to HES data assets) solution to Salford Royal NHSFT. However, when demonstrating the NCPA solution to the Trust, the Trust commented that; NCPA, provided an insight into the performance of Salford Royal NHSFT and allowed them to benchmark against other Trusts both nationally and within the Greater Manchester region and identified that there was an issue. In order to investigate further and allow for real change, deeper analysis on local Trust data, taking in additional datasets not available in HES was required. However without the initial NCPA analysis, these issues may have gone unnoticed. Instead of individual “APC – Admitted Patient Care Events” the Trust would ideally want to see more detailed event level granularity within the ‘hospital stay’ part of the treatment pathway, i.e. visualisation of a daily activity breakdown of the patients’ treatment The Trust have developed for 2017/18 a local programme between the ‘Better Care Lower Cost – BCLC’ team and ‘Quality Improvement - QI team’. The programme would utilise QI methodologies to be used on local projects which would benefit from this approach. The BCLC and QI teams are developing a 90-day cycle approach to service review with a view to identifying 10% gains in both quality and efficiency. Trust senior directors invited IQVIA to develop Care Pathway technology to support them in the delivery of this local improvement initiative. Because of the utilisation of National HES data delivered to the Trust in a unique and creative way has led to the development and delivery of a detailed local project that can enable the Trust to support the identification and reduction of unwarranted variation in patient treatment pathways. Outcome – Delivered to Client, Acute Care Pathway Analytics product that will enable and support the delivery of a reduction in clinical variation, improving Length of Stay (LOS), increase profitability and deliver improved operational efficiency UCLH – Trauma & Orthopaedics Clinical Productivity This Clinical Productivity Pilot aimed to understand the causes of clinical variation in the provision of T&O services at UCLH, using a combination of benchmarking (HES data), and “deep dive” analytical capabilities that IQVIA tested with Trust members including operational, financial and clinical stakeholders. From an overall analysis of clinical variation across the T&O speciality, knee replacement and hip replacement represented some of the biggest opportunities to reduce length of stay. To fully understand how actionable and feasible these opportunities were to the Trust, a detailed analysis around knee replacement elective procedures was completed. The results of the analysis into knee replacement indicated that: • Some of the LOS variation that could be reduced are patients that are currently being discharged on Thursdays and Fridays. Patients characteristics (including complexity measures) between key consultants is indicated to be comparable. Data highlighted that key consultants operate on a specific day from Wednesday to Friday, generating three types of “pathways” to compare against. • Further emphasis in ward rounds/discharges during Wednesdays could be worth investigating further (most of the capacity gaps were isolated to this day). • By targeting only 20% of the potential opportunity, a reduction of 107 Bed Days across a year could be achievable. The main purpose of this work was to provide actionable insights and support discussions that the Trust might undertake for further service transformation involving the provision of T&O services. Going further, the same data assets, methodologies and approaches used in this pilot study were suggested to be used to identify potential opportunities in other specific procedures, or specialties to inform wider a case for change and improve further operational and clinical performance. Greater Manchester breast cancer service analysis, a joint working project led by the Christie NHS FT, Novartis and IQVIA The principle of this joint working project is that the Greater Manchester (GM) secondary breast cancer service could be optimised and perceived variations in access to treatment addressed if an analytical approach to pathway optimisation was conducted. This can be achieved through the analysis of data generated by routine delivery of care, in particular Hospital Episode Statistics, for breast cancer services in GM involving The Christie. The project consists of two components, pathway analysis and patient pathway experience. The project, due to conclude summer 2018, will provide a visual representation of the secondary breast cancer service in order to understand the variation of access to breast cancer services that exist to the people of GM and build a case for change that can be shared with operational healthcare leaders in the GM region. This project is based around partnership working and the collaborative sharing of insights and expertise between the Christie/Greater Manchester and Novartis, with the support of IQVIA, in order to optimise the treatment of secondary breast cancer within Greater Manchester.

Expected Benefits:

IMS operates on a project by project basis. Each project using this data source must generate benefit to healthcare, for example by:
• Providing detailed evidence based recommendations for how to improve care in specific organisations or therapy areas
• Giving healthcare professionals (HCPs) the ability to understand their own organisation’s performance via dashboards and reports; enabling them to reduce cost whilst delivering best practice care
• Providing analyses to decision making bodies such as the European Medicines Agency and the National Institute for Health and Care Excellence; in order to enable them to grant patients access to innovative medicines
• Contributing to knowledge to the medical community in order to stimulate further research into improving patient care

Examples of how previous projects have provided benefit to patient care are given below.

Developing diagnostic pathways in Fabry Disease:
IMS Health developed a diagnostic algorithm for patients with Fabry disease. In current ICD-10 coding the 4 characters code (E75.2 other sphingolipidosis) encompasses 5 different diseases: Gocher disease, Krabbe disease, Niemann-Pick disease and Metachromatic leukodystrophy. Despite the similarities in disease genesis the symptoms, treatment pathways, procedures and prognosis are different. By identifying the actual underlying disease patient would be put on the correct treatment pathway more quickly and better managed their condition.

The project involved working with Lysosomal storage disorder (LSD) clinical experts to understand the different diseases, the epidemiology and the diagnosis and treatment pathway.

Clinicians then worked with IMS to identify inclusion and exclusion criteria for Fabry disease based on the specialties visited by the patient, associated diagnosis codes (ICD-10 codes), procedures and treatments performed (OPCS codes), and LSD specialty centres visited (key specialist centres). The team also divided some of the variables by age of the patients to define patients for disease that typically affect certain age groups. The output was a logic-based algorithm which could be used to identify Fabry disease patients in routine clinical practice.

This project was completed in March 2016, the expected benefit is an improvement in the speed and accuracy of Fabry disease diagnoses.

Analysis and validation of musculoskeletal services for the NHS and Care UK:
Working with Aylesbury Vale CCG, Chiltern CCG, Buckinghamshire NHS Trust & Care UK, IMS Health modelled the level of service in changing environments over the next five-year period in order to improve their long-term planning process. The analysis used HES data plus data supplied from 8 CCGs.

The IMS Health team designed interventions to make the service more efficient and compiled forecasts to show the impact of these interventions on the forecast. The analysis was initially summarised in a presentation but subsequently delivered as dashboard that allowed the clients to model and understand the impact of pursuing different strategies for transformation and therefore inform decision making. For example, the model predicted that reducing the rate of inpatient spells with excess bed days had a low impact on overall MSK spend; however, reducing the rate of inpatient spells where the patient had complications or comorbidities or moving outpatient appointments to the community had a much greater potential to increase efficiency.

The research proposal for this project was submitted in October 2014. The final analysis was delivered in February 2016. The expected benefit is that this tool will allow HCPs to understand how to deliver more effective cost saving programmes.

Patient profiling and pathway analysis for University Hospital South Manchester:
In response to a requirement from a senior clinician at the University Hospital of South Manchester (UHMS), IMS performed an exploratory analysis using VHD in pneumonia and cellulitis. In both diseases, the analysis showed that more than half of all admissions were in patients from the most deprived 20% of neighbourhoods. The analysis went on to benchmark UHSM against its peers and found it had the third highest readmissions ratio in the region.

The UHSM project also included analysis of patient pathways. The analysis found that the average pneumonia pathway was 69% longer than the national average and cost the NHS 37% more. Further analysis showed 38% of pneumonia pathways in Manchester contained at least one COPD-related event; this was 10 percentage points more than the national average. On average, this group of pathways was more expensive and longer than the group without a recorded COPD event.

The results of this work were presented to the Trust in February 2016. The Trust expects to reduce costs and improve patient outcomes by applying best practice from Trusts with a similar case mix.

Analysis of cardiology pathways for the Heart of England NHS Foundation Trust:
CPA and VHD were used to review the cardiology services in the Heart of England Foundation Trust. The analysis, combined with the Trust’s own data, aimed to improve the efficiency of care. The analysis was presented at various stages to a team from the Trust in early 2016. Following on from the analysis, IMS Health recommended providing care based on clusters of procedures as this would allow the Trust to monitor consistency more closely and improve demand forecasting.

IMS Health expects that this analysis will allow the Trust to improve care by being better prepared for demand for cardiology services.

Using CPA to streamline hip replacement pathways in Cambridge and Peterborough CCG:
CPA in combination with HES and the CCG’s own local activity data was used to establish a gold standard pathway in Cambridgeshire and Peterborough for four providers in the region. Treatment pathway analysis and benchmarking against similar CCGs enabled them to envisage where the NHS’s Cost Improvement Programme (CIP) and the Quality, Innovation Productivity and Prevention (QIPP) programme could be delivered.

In the words of the Local Chief Officer “IMS Health provided me the insight to see where CIP and QIPP could be delivered by commissioning shorter pathways in line with best practice”

This work was presented in February 2016. The expected benefit is that this analysis will help HCPs deliver hip replacements safely and efficiently in line with best practice.

Three further examples of projects under development and their expected benefits are:

Cancer Vanguard Medicines Optimisation Project:
IMS Health has won a tender with a group of NHS Trusts. The aim of the project is to optimise the use of cancer medicines and reduce the unnecessary variation in cancer care and is currently in the scoping phase. IMS Health will use Advanced Statistical Analysis and the Care Pathway Analyser tool to deliver this project; it will involve a review of medicines usage in cancer, and identification of avoidable variation. The output will be a model to reduce the cost of treating cancer to ensure clarity around best practice processes. Patient reported outcomes will also ensure that the relationship between best practice and improvement in patients’ quality of life is quantified.

This analysis is being developed alongside HCPs and the expected benefit is that the results will be presented back to HCPs in a way that will allow them to improve patients’ quality of life in a cost effective manner.

More information about the Cancer Vanguard can be found here - http://cancervanguard.nhs.uk/about/.

Staffordshire CCGs and the Rightcare programme:
IMS Health is working with the Director of Strategic Programmes for a group of CCGs including: Cannock and Chase CCG, Stafford and Surrounds CCG, South East Staffs and Seisdon Peninsula CCG. The Director would like to use CPA to support the implementation of the NHS Rightcare programme. The Director had the following to say about the initiative:

“NHS Rightcare (http://www.rightcare.nhs.uk/) promotes the principle of eliminating unwarranted variation in healthcare. The IMS care pathway analysis tool allows commissioners and providers to see variation in care provided and benchmark compliance with best practice utilising national HES (Hospital Episode Statistics) data in conjunction with locally available data. It is therefore a potentially valuable tool in allowing commissioners and providers to redesign pathways to achieve high quality affordable care.”

Hospital Feedback Services:
IMS Health is committed to ensuring that the NHS chief pharmacists have accurate and up-to-date information in order to better manage their drug dispensing. The IMS medicines optimisation dashboard is designed to include IMS Health’s Hospital Pharmacy Audit data and National HES data.

The ability to include HES data is a vital component in ensuring that the output accurately reflects the seasonal variation in hospital activity and medicines usage. For example, the antibiotic usage dashboard includes the following report: Ratio of Defined Daily Dose of all dispensed antibacterial (ATC J1) products per 1000 admissions. It is the HES data that ensures accuracy on the 1000 admissions and enables IMS Health to account for seasonal variation in the analysis.

IMS Health will provide HFS to all hospital trusts. It is being designed in collaboration with the chief pharmacist community to ensure that it meets their needs. IMS Health expects HFS to benefit healthcare by allowing chief pharmacist to improve prescribing efficiency leading to a financial savings; it will also allow them to better forecast the amount of medicines required and therefore prevent waste:

Outputs:

For clarity services covered within this application only produce two types of output:
• Dashboards
• Aggregated tables

In both cases outputs are aggregated and small numbers suppressed in line with the HES Analysis Guide. Details for each of the services are given below.

Visualise Healthcare Data (VHD):
VHD is an internet browser based application, an iPad application or a bespoke report. Users are given role based access to the applications. The applications allow users to produce graphical and tabular estimates of burden of disease, cost of care, common comorbidities and similar analyses. These analyses may be stratified by diagnosis, organisation and other similar parameters.

Care Pathway Analyser (CPA):
CPA is currently an internet browser based application and other delivery methods are in development. CPA will either be deployed directly to users or used to support consulting projects. In the former users are given role based access to the application which will allow them to analyse images of aggregated pathways. In the latter, outputs will be presentations and reports containing pathway images as well as IMS Health recommendations.

Hospital Feedback Services (HFS):
Version 1.0 of the HFS tool has been presented to the NHS as of December 2016 (as a browser-based application) and other delivery methods are in development. Chief pharmacists will be given role-based access to a dashboard which will show them aggregated HES data, aggregated prescribing data and performance indicators. These data will be presented in graphs and tables. It is expected that log in details for Version 1.0 will be supplied by the end of Q1 2017.

Advanced Statistical Analysis
The data included in advanced statistical analysis are always aggregated and small number suppressed in line with the HES Analysis Guide. These outputs are produced to meet different objectives and delivered in different ways. A health economic analysis may require analysis of the data to estimate the cost of managing a given condition then used as an input in an economic model for a NICE submission. Developing a diagnostic algorithm will result in the production of a formula which may be presented to clinicians in an Excel based calculator with an explanatory report or presentation. Many of the outputs of advanced statistical analysis are reported in journal articles or conference presentations.

Processing:

IMS Health will receive the data from HSCIC and will apply derivations. No linkage is carried out to other datasets. In the context of this application/agreement applying derivations does not mean linking to other patient-level information. In this application/agreement, applying derivations means that IMS Health will use non-identifiable data to derive new information. For example, length of stay is approximated using the relationship between admission and discharge dates and the cost of an admission is approximated using the NHS payment by results tariff.

For data visualisation and benchmarking services, further derivations are applied to allow benchmarking and the data is presented in dashboards alongside other IMS Health and publically available data sources e.g. Quality Outcomes Framework data. All data visualisation and benchmarking tools are hosted by IMS Health. All data seen by end users is aggregated, small number are suppressed and are compliant with the HES Analysis Guide. Usage of these tools is auditable and role based access controls are applied. Customers using these tools are contractually prevented from using the data for solely commercial purposes.

For advanced scientific analysis, IMS Health produce bespoke analysis for external organisations on a project by project basis. All requests for bespoke analysis are subject to review by an independent scientific advisory committee (ISEAC – details in the following paragraph) who review the proposed study design. If ISEAC approves the study, it is logged on an access control register and the IMS Health researchers are allowed to access the relevant subset of HES data. The researchers will present the results of their analysis to external organisations in the form of aggregated, small number suppressed tables compliant with the HES Analysis Guide. These outputs may also take the form of counts, proportions or formulae. Anonymised abstracts will be published on the IMS Health global bibliography 6-12 months after completion of the study.

ISEAC is a group of medical and scientific advisors who are independent of IMS Health.


Hospital Treatment Insights — DARS-NIC-13925-Q7R2D

Opt outs honoured: N, Yes - patient objections upheld, No - data flow is not identifiable, Anonymised - ICO Code Compliant, No, Yes (Mixed, Mixture of confidential data flow(s) with consent and flow(s) with support under section 251 NHS Act 2006, Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s))

Legal basis: Health and Social Care Act 2012, Section 251 approval is in place for the flow of identifiable data, Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 - s261 - 'Other dissemination of information',

Purposes: Yes (Supplier, Commercial)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2019-07-01 — 2020-06-30 2017.06 — 2021.05.

Access method: Ongoing

Data-controller type: IQVIA SOLUTIONS UK LIMITED, IQVIA LTD, IQVIA SOLUTIONS UK LIMITED, IQVIA LTD

Sublicensing allowed: No, Yes

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. Hospital Episode Statistics Outpatients
  3. Hospital Episode Statistics Accident and Emergency
  4. Hospital Episode Statistics Critical Care
  5. Emergency Care Data Set (ECDS)
  6. IQVIA Data Quality Reports

Objectives:

IMS Health Ltd is an information and technology company serving the health care industry. IMS Health Ltd produces a longitudinal research database, Hospital Treatment Insights (HTI) and in collaboration with the CPRD produces HTI-CPRD-GOLD. This database contains unique information on diagnosis, treatment and drug usage across primary and secondary care in England and HTI is currently the only population based database available for monitoring the safety of medicines used in the secondary care setting. NHS Digital acts as a Trusted Third Party and provides linkage services for both HTI and HTI-CPRD-GOLD datasets.

The HTI data links record level pseudonymised, non-sensitive HES data to IMS Health Ltd's unique database of hospital prescribing data. The HTI-CPRD-GOLD data is a subset of the HTI data and links record level HTI data to CPRD-GOLD data. Currently there are 4.5 million patients in HTI data and 320,000 patients in HTI-CPRD-GOLD. The HTI and HTI-CPRD-GOLD data are treated as separate databases for study purposes so a researcher must specify which data they are requesting accessing to. Researchers accessing these datasets will be substantive employees of IMS Health Ltd or external researchers who have an honorary contract with IMS Health Ltd.

1. Data will be used to undertake a programme of research studies, in two main areas, aimed at promoting public health which will include:

Advanced statistical analysis
• epidemiology
• natural history of disease
• health economics and outcomes research
• drug exposures

Drug safety monitoring
• monitoring of adverse events for newly licenced drugs prescribed in secondary care
• pharmacovigilance

In all cases, purposes are restricted to those for the provision of health care or adult social care, or the provision of health.

Access to the data will be as follows:

1. Regulatory Authorities. Regulators such as the Medicines and Healthcare Products Regulatory Agency (MHRA) and the European Medicines Agency (EMA) will investigate potential adverse drug reactions (ADR) in marketed products. When new ADRs are discovered regulators can recommend actions to limit harm to patients exposed to the products. Responsiveness is key as products are already marketed meaning that further events could occur immediately. Therefore it is crucial to have data, like HTI and HTI-CPRD-GOLD, readily available that will allow direct investigation of ADR or evaluation of the likely public health impact should the potential ADR be established as a real effect.

Examples of current studies
The EMA has expressed interest in accessing HTI data for the purpose of investigating ADRs. The EMA has access to data sources that collect information on drug exposure and clinical events in general practice. However, the EMA is responsible for pharmacovigilance in many products that are only used in a hospital setting and currently has no directly accessible data on the use of these drugs and, in particular, no data on clinical events in patients exposed to the products except those supplied via spontaneous reporting systems. These latter are frequently the source of signals regarding potential ADRs and hence have limited use in further investigation of the signal. For this reason the Agency is in need of information on drug use in a hospital setting. Thus the EMA considers that access to HTI data is likely to have an important role in improving the use of medicines used in hospitals and could have a significant impact on public health.

2. Arm’s Length Bodies (ALBs) involved in Health and Social Care. ALBs, such as NICE, can use HTI to monitor adherence to clinical guidance and decisions regarding uptake of innovative therapies, in order to reduce variation in the delivery of healthcare. ALBs concerned more directly with the provision of healthcare such as NHS England and Public Health England will be able to measure equity of access to treatments in the NHS using HTI and, therefore, reduce health inequality.

HTI will allow the study of key performance measures used by the NHS, in association with pharmaceutical treatments in the following areas:

• Uptake and utilisation of new and existing therapies
• Medical vs surgical treatment rates associated with specific pharmaceutical treatments
• Readmission rates associated with specific pharmaceutical treatments
• Rates of elective vs non elective admissions in patients following different treatment regimens

Examples of current studies
NHS Digital’s Prescribing and Medicine’s team have spoken with IMS Health Ltd about using HTI data to generate extracts and analyses related to secondary care prescribing data. This work would help to identify trends and variation in secondary care prescribing to support national policy. As NHS Digital work closely with a range of organisations which have an interest in this information, including the NHS Business Services Authority, NICE and the Department of Health the outputs of this collaboration could have wide-reaching benefits across a number of healthcare bodies.

3. Medical researchers from academia and other types of organisation such as patient groups, charitable trusts and pharmaceutical companies. HTI will be used to undertake research into disease management and outcomes to improve patient care, health economic studies, comparative effectiveness studies and drug utilisation studies. These studies support the long term sustainability of the NHS by evaluating cost effectiveness. This will become increasingly important as drugs become more expensive placing a higher financial burden on the health system. The topic of sustainability will be a focus area for researchers, particularly as 94% of new molecular entities will be for specialist care delivered within the hospital setting.

Current academic relationships:
London School of Hygiene and Tropical Medicine (Faculty of Epidemiology and Population Health) – working with the epidemiology group to study the cardiovascular outcomes of varying cancer treatments in survivors of breast cancer.

University College London (Dept of Public Health) – a validation study to determine the strengths and limitations of the data for antibiotic research also a second piece of work on babies diagnosed with Respiratory syncytial virus treated with Pavalizumab.

King's College London (School of Biomedical Sciences) - a five-year collaboration (2015-2019) on publication stream initially exploring prescribing patterns over time of novel anti-coagulants (NOACS), expanding to a wider range of therapies after proof-of-concept results in grant funding.

Brighton & Sussex Medical School (Division of Primary Care and Public Health) - Associate Membership in proposing and establishing the Centre for Interdisciplinary Health Records Research to develop methodology in EMR-based research across Medical School, Schools of Engineering & Informatics, Business, Management & Economics, Mathematical & Physical Sciences and the School of Applied Social Sciences (University of Sussex).

There are controls in place to ensure secure access to the data:

1. Researcher access - the HTI and HTI-CPRD-GOLD databases contain pseudonymised data. All researchers accessing the data need to be a substantive employees of IMS Health Ltd or must have an honorary contract with IMS Health Ltd in place. All researchers accessing these data undertake training and sign additional confidentiality agreements with IMS Health Ltd mirroring the requirements set out by NHS Digital. Researchers are informed that any misuse of data will result in formal disciplinary procedures. IMS Health Ltd does not permit any access to pseudonymised patient record-level data from outside of the UK.

2. Strong internal governance process - researchers only access the HTI and HTI-CPRD-GOLD data for single-study research projects that have received approval from IMS’s Independent Scientific Ethical Advisory Committee (ISEAC) for HTI studies and the CPRD’s Independent Scientific Advisory Committee (ISAC) for HTI-CPRD-GOLD studies. This ensures that access is only granted to answer medical research questions and that only data required to answer the study question is extracted from the database. All additional studies, study modifications, or study extensions require further approval.

3. Advanced study planning - further safeguards include the standard IMS Health Ltd procedures for conducting observational research which require pre-registration of study objectives and procedures in the form of a detailed protocol and statistical analysis plan (SAP). IMS Health Ltd maintains an access control register and all usage of the database against an ISEAC or ISAC approved protocol is logged and auditable.

Where appropriate, researchers accessing HTI or HTI-CPRD-GOLD data will be required to publish their findings or allow an anonymised (company and product blinded) version of the study to be made available on the publically available IMS global bibliography. IMS Health Ltd will share these findings with participating trusts and healthcare stakeholders at an annual research day. IMS Health Ltd will not make the outputs of safety studies publically available to avoid generating undue public concern before guidance is issued by regulatory agencies.

Data minimisation

IMS Health Ltd understand the importance of data minimisation and have taken steps to reduce the number of HES records requested.

IMS is requesting access to the following data:
• HES records, either in an HTI trust or a non-HTI Trust, that can be linked to a pharmacy record
• All other HES records from HTI Trusts that can't be linked to a pharmacy record. These records will only be used for data validation purposes to compare the % of linkage across different trusts and therapy areas. These records will only be accessible to researchers for data validation purposes and will be used to indicate whether the data is high quality enough for medical research studies.

Justification for historical data -

IMS Health Ltd uses historical HES data (from 2005 where available) in HTI studies in order to identify diagnosis prior to patients receiving a drug, identify whether the patient has co-morbid conditions and identify the date when a patient first had a secondary care visit for a particular diagnosis (index date). HTI has been used to conduct studies on drug treatment for chronic conditions including psoriasis, rheumatoid arthritis, multiple sclerosis and ulcerative colitis. Patients with chronic diseases have these conditions for life so it is important to have the maximum number of years of back data in order to conduct studies rigorously.

When researchers conduct studies using HTI they need to establish the index date of a patient at the beginning of treatment or diagnosis in order to determine progress and treatment efficacy over a follow up period. They also need to understand if patients have a history of serious comorbid conditions e.g. if a patient was hospitalised 10 years ago for a stroke then this needs to be taken into account. By answering these questions researchers are able to build cohorts for studies with the right type of characteristics. If historical HES data was not provided then researchers could miss important events which would then not be adjusted for in study results.

In addition the historical data will be used to detect rare, delayed adverse events. Researchers from regulatory agencies need access to historical HES data in the HTI database in order to monitor drug safety, particularly of rare and delayed adverse events which may take many years to develop.

Further scientific need for the historical data -

• Historic data is required to support Advanced Statistical Analysis projects and safety studies, as historical data allows robust analysis of trends over time.

• Historical data on patient contact with secondary care is important because the lead up to diagnosis of many conditions, particularly rare diseases, can be complex and lengthy. Evidence highlighted in the UK Strategy for Rare Diseases suggests that four in ten patients with rare diseases have “found it difficult to get the correct diagnosis” and that “25% of patients said that there was a gap of between 5 and 30 years between getting their first symptoms and a diagnosis”

• Historical data is needed for patients with chronic conditions to understand disease progression and can be used to investigate how the usage of different treatments impacts the typical time of disease progression

• Historical data is needed to understand previous and co-morbid conditions in order to adjust for these in the research study outcome. For example a trust could be incorrectly identified as having poor outcomes or performance when they are in fact treating sicker or higher risk patients e.g. patients with previous cardiovascular and stroke events. This information in also needed to ensure the research outcome is correctly interpreted so that the medical professionals are able to provide the most appropriate care for patients e.g. a medication may be considered to have higher risk profile in a certain patient population

• Historical data provides extended longitudinal coverage to allow researchers to look at delayed adverse events or outcomes which have a long latency period from the time of exposure to manifestation.

Yielded Benefits:

HTI studies to date: Three papers have been published in scientific journals and one poster has been published Antibiotic use: There has been a focus on stewardship programmes to curb inappropriate antibiotic prescribing and reduce antimicrobial resistance. In-hospital, patient-level prescribing linked to indication is needed to support surveillance, evaluation of stewardship initiatives, as well as other antibiotic research. Iqvia evaluated whether a novel dataset linking hospital pharmacy records to Hospital Episode Statistics (HES) data can be used for antibiotic research: Linking individual-level data on diagnoses and dispensing for research on antibiotic use: Evaluation of a novel data source from English secondary care Patrick Rockenschaub, David Ansell, Laura Shallcross Pharmacoepidemiology and Drug Safety 2018, 27 (2): 206-212 Iron Deficiency Iron deficiency anaemia (IDA) is the most common micronutrient deficiency worldwide, and is a major cause of referral to secondary care. Orally administered iron may inadequately control symptoms of IDA, particularly in those with underlying gastrointestinal (GI) conditions that impair iron absorption or cause bleeding. Inadequate treatment of IDA may increase the risk of hospital treatment and associated costs. In this study, the potential relationship of oral and intravenously administered iron with hospitalization is examined in a retrospective cohort. Treating iron deficiency in patients with gastrointestinal disease: Risk of re-attendance in secondary care: Tomkins, Susannah & Chapman, Callum & Myland, Melissa & Tham, Rachel & de Nobrega, Rachael & Jackson, Brinley & Keshav, Satish. (2017).. PLOS ONE. 12. e0189952. 10.1371/journal.pone.0189952. Dose Escalation in Colitis The objectives of this study was to estimate the proportion of UC patients administered adalimumab who dose escalate (doubling dose or increasing dose frequency [100%]), the time to dose escalation and any subsequent de-escalation, and the proportion who de-escalate. Dose Escalation And Healthcare Resource Use Among Ulcerative Colitis Patients Treated With Adalimumab In England: C. M. Black1, E. McCann2, E. Yu3, M. Nixon3, S. Kachroo1 1Merck, Whitehouse Station, United States, 2Merck, Hoddesdon, 3IMS Health, London, United Kingdom PLOS ONE | DOI:10.1371 2016

Expected Benefits:

Trusts: HTI will help hospital trusts understand the use of and outcomes associated with hospital prescribed medicines. This will enable Trusts to undertake evidence based decisions on access to high cost drugs, support treatment policies, compare provision and outcomes with other Trusts and monitor patient outcomes. IMS Health Ltd will conduct studies on behalf of trusts and results from these studies will be shared as part of an annual research report and also at an annual research day which will be attended by participating trusts and other healthcare stakeholders.

ALBs: HTI will be used to monitor adherence to clinical guidance and decisions regarding uptake of innovative therapies, in order to reduce variation in the delivery of healthcare. ALBs concerned more directly with the provision of healthcare such as NHS England and Public Health England will be able to measure equity of access to treatments in the NHS using HTI and, therefore, reduce health inequality.

Academia: HTI will be used to undertake research into disease management and outcomes to improve patient care, health economic studies, comparative effectiveness studies and drug utilisation studies. These studies support the long term sustainability of the NHS by evaluating cost effectiveness. This will become increasingly important as drugs become more expensive placing a higher financial burden on the health system.

Pharma: HTI will be used to answer focused, scientific research questions with clear health and social care benefits. Such studies will include epidemiology, natural history of disease and health outcomes research. These studies will add to the body of research evidence used for drug development and the results of these studies can support optimal allocation of finite NHS resources. All studies will need to be published or a blinded version of results will be made available on IMS Global Bibliograhy meaning findings and information from the studies will be available to the healthcare and medical research community.

In addition, pharmaceutical companies will conduct pharmacovigilance studies using HTI data. HTI data enables pharmaceutical companies to fulfil their regulatory requirements and keeps patients safe through identification and evaluations of adverse drug reaction.

1. Patient safety

Adverse drug reactions (ADRs) create a burden for the NHS (Ref 3) and are an important cause of mortality amongst hospitalised patients. HTI is currently the only population based database available for monitoring the safety of medicines used in the secondary care setting. There is an unmet need for this type of data as a study found that 50 % of newly licensed drugs are now solely prescribed in secondary care and therefore could not be monitored in widely used primary care databases (Ref 4).

IMS Health Ltd has conducted two important safety studies in the HTI database and intends to carry out more. The inclusion of a Trust Identifier in the database would enable the monitoring of ADRs at a Trust level.

In one study, IMS Health Ltd were engaged by a drug’s marketing authorisation holder to conduct a three year post-authorisation safety study (PASS), a requirement for the marketing approval for this drug set out by the European Medicines Agency (EMA) on patients in England. The EMA requested the use of the drug be monitored using HTI. The indication associated with use of the drug was extracted from the database and the site of administration was determined as off label usage of this formulation in contra- indicated sites has been shown to cause significant harm.

IMS Health Ltd has monitored off-label usage of this drug for 2.5 years, and has reported these results to the EMA, however as IMS Health Ltd only hold data up to March 2015 updated data is needed to check whether there is off-label drug usage in 2015/2016. If there is found to be off-label usage then the drug manufacturer will need to update their risk management plan to prevent this from happening in the future and to prevent patient harm. This should be of significant interest to the NHS due to implication for preventable harm and the potential for litigation.

A second study looked at the use of a marketed medicine which is known to have harmful effects in specific sub-populations of cancer patients. The regulator requested monitoring of exposure to the drug within these populations. This protects patients from receiving drugs which are contraindicated for them. The results of the study were submitted to the European Medicines Agency in support of a risk management plan (RMP) which helped to characterise the overall benefit risk profile the drug and ensures that it is used as safely as possible. Safety information which is included in the summary of product characteristics and on the drug’s package leaflet in based on the RMP so findings and guidance from this study are directly available to healthcare professionals and patients.

Using HTI for safety studies allows quick analysis as the data already exists. If this database was not available, the two safety studies described would have taken months rather than days if conducted by other methods and only measured a smaller number of patients. Using a larger sample of patients ensures that studies are robust and enables the detection of rare events.

2. IMS Health Ltd have performed a number of studies on healthcare resource utilisation within patients prescribed high cost drugs

Autoimmune conditions are complicated to manage and result in debilitating conditions for patients. Recent immuno-modulating therapies such as anti TNF based drugs (all prescribed in secondary care) have been shown to provide considerable benefit to patients with a reduction in morbidity and improved quality of life. However, these drugs are expensive and the control and use within guidelines is important for NHS trusts with implications for those involved with commissioning fully funded pathways. IMS Health Ltd has conducted a series of epidemiological studies in this therapy area to determine the dosing patterns, the indications for which the drugs are prescribed and the patient populations within which they are used.

It has been shown that high cost drugs (anti-TNFs and biologics) are used more frequently in routine clinical practice than anticipated. This creates an additional cost burden to the NHS then planned for. Using HTI data IMS Health Ltd showed that inflammatory bowel disease patients treated with high cost drugs showed differences in the rates of hospitalisation and surgical interventions between different agents (Ref 1). This information can be used to identify patient groups that would benefit most from these high-cost drugs and allow resources to be allocated accordingly. This piece of work has been disseminated at one of the leading European conferences, the United European Gastroenterology Week (UEG). Attendees at the UEG include leading specialists across gastroenterology making it a key opportunity for knowledge sharing across the gastroenterology community. It has also been published via an open access journal PLOS One this means that NHS staff are able to access this for free via a standard literature search for evidence meaning there is no paywall standing in the way of health professionals accessing this material.

3. Probability of hospitalisation

Intravenous iron therapy is not considered as first line treatment of iron deficiency anaemia in the majority of patients. IMS Health Ltd conducted an epidemiological study in collaboration with a pharmaceutical company to show that the 30-day readmission rates among those patients treated with IV were significantly lower than those treated with oral therapies (Ref 2). Readmission to hospital is distressing for patients but is also an inefficient use of NHS resources. 30 day readmission rate is a key quality metric that is used to evaluate NHS Trusts. This study was presented at the Digestive Disorders Federation's annual scientific meeting and its inclusion was decided by a panel of gastrointestinal experts.


References:

Ref 1 Dose Escalation and Healthcare Resource Use among Ulcerative Colitis Patients Treated with Adalimumab in English Hospitals: An Analysis of Real-World Data Christopher M. Black1, Eric Yu2, Eilish McCann3, Sumesh Kachroo1* 1 Merck & Co, Inc., Kenilworth, United States of America, 2 IMS Health Ltd, London, United Kingdom, 3 Merck Sharp & Dohme Ltd, Hoddesdon, United Kingdom http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149692

Ref 2 ASSOCIATION OF ORAL AND INTRAVENOUS IRON WITH THE PROBABILITY OF HOSPITALIZATION IN ENGLAND S. Keshav 1, C. Chapman 2, S. Tomkins 3,*, L. Mills 4, B. Jackson 41 Translational Gastroenterology Unit, John Radcliffe Hospital and University of Oxford, Oxford, 2 West Middlesex University Hospital, Isleworth, 3Real World Evidence, IMS Health, London, 4Vifor Pharma, Bagshot, United Kingdom http://gut.bmj.com/content/64/Suppl_1/A18.2

Ref 3 Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients Munir Pirmohamed, professor of clinical pharmacology,1 Sally James, research pharmacist,3 Shaun Meakin, research nurse,2 Chris Green, senior pharmacist,2 Andrew K Scott, consultant in care of the elderly,3 Thomas J Walley, professor of clinical pharmacology,1 Keith Farrar, chief pharmacist,3 B Kevin Park, professor of pharmacology,1 and Alasdair M Breckenridge, professor of clinical pharmacology https://www.ncbi.nlm.nih.gov/pmc/articles/PMC443443/

Ref 4 Cederholm S, Hill G, Asiimwe A, Bate A, Bhayat F, Persson Brobert G, Bergvall T, Ansell D, Star K, Norén GN. Structured assessment for prospective identification of safety signals in electronic medical records: evaluation in the health improvement network.Drug Saf. 2015 Jan;38(1):87-100. doi: 10.1007/s40264-014-0251-y.PMID:25539877) http://www.who-umc.org/graphics/29625.pdf

Outputs:

All HTI and HTI-CPRD-GOLD studies result in a scientific report structured along the lines of a scientific paper (e.g. Summary, Background, Methods, Results and Discussion). Interim tables of results (aggregated data with small number suppression in line with the HES analysis guide) may be circulated as interim results for discussion and appended to the study report.

Further outputs include research publications in peer-reviewed journals and presentations at scientific conferences in addition to research included in Health Technology Assessments and Regulatory evidence. Researchers accessing HTI and HTI-CPRD-GOLD data will be required to publish their findings or allow an anonymised (company and product blinded) version of the study to be made available on the publically available IMS Global bibliography. IMS Health Ltd will present these results with participating trusts and healthcare stakeholders at an annual research day.

The following outputs are expected for each of the research groups IMS Health Ltd engages with:

1. Participating NHS Trusts. IMS Health Ltd’s research team will work with NHS Trusts to assist in the production of information that will impact on improving patient care. Initially IMS Health Ltd will answer 2-3 research questions on drug usage within secondary care that have been suggested by participating trusts. The report delivered to each trust will contain aggregated, small number suppressed data across all trusts and will also contain trust-specific outputs which will only be shared with that trust. These findings will be sent to Chief Pharmacists and Research departments at participating hospital Trusts.

IMS Health Ltd will also hold an annual research day for hospital trusts and other healthcare stakeholders to make people aware of the types of research that the HTI database has been used for and present the findings from the hospital trust specific studies. Over time IMS Health Ltd expect hospital trusts and arm’s length bodies to feed into the type of questions they want to answer so IMS Health Ltd can produce content that is relevant and directly benefits healthcare.

The inclusion of a pseudo-Trust ID within the data will allow for the provision of Trust-level analysis requested by Trusts. In addition it will improve the quality of the data as researchers will be able to exclude Trusts who have provided incomplete data.

2. Regulatory Authorities and Arm’s Length Bodies (ALBs) involved in Health and Social Care. Access to pseudonymised, non-sensitive record level database by regulatory authorities for drug safety studies, signal detection and evaluation, of adverse drug reactions of hospital prescribed therapies. ALBs such as NICE, NHS Digital and NHS England will be able to access the pseudonymised, non-sensitive record level database for health technology assessments and to inform policy decisions. This output is dependent on allowing external researchers access to the database under the same secure conditions as IMS Health Ltd employees and will be subjected to formal contracting processes compliant with terms set out by NHS Digital.

3. Medical researchers from academia. Access to pseudonymised, non-sensitive record level database or delivery of aggregated tables for generation of research publications in peer-reviewed journals and presentations in scientific conferences. Information to be included in health technology assessments and evidence for regulators.

4. Pharmaceutical companies. Provision of aggregated, small number suppressed tables to answer research questions in areas of:
Advanced statistical analysis
• epidemiology
• natural history of disease
• health economics and outcomes research
• drug exposures

Drug safety monitoring
• pharmacovigilance

The outputs from these studies could be published in peer-reviewed journals and presented at scientific conferences, included in Health Technology Assessments or delivered to regulators. Pharmaceutical companies will be required to publish their findings or allow an anonymised (company and product blinded) version of the study to be made available on the publically available IMS Global bibliography. IMS Health Ltd will present these results with participating trusts and healthcare stakeholders at an annual research day. Pharmaceutical companies will not have direct access to the pseudonymised record level data.

Processing:

Data flow

Each month, participating trusts provide to NHS Digital three files. The 'TRUSTED' file contains patient identifiable hospital pharmacy issues data, which is used for the subsequent linkage to HES. The 'ISSUES' file is a non-identifiable version of the TRUSTED file which NHS Digital provides onward to IMS Health Ltd, and also checks against the TRUSTED file to ensure the payload data in the two files are consistent. A third data definition file ‘DEFS’ is also provided to NHS Digital which is forwarded to IMS Health Ltd. The definitions file is not identifiable. The ISSUES and DEFS files are received by IMS Health Ltd for their hospital pharmacy audit work, which is outside the scope of this agreement.

Hospital prescribing and HES data are linked by NHS Digital and data are pseudonymised before being passed on to IMS Health Ltd on a quarterly basis. Once received, these data are downloaded via SFTP to a secure server within an ISO27001 accredited environment.

Security measures include:
• Access authentication
• Monitoring of access
• Round the clock security staff presence
• Robust firewalls and other access restrictions

Remote Access to IMS ISO27001 compliant environment:
Researchers access the IS027001 environment remotely via a secure portal. Researchers are able to query the data within this environment and create patient cohorts for further study. Data cannot be copied from the secure environment and usage of the security environment is auditable. Researchers can export aggregated, small number suppressed data from the secure environment and a record of all exports is kept for monitoring and audit purposes.

External Researcher access:
Access to the data is only permitted for substantive IMS Health Ltd employees and researchers working under honorary contract to IMS Health Ltd. Honorary contractors will be subject to the same access controls as substantive IMS Health Ltd employees. They will be provided with a username and password and access the data through the secure portal. If an honorary contractor is accessing data from any location apart from the IMS Health Ltd office they will be required to provide details of the processing location in their honorary contract. IMS Health Ltd will validate that the data processing location listed has appropriate security measures in place such as ISO27001 or IG Toolkit before access to the data is granted. Access to the data is not permitted from outside the UK. IMS Health Ltd reserves the right to undertake an audit of the honorary contractor at any time to ensure that appropriate security measures are in place and that all terms of the agreement are being abided by (such as agreed processing location).


Pulmonary Hypertension (PH) population characterisation and epidemiological analysis — DARS-NIC-296034-T4Y4K

Opt outs honoured: Yes - patient objections upheld, Anonymised - ICO Code Compliant, Yes (Section 251 NHS Act 2006)

Legal basis: National Health Service Act 2006 - s251 - 'Control of patient information'. , Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii); National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 - s261 - 'Other dissemination of information'; National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: Yes (Commercial)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2020-05-19 — 2021-05-18 2020.07 — 2020.10.

Access method: One-Off

Data-controller type: IQVIA LTD, IQVIA TECHNOLOGY SERVICES LTD., JANSSEN-CILAG LIMITED, SHEFFIELD TEACHING HOSPITALS NHS FOUNDATION TRUST

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Admitted Patient Care
  2. Hospital Episode Statistics Outpatients
  3. Hospital Episode Statistics Accident and Emergency

Objectives:

Pulmonary Arterial Hypertension (PAH) (a type of Pulmonary Hypertension (PH)), is a progressive and rare disease caused by narrowing or tightening (constriction) of the pulmonary arteries. PAH primarily affects the small pulmonary arterioles through vascular proliferation and re-modelling, while resulting in a progressive rise in lung blood pressure and heart failure. PAH represents Group 1 (of 5) within the Pulmonary Hypertension World Health Organisation clinical classification system (Dana Point, 2008). In Europe, PAH prevalence and incidence are in the range of 15–60 subjects per million population and 5–10 cases per million per year, respectively. Furthermore, there are several types of PAH including Idiopathic PAH (iPAH) and Associated PAH related to a range of disease processes, including cirrhosis, connective tissue disease, congenital heart disease, HIV infection and sickle-cell disease

In addition, there are other types of PH, including Chronic Thromboembolic Pulmonary Hypertension (CTEPH) and Sarcoidosis-Associated Pulmonary Hypertension (SAPH). CTEPH is caused by complications of acute Pulmonary embolism (PE), leading to an obstruction of the large pulmonary arteries and distal small-vessel arteriopathy, with an estimated incidence of 3-5 cases per 100,000 patients. Sarcoidosis is a multisystem immune-mediated inflammatory disorder categorised by the presence of granulomas in tissues and is highly associated with respiratory tissue. Sarcoid patients may develop SAPH, which can lead to significant complications.

Due to the complexity, late diagnosis and absence of real world studies across all subtypes of PH (PAH, CTEPH and SAPH), there is currently a lack of understanding regarding the clinical pathway leading to diagnosis. In addition, there is also the need to further explore and study post-diagnosis pathways and patient related outcomes associated with these diseases in routine clinical practice.

As a result, IQVIA Ltd and IQVIA Technology Services Ltd, are carrying out a retrospective real world data analysis of UK PAH, CTEPH and SAPH patients for Janssen-Cilage Limited. The study will focus on describing and comparing PAH, CTEPH and SAPH populations (and define sub-populations) in terms of characteristics of patients, clinical pathways pre-diagnosis and diagnostic procedures, treatment patterns post diagnosis, clinical outcomes and Healthcare Resource Utilisation (HCRU). This presents a great opportunity for both Janssen and the broader scientific/research community to improve the body of knowledge around these three sub-types and further enhance the possibility of developing better clinical pathways, improving treatment patterns and informing clinical guidelines to improve patient’s health and Quality of Life (QoL).

The Pulmonary Hypertension Association UK (PHA UK) is the UKs largest and foremost patient advocacy group for those suffering from this life threatening and shortening condition; PHA UK has some 4,340 members representing the views of a large part of the UK patient population.

The Pulmonary Hypertension Association UK, agree that the study “Pulmonary Hypertension
(PH) population characterisation and epidemiological analysis” will benefit the broader scientific
community by improving the body of knowledge around PH and its subtypes, while enhancing the possibility of developing better clinical pathways, advancing treatment patterns and informingclinical guidelines to improve patient’s health and Quality of Life (QoL). This research study represents for the members a crucial part of PHA UK's collective endeavours to find new and more efficacious therapies for those with PH.

There is an additional purpose of this study for the patients involved, namely that the study could potentially support the development of future novel drugs in clinical trials. It was determined that the combined analysis of HES data and the enhanced diagnostic clinical data jointly held by STHFT and the University of Sheffield (UoS) could be very beneficial to this study, hence the joint involvement of each in the development of this study.

IQVIA Ltd and IQVIA Technology Services Ltd. will be processing the data in line with their related to the research objectives, as part of their legitimate interests. IQVIA Ltd and IQVIA Technology Services Ltd are affiliated healthcare technology companies with interest in medical research which typically establishes relationships between pharma companies and healthcare providers to further this interest. IQVIA Ltd and IQVIA Technology Services Ltd considers this study will provide the healthcare community and academia with a better understanding of the diagnosis and treatment of PH patients in England, providing opportunities to identify areas to improve services, improve the patient journey, as well as opportunities to provide earlier treatment and to improve quality of life for patients. It is anticipated that patients and other healthcare stakeholders will have increased access to information about the disease given the planned production of publications of study findings. The evidence produced upon completion of the study could potentially help inform research direction for novel treatment in this severely under-served disease.

Covered under the GDPR Article 6(1)(f) - This work is necessary for the purposes of the legitimate interests pursued by the controller except where such interests are overridden by the interests or fundamental rights and freedoms of the data subject which require protection of personal data, in particular where the data subject is a child. The data requested is to help achieve the following: pre- and post-diagnosis pathway analysis, health economics/burden of illness studies, treatment pathway analysis, diagnostic pathway assessment and improvement of clinical services as well as potential support for drug development.

Janssen-Cilag Limited will be processing the data in line with their research objectives as part of their legitimate interests. Janssen-Cilag Limited is a pharmaceutical company with interest in the pulmonary hypertension therapy area. It is funding this study to further the body of knowledge of PAH, CTEPH and SAPH populations in terms of patient characteristics, clinical pathways pre-diagnosis, treatment patterns post diagnosis, clinical outcomes and HCRU. Janssen-Cilag Ltd. is particularly interested in understanding more about patient journeys through the secondary care system which can help identify ways of improving diagnosis and treatment, as well as potentially providing evidence to support applications for novel therapies in this highly under-served disease area. The reported results of this analysis may potentially assist in allowing patients to get access to new treatment options and provide health economic information to help design a more efficient care pathway for PH patients

Reseacrh veung undertaken by is covered under the GDPR Article 6(1)(f) - This work is necessary for the purposes of the legitimate interests pursued by the controller except where such interests are overridden by the interests or fundamental rights and freedoms of the data subject which require protection of personal data, in particular where the data subject is a child. The data requested is to help achieve the following: pre- and post-diagnosis pathway analysis, health economics/burden of illness studies, treatment pathway analysis, diagnostic pathway assessment and improvement of clinical services as well as potential support for drug development.

Sheffield Teaching Hospitals NHS Foundation Trust will be processing the data in line with its goals to carry out the task in the public interest. STHFT one of England’s leading PH diagnostic and treatment centres considers that this analysis may provide it with information to better support their patients through the full care pathway. They consider that their patients will benefit from the research by furthering their understanding of patient journeys outside of the Sheffield Pulmonary Vascular Disease Unit (SPVDU) – leading to a more coordinated approach to care - in addition to understanding the outcomes of its unique diagnosis process and allowing it to share its learning with other centres.

Research being undertaken by Sheffield Teaching Hospitals NHS Foundation Trust is covered under the GDPR Article 6(1)(e) - Processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller. The data requested is to help achieve the following: pre- and post-diagnosis pathway analysis, health economics/burden of illness studies, treatment pathway analysis, diagnostic pathway assessment and improvement of clinical services as well as potential support for drug development.

The use of this pseudonymised data will allow the data controllers and data processors to provide the research outputs to Sheffield Teaching Hospitals assist in enabling them to deliver better healthcare. This includes analysis and interpretation which provide understanding of care pathways, understanding use of medicines, and supporting healthcare commissioners and providers in achieving data quality, all these examples are with the aim of improving overall healthcare. The data processors also utilise this pseudonymised data to provide services to Janssen-Cilag Limited to improve healthcare and assist the efficiency of the National Health Service in the interest of public health. The service supports the development of innovative solutions and service improvement and helps to track outcomes and provide real-world evidence as required by the NHS, NICE, and NHS England with the aim of improving patient care and support enhanced access to improved services and innovative solutions. Individuals have got the right to opt out of the use of the HES data. All data removed from the storage location is small number suppressed as per HES Analytics guide.

Research overview

The goal of the research is to:
• Generate real world evidence on the clinical and treatment management of patients diagnosed with PAH, CTEPH and SAPH
• Analyse the characteristics of diagnosed patients at the time of confirmed diagnosis, and the clinical pathways leading up to diagnosis, diagnostic procedures and the post-diagnostic treatment patterns
• Investigate various clinical outcomes associated with PH and its subtypes, and comparative outcomes analysis of PH treatments
• To explore the healthcare resource use (HCRU) and the costs associated with the diagnosis of PAH, CTEPH and SAPH

To achieve the above objectives, IQVIA Ltd and IQVIA Technology Services Ltd propose to build a joint dataset that has the required patient cohorts and clinical information. The database will comprise of non-identified patient data derived from the STHFT deep clinical databases which collect data on all patients attending the Sheffield Pulmonary Vascular Disease Unit (SPVDU) and national level hospital interactions from HES data.

Parties involved in the research:

Each party in the collaboration will have a different role during the research:
1. STHFT will take responsibility for ethics approval for the study, provide expert clinical insight on the research findings, support on datasets de-identification and linkage of data in addition to supporting the publication of research findings
2. IQVIA Ltd will support STHFT ethical approvals activities, co-develop the study protocol and SAP, conduct the transformation and data processing of non-identified data into analysable format and perform the analysis to meet the research objectives
3. Janssen- Clilag Ltd (Janssen) will be funding the research and willvsupport the development of the study protocol and SAP and provide expert clinical insight on the findings.
4. IQVIA Ltd and IQVIA Technology Services Ltd will each have access to non-identifiable record level data and create aggregate outputs which will be shared with both Janssen and STHFT. Janssen will not be permitted to access record level HES data and shall only ever have access to aggregated data with small number suppressed in line with the HES Analysis Guide.
5. STHFT and Janssen will provide clinical interpretation of the results.

Janssen is funding the research to further the body of knowledge of PAH, CTEPH and SAPH populations in terms of patient characteristics, clinical pathways pre-diagnosis, treatment patterns post diagnosis, clinical outcomes and HCRU. STHFT as one of England’s leading PH diagnostic and treatment centres benefits from the research by furthering their understanding of patient journeys outside of the Sheffield Pulmonary Vascular Disease Unit (SPDVU), in addition to the verification that their unique diagnostic process is beneficial to patients, allowing them to share their learning with other centres.

To ensure findings are published fairly and not suppressed there will be a clinical interpretation committee in place. This shall be comprised of two representatives of each ofSTHFT the UoS and Janssen and with IQVIA Ltd and IQVIA Technology Services Ltd having one representative who will chair the group.
The committee will perform the following functions:
1) Provide clinical interpretation of the results to support refinements of the analysis within the bounds of the protocol
2) Agree the dissemination / publication routes for research findings (e.g. conference posters vs peer review papers etc.) based on the nature and strength of findings. (Please see output section for further information)

No organisation on the clinical interpretation group or any of the data controllers will have the ability to suppress any of the findings or outputs of the analysis. The clinical interpretation group members do not have any access to record level data.
The studies chief investigator from STHFT will oversee the research and offer clinical insight on the findings.
The dissemination of findings has been pre-agreed with the committee and outlined in the outputs section.

It is important when linking HES data with the STHFT dataset to utilise the confirmed and sub-typed (PAH, CTEPH and SAPH) patient diagnoses present in the STHFT dataset, where the patient PH classification has been confirmed by world leading clinical experts. This will allow IQVIA Ltd and IQVIA Technology Services Ltd to identify patients with confirmed PAH, CTEPH and SAPH diagnoses within the HES data for investigation and analysis with high certainty. Current ICD-10 coding available in HES (the International classification system for coding of disease types, maintained by the World Health Organisation) does not have a specific code for the different PH sub-types (PAH, CTEPH and SAPH), instead multiple different pulmonary diseases coded under the same ICD-10 4-level character code. In addition, coding is not consistently applied across centres, meaning that PH patients in HES are coded across many different ICD-10 codes and therefore confirmation of disease and subtype in HES alone is not possible with complete certainty.

Access to STHFT data will be critical as it will provide great insight on all the patients who have attended SPDVU since 2012, to gain a better understanding of both the diagnostic pathway and process at SPDVU. Patients diagnosed with PAH, CTEPH or SAPH will be included. Furthermore, STHFT data will also provide information on in-depth patient clinical characteristics that will allow IQVIA Ltd and IQVIA Technology Services Ltd to characterise these patients at different points of the patient journey and develop matched cohorts for conducting comparative outcomes analysis across different PH treatments, and further investigate the variation in clinical outcomes of patients that have other comorbidities. Data from Hospital Episode Statistics Accident and Emergency, Hospital Episode Statistics Outpatients and Hospital Episode Statistics Admitted Patient Care is being requested in order to understand the complete patient pathway. Often, PAH patients may be admitted to A&E and may be taken as inpatients in relation to their PAH diagnosis, therefore this data will be crucial to understand the complete patient pathway. All 20 of the diagnosis codes are being requested in order to understand the comorbidities associated with PAH. The OPCS codes are also required in order to understand the procedures that a patient may have undergone as a results of their PAH diagnosis. Other data requests such as age, gender and ethnicity are needed in order to characterise the patients. This data will allow the aims discussed in previous sections of this application to be achieved.

The study design is a non-interventional retrospective database analysis of data collected on patients with PH (including its subtypes; PAH, CTEPH and SAPH) based on data from the Pulmonary Vascular Disease Unit at Sheffield Teaching Hospitals NHS Foundation Trust (STHFT) and HES data from NHS Digital.
To facilitate this project, IQVIA Ltd and IQVIA Technology Services Ltd are requesting the following patient cohort from NHS Digital; Cohort: Patients with PH who have been managed at the SPDVU since 2012, which will allow IQVIA Ltd and IQVIA Technology Services Ltd. to confirm the patient diagnosis (and subtype) in HES data and understand the diagnostic pathway before and at the SPDVU along with the post-diagnosis pathway. It is estimated that this cohort will be around 3000 patients. NHS numbers and dates of birth will be provided by STHFT to NHS Digital with the defined cohort of patients for linking to HES data.
Due to the complicated disease area and goals of the research the patient pathway analysis requires a long period of data for the following reasons:

• Having sufficient time to understand patient activity from onset of symptoms to diagnosis
IQVIA Ltd and IQVIA Technology Services Ltd. are requesting ~12-year historical extract of data for the study to cover the requested cohort (patients with PH who have been managed at the SPDVU since 2012).
The reason for the extended time period of the extract is that PH patient populations (especially at subtype level) are very small and the diagnosis pathway is a long multi-year processes and often complex. By analysing a longer period of data, it is more likely that IQVIA Ltd and IQVIA Technology Services Ltd will be able to create cohorts of patients with a higher number which will contribute to a more robust analysis and avoid some of the predicaments and biases of small patient number.

In Europe, PAH prevalence and incidence are in the range of 15–60 subjects per million population and 5–10 cases per million per year, respectively. One of the most common forms, idiopathic PAH, has an annual incidence of 1-2 cases per million. The other subtypes of PH are equally rare, with CTEPH having estimated full incidence of 3-5 cases per 100,000, and SAPH having an estimated incidence of 10-35 cases per 100,000. Because these datasets have relatively small numbers of patients with a rare disease, this request is for a ~10-year historical extract to ensure that a sufficient number of patients in order to ensure the results show statistical significance.

Expected Benefits:

There are likely benefits from this research for patients, the NHS, academia and life sciences companies. Overall, there are large gaps of knowledge within PH especially when looking at a subtype level (e.g. PAH, CTEPH, SAPH).
Understanding more about patient journeys through the secondary care system can help identify ways of improving diagnosis and treatment, as well as potentially providing evidence to support applications for novel therapies in this highly underserved disease area. This could potentially allow patients to get access to new treatment options and provide health economic information to help design a more efficient care pathway for PH patients. This more efficient care pathway could potentially lessen the burden on patients by reducing repeat visits during patient’s diagnostic pathways and supporting earlier diagnosis to improve patient treatment outcomes. In heritable forms of the disease benefits may well subsequently advantage patient’s family members.

Patient pathway analysis:
• The healthcare community & academia will gain a better understanding of the diagnosis and treatment of PH patients in England, providing opportunities to identify areas to improve services, improve the patient journey, provide earlier treatment and to improve quality of life for patients.
• Participants and non-participants will have increased access to information about their disease from the production of publications of study findings, which will be made available through the listed IQVIA Ltd website (noted on the posters at the STHFT), and potentially other channels e.g. PHA UK who support this research.

• The evidence produced will help inform research direction for novel treatment in this severely under-served disease.

• There is also the additional purpose of this study for the patients involved, as the study could potentially support the development of future novel drugs in clinical trials. The outcomes from this study could help provide vital insights into the PH (and its subtypes) population, and potential novel treatments that could be useful in the drug development process from its early stages through to post-authorisation.

Outputs:

IQVIA Ltd and IQVIA Technology Services Ltd will conduct initial analysis of the clinical pathway leading up to diagnosis and the post-diagnostic treatment patterns of patients, including a comparative outcomes analysis of PH treatments that will aim to investigate different outcomes of interest such as, but not limited to, overall survival of the treated groups, liver toxicity, oedema/fluid retention, as well as the reason and number of hospitalisations. In addition, the study will explore both the HCRU and the costs associated with the diagnosis.

An amendment of the Data Sharing Agreement will be required in the future to support any supplementary questions arising from publications, in addition to supporting further publications to contribute to the body of knowledge on PH.

In addition to the publication, additional dissemination plans are as follows:

• Published results will be shared with the Pulmonary Hypertension Association UKPHA UK (PAH UK) patient advocacy group at the beginning of the year 2021.

• Furthermore, the abstracts and links to publications will be hosted on IQVIA Ltd's online bibliography that is publicly available. Results will be presented at the European Society of Cardiology in August 2021, and in other journals such as the International Journal of Cardiology, the European Journal of Health Economics and potential others.

• Results will also be shared with other parties where appropriate. For example, sharing results with other NHS trusts that also manage PH patients or sharing with international centres that also diagnose and manage PH patients

Note: All outputs will contain only data that is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

To ensure the minimum amount of patient identifiable data will be used and handled by the fewest people outside of the direct care team the following process is currently proposed

1. STHFT will share with NHS Digital team, via a secure file transfer protocol, the NHS numbers and dates of birth of patients that have attended the SDPVU clinic since 2012, aligned to a generated study ID. The total number of this cohort is about 3000 patients

2. NHS Digital will link the identifiable patient cohort to the following data sets; Admitted Patient Care, Outpatient and Accident & Emergency data. NHS Digital will remove the NHS numbers and dates of birth and returning the non-identified extract (including study ID) to the STHFT informatics team.

3. Patient data received from STHFT will be linked to the HES data via the generated study ID and will be done in compliance with all trust policies on patient data handling. This data will only be accessible by the patient management team. NHS Digital will supply STHFT with the HES Data with a non-identifiable ID linked to the Study ID, which will allow STHFT to link the HES data to the diagnostic clinical data held at STHFT. The linked non-identified data will then be loaded to a second logical environment also located within STHFT.

4. This environment will be remotely accessed within the STHFT “De-militarized Zone” (DMZ), which is a special local network configuration designed to improve security by segregating computers on each side of a firewall. This is an additional layer of security between the trusted internal network and an untrusted external network i.e. the internet. The research will be undertaken by trained researchers from IQVIA Ltd and IQVIA Technology Services Ltd and specific members from the University of Sheffield who are under honorary contracts or hold Research Passports at the STHFT. Access is granted using strong two factor authentication based on USB keys which produce one time use passwords (more information can be found at https://www.yubico.com/).

The research conducted on this combined dataset will be for the agreed research questions and will be performed on non-identified patient information. The HES linked data will not leave STHFT. Only aggregated non-patient level outputs will be shared. The applicant expects to conduct the following analysis with the data, in order to generate real world evidence on the current clinical and treatment management of adult patients with a confirmed diagnosis of PAH, CTEPH or SAPH within the Sheffield EMR who have been treated with at least one PH-specific medical treatment.

1.Detailed analysis of the clinical characteristics (including diagnostic tests where available, functional class, presence of co-morbidities, previous pulmonary embolism for CTEPH patients) of diagnosed patients at the time of confirmed diagnosis between 2010 and 2019

2. Describe the pathways leading to diagnosis, including time to diagnosis from initial symptoms (PAH and CTEPH patients) or from sarcoidosis diagnosis (SAPH patients)

3.Describe the prescribed treatments after diagnosis and the treatment pathways, from the date of start of the first prescribed treatment, including use of co-medications (if available), persistence, discontinuation, switching and add-on therapy

4. Investigate various clinical outcomes associated with PAH, CTEPH and SAPH across different patient groups (e.g. categorised by comorbidity) as well as conducting comparative outcomes analysis of PH treatments. In addition, there is the aim to explore the healthcare resource use (HCRU) and the costs associated with the diagnosis.

These analyses are expected to be completed 9 to 12 months after HES data has been provided.

Researchers who will access the patient level HES data will be logged on an access control register, ensuring that it is possible to identify everyone with access to patient level information. Each researcher from IQVIA Ltd. and /or IQVIA Technology Services Ltd that will have access to record level data will sign an Intra-Company Agreement that will contain information on best practice and rules. These rules must be adhered to as stated in specific section of the contract, including the prevention of exporting any data from the Sheffield (STHFT) server that contravenes the HES small numbers protocol.

All individuals with access to the record level data must be substantive employees of IQVIA Ltd, and/or IQVIA Technology Services Ltd, and or/STH save researchers from other parts of the IQVIA group, who may be required from time to time to provide expertise in analysis of the data. These individuals will work under an Intra-Company Agreement to IQVIA Ltd. All individuals accessing the data under an Intra-Company vAgreement issued by IQVIA Ltd will be a substantive employee of the IQVIA company group. IQVIA Limited is not permitted under this agreement to enter into an Intra-Company Agreement with any individual, who is not substantively employed by an IQVIA group company.

During the analysis process of the pseudonymised and aggregated data, there will be regular sessions with Sheffield (STHFT) and Janssen clinical experts to provide clinical perspective and impact of the results generated.

Janssen-Cilag Limited will not carry out any processing of data under this agreement.

There will be not attempt to re-identify any individuals

No data linkage will be undertaken with NHS Digital data provided under this agreement except for the linkage already noted in the agreement.

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by "Personnel" (as defined within the Data Sharing Framework Contract i.e. employees, agents and contractors of the Data Recipient who may have access to that data).


Pulmonary Arterial Hypertension (PAH) population epidemiological analysis platform formation — DARS-NIC-58999-K6P8B

Opt outs honoured: N, Y, Anonymised - ICO Code Compliant (Mixture of confidential data flow(s) with consent and flow(s) with support under section 251 NHS Act 2006, Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s))

Legal basis: Section 251 approval is in place for the flow of identifiable data, Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii); Health and Social Care Act 2012 – s261(7)

Purposes: Yes (Supplier, Commercial)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2019-05-01 — 2020-04-30 2017.09 — 2017.11.

Access method: One-Off

Data-controller type: IQVIA SOLUTIONS UK LIMITED, IQVIA TECHNOLOGY SERVICES LTD., SHEFFIELD TEACHING HOSPITALS NHS FOUNDATION TRUST, IQVIA LTD, IQVIA SOLUTIONS UK LIMITED, IQVIA TECHNOLOGY SERVICES LTD., SHEFFIELD TEACHING HOSPITALS NHS FOUNDATION TRUST, IQVIA LTD, IQVIA TECHNOLOGY SERVICES LTD., SHEFFIELD TEACHING HOSPITALS NHS FOUNDATION TRUST

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Accident and Emergency
  2. Hospital Episode Statistics Outpatients
  3. Hospital Episode Statistics Admitted Patient Care

Objectives:

Background to the research:

Pulmonary Arterial Hypertension (PAH) is a disease primarily of small arteries in the lung which results in a progressive rise in lung blood pressure and heart failure. There are several types of PAH including Idiopathic PAH (iPAH) and Associated PAH related to a range of disease processes, including cirrhosis, connective tissue disease, congenital heart disease, HIV infection and sickle-cell disease.

The difficulties of early PAH diagnosis are well understood; signs and symptoms are subtle, there is no single approach for non-invasive, specialist diagnosis and misdiagnosis is common (Gibbs et al, 2015). Contemporary PAH literature discusses the challenges of PAH diagnosis and the urgent need for novel tools to detect patients earlier (Lau et al, 2014) (Forfia and Trow, 2013).

Late diagnosis of PAH is common and leads to significantly worse outcomes, however identifying patients with PAH earlier can allow targeted therapies to be started before the development of significant right heart failure and thus vastly improve patients overall survival and quality of life (Hoeper et al., 2013)

IMS Health Ltd have previously been commissioned by GlaxoSmithKline to carry out a retrospective analysis of UK iPAH patients in the English Hospital Episode Statistics (HES) data. The study focused on diagnosis pathways but also considered post-diagnosis treatment patterns of patients. This was commissioned to improve GSK’s understanding of PAH disease and patient care in England.

The findings further confirmed there is a large unmet need for early diagnosis, with results showing that there is a high level of activity pre-diagnosis with the average patient having 25 events in 3 years prior to diagnosis. Of those, 12 are within the final year pre-Right Heart Catheterisation (the confirmatory diagnostic test for PAH).

The IMS Health Ltd believes that there are opportunities to identify iPAH patients earlier based on the pattern of patients' interaction with secondary care facilities, symptoms shown and demographics, therefore identifying predictive signals/ markers which could lead to an earlier diagnosis of iPAH patients.

Secondly, the original HES analysis highlighted that when patients hit Sheffield Teaching Hospitals NHS Foundation Trust (STHFT) they appear to be diagnosed quicker than other centers, thus leading the applicant to hypothesis that the patient care pathway at the Sheffield Pulmonary Vascular Disease Unit (SPVDU) is optimised for quicker patient diagnosis and potentially leads to improved PAH patient outcomes. Therefore understanding the differences in patient pathways can lead to learning’s which could influence patient management at other centres.

These outputs, gave cause to believe that there is potentially high value in pursuing further analysis of this data when coupled with the enhanced diagnostic clinical data jointly held by STHFT and the University of Sheffield (UoS), leading to the IMS Health Ltd approaching STHFT/University of Sheffield for partnership.

Research overview:

The goal of the research is to:
• Validate the original analysis using STHFT’s data to confirm patient diagnosis of the selected cohort
• Understand the patients diagnostic pathway and outcomes of going through different routes to diagnosis
• Understand how SPVDU has streamlined their diagnostic process to allow quicker diagnosis of PAH patients when they enter the specialist center
• Utilising linked clinical and biological data (available in Sheffield’s data) to define novel disease phenotypes
• Develop a predictive algorithm which would be able to flag patients with a high probability of having idiopathic PAH (iPAH) from their data “fingerprint”. This will support finding undiagnosed patients through developing a predictive algorithm

In order to achieve the objectives, the IMS Health Ltd proposes to build a joint dataset in order to develop analysis to test these hypotheses. The database will be comprised of identifiable patient data derived from the STHFT “deep” clinical databases which collect data on all patients attending the SPVDU and national level hospital interactions from HES data.

Parties involved in the research:

Each party in the collaboration will have a different role during the research:
• STHFT will take responsibility for ethics approval for the study, provide expert clinical insight on the research findings, support on datasets de-identification, linkage and transformation in addition to supporting the publication of research findings

• IMS Health Ltd will support STHFT ethical approvals activities, conduct the transformation and data processing of de-identified data into analysable format and perform the analysis described in this agreement. IMS Health Ltd has significant experience with HES data, other retrospective databases, outcomes research expertise and advanced machine learning capabilities for predictive algorithm development.

• Both the University of Sheffield (UoS) and GlaxoSmithKline (GSK) will provide clinical interpretation of the results. UoS and GSK will are not permitted to access record level HES data. UoS and GSK only ever have access to aggregated data with small number suppressed in line with the HES Analysis Guide.

GSK is funding the research to further their understanding in a relatively understudied disease area, in addition to improving therapy efficacy in patients who are diagnosed and thus treated earlier.

STHFT as one of England’s leading PAH diagnostic and treatment centres benefits from the research by furthering their understanding of patient journeys outside of the Sheffield Pulmonary Vascular Disease Unit (SPDVU), in addition to the verification that their unique diagnostic process is beneficial to patients, allowing them to share their learnings with other centres.

The research focuses on the diagnostic pathway of patients, in a disease area where specialists and publications indicate there is a large degree of late diagnosis and this in turn impacts the efficacy of medicines and thus outcomes of the patients. However to ensure findings are published fairly and not suppressed there will be a clinical interpretation group in place. This is comprised of 2 representatives of each STHFT the UoS and GSK with IMS Health limited chairing the group.

The committee will perform the following functions:

1) Provide clinical interpretation of the results to support refinements of the analysis within the bounds of the protocol

2) Agree the dissemination / publication routes for research findings (e.g. conference posters vs peer review papers etc.) based on the nature and strength of findings. (Please see output section for further information)

No organisation on the clinical interpretation group will have the ability to suppress any of the findings or outputs of the analysis. The clinical interpretation group members do not have any access to record level data.

The studies chief investigator is Professor David Kiely from STHFT, who will oversee the research and offer clinical insight on the findings. The patient selection criteria has been based on patients who attended STHFT and those who share similar symptomology to PAH patients, this has been developed and chosen by IMS Health Ltd in conjunction with Professor David Kiely from STHFT. The dissemination of findings have been pre-agreed and outlined in the outputs section.

Data retention times has been agreed in CAG, REC and in the data sharing agreement that will be in place with the NHS Digital upon approval of the application. If IMS Health Ltd requires more time for the analysis they will request an extension on the agreement with NHS Digital.

Why link data:
It is important to link HES data with the STHFT dataset in order to utilise the confirmed and sub-typed PAH patient diagnoses present in the STHFT dataset, where the patient PAH classification has been confirmed by world leading clinical experts. This will allow the IMS Health Ltd to identify patients with confirmed PAH (and subtypes of PAH) within the HES data for investigation and analysis with high certainty. Current ICD-10 coding (the International classification system for coding of disease types, maintained by the World Health Organisation) does not have a specific code for PAH, with multiple different pulmonary diseases coded under the same ICD-10 code. In addition coding is not consistently applied across centres, meaning that PAH patients in HES are coded across many different ICD-10 codes and therefore confirmation of disease and subtype in HES alone is not possible with complete certainty.

In addition to providing clarity on the patients actual diagnosis, the STHFT data will provide insight on all the patients who have attended SPDVU, this is important as the applicant wishes to understand the diagnostic pathway and process at SPDVU, including those patients suspected of having a PAH diagnosis and subsequently being diagnosed with other conditions.

What data is requested:
The study design is a retrospective database analysis of data collect on patients who have attended the SPVDU at STHFT.

In order to facilitate this project the applicant is requesting 2 different cohorts of patients from NHS Digital:
1) Cohort A: Patients who have been managed at the SPDVU since 2000 – which will allow IMS Health Ltd to confirm the patient diagnosis (and subtype) in HES data, verify the original cohort selection in the previous HES analysis and understand the diagnostic pathway in SPDVU and why it is quicker than other centres (as shown by previous HES analysis)

2) Cohort B: A comparison group of patients - This group will be used in the development of the predictive algorithm, which will allow the applicant to use statistical techniques to compare the differences in care pathways of confirmed PAH patients (from cohort 1) and those patients who do not have confirmed PAH (from cohort 2). This requires IMS Health Ltd to look in detail at a group of patients similar to the confirmed cohort. IMS Health Ltd have done this by selecting patients with confounding or differential diagnosis to the PAH diagnosis, and there is various scientific literature which shows the association of these conditions with PAH/ pulmonary hypertension (PH).

The second cohort selection criteria are as follows:
• Historical patient data for selected cohort from 2000
• No patients under the age of 18
• Full (including historical) records for patients with any of the following ICD-10 codes within any diagnosis position: Dilated cardiomyopathy (I42.0), Hypothyroidism (E03.9), Mitral Stenosis (I05.0, I34.2 OR Q23.2), Mixed Connective-Tissue Disease (M35.1), Obstructive Sleep Apnoea (G47.3), Systemic Lupus Erythematosus (M32), Portal Hypertension (K76.6), Pulmonic Stenosis (I37.0), Scleroderma (L94.0, L94.1 OR M43), Ischaemic heart diseases (I20-I25), Heart failure (I50), Pulmonary heart disease and diseases of pulmonary circulation (I26 – I28), Asthma (J45), COPD (J47 OR J40 - J44) and Interstitial lung disease (J84.9).

If a patient has any of the above ICD-10 codes the applicant would like to have the full longitudinal patient record. Due to the complicated disease area and goals of the research the patient pathway analysis requires a long period of data for the following reasons:

• Understanding impact of STHFT changes to service:
Previous work at STHFT has resulted in the improvement of the diagnostic process of pulmonary conditions. Firstly by streamlining the diagnostic process within STFHT to allow the majority of patient to be diagnosed within 2 consultations, secondly by continuing medical education outreach to satellite centres through talks and guideline publications. The historical length of data will allow the measurement of the impact of these improvements and support messaging to other specialist centres to allow them to adopt the learnings from these efforts, thus potentially improving diagnostic efforts and thus patient outcomes.

Furthermore the requested length of HES data aligns with the length of data held by STHFT allowing the applicant to utilise the full breadth of clinical data that STHFT hold.

• Having sufficient time to understand patient activity from onset of symptoms to diagnosis:
IMS Health Ltd are requesting 2 ~15 year historical extract of data for the PAH project to cover both requested cohorts (patients who have attended SPDVU and cohort for development of the predictive algorithm).
The reason being that PAH patient populations (especially at subtype level) are very small and the diagnosis pathway is a long multi-year processes and often complex, the previous HES analysis showed that patients have a very high level of activity pre-diagnosis with >1/5th of patients experiencing hospitalisations, consultations or symptoms relating to IPAH disease >3 years before a positive diagnosis.

In addition the need to create a sophisticated algorithm that has the potential to perform well in the live clinical environment, a large sample of data is required. This is driven by the following reasons::

• Disease characteristics:
Cohort B was selected to try and ensure that the applicant adheres to data minimisation rules but also has enough data for meaningful analysis. The comparison group (cohort B) needs to be similar enough to the confirmed PAH cohort (cohort A), so the algorithm development process can start to identify the differences between patients who are often confused for PAH patients and those with a confirmed PAH diagnosis. PAH signs and symptoms are subtle and often confused with a range of different conditions. This means that the comparison group (cohort B) needs to be created from a sample of patients who share symptomology which is similar to PHA or occurs in conjunction with PAH disease. Minimising this data will lead to the development of a biased algorithm (For further information see the 180119_PAH Predictive algorithm overview- HES application Vf.dox).

• Refining the cohort based on clinical characteristics:
In order to select the most appropriate cohort of patients to act as a comparison group to confirmed PAH patients (cohort A), IMS Health Ltd require to undergo analysis of the patient data, this is a data driven approach coupled with insights from the clinical specialists. As noted previously PAH patients are often misdiagnosed as other conditions due to the rarity of the disease and huge range of clinical manifestations they can present with. The aim is to identify a cohort of patients which do not have a confirmed diagnosis but share very similar clinical features, have contaminant diagnosis, visit the same specialists etc. This allows development of the algorithm on a comparison group as close to the real cases physicians experience in clinical practice as possible and thus stretch the algorithm as much as possible.

For example, in previous work, IMS created an algorithm to identify a rare disease population (Idiopathic Pulmonary Fibrosis), which manifests as a lung condition commonly misdiagnosed as asthma or COPD. To focus the algorithm on the clinical challenge IMS developed the algorithm to distinguish between IPF patients (8,574 patients) and those with COPD/Asthma (7.5m patients). In order to find the most appropriate comparison group to our confirmed PAH patients (cohort A) it requires a deep dive into the data to align the patient cohorts

• Refining the cohort based on availability of appropriate length of historic data:
The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease.

A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013). Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis.

• Bringing the algorithm to clinical practice:
If the algorithm were to be implemented in real clinical practice setting the algorithm can only run on patients who fit inclusion and exclusion criteria used to pull HES data. Therefore the narrower the patient sample requested means that the more limited real world sample that can be assessed for risk of disease. For example if IMS only requested a sample of HES data made up of male patients who are over 40 years old. This would mean that IMS could not expect the model to produce robust predictions for any female patients or patients under the age of 40.

Due to these reasons the applicant requires HES data for a longer period than the usual 5 year period routinely offered by NHS Digital in order to capture sufficient patients for the analysis.

Yielded Benefits:

This Data Sharing Agreement permits the retention of the data for an interim period. Permission to retain the data for the interim period is a practical step to enable the study to comply with the necessary legal and ethical requirements. If, for any reason, it is not possible for the study to meet the necessary requirements, this Agreement will be terminated, and destruction of the data will be required. The following information provides background information on the purpose of the original study. No new data will be released under this version of the agreement, and this agreement allows the applicant to hold data that has already been disseminated. Several abstracts have been submitted and approved, with several more underway: • “Real world data from hospital episode statistics can be used to determine patients at risk of idiopathic pulmonary arterial hypertension” submitted to ERS (European Respiratory Society) 2018 • “Development Of A Predictive Algorithm Based On Healthcare Behaviour To Support Earlier Diagnosis Of Idiopathic Pulmonary Arterial Hypertension: Results Of A Feasibility Study In The UK” submitted to ATS (American Thoracic Society) 2018 Results of analysis will be presented at the European Respiratory Society this year. The initial predictive algorithm is indicating a large benefit to disease detection efforts and it is anticipated that the predictive algorithm, upon further refinement can be used to support screening programs. The algorithm is developed to flag the high risk patients who share characteristics in common with iPAH patients. iPAH disease prevalence is 5 in 1 million meaning that >20 million patients at random would have to be tested to find 100 patients with iPAH disease. Utilising the developed predictive algorithm on the HES data it was found that if the top 1000 patients flagged as “high risk” were analysed based on our algorithm patients would be detected with the disease.

Expected Benefits:

There are likely benefits from this research for patients, the NHS, academia and life sciences companies. Overall there are large gaps of knowledge within PAH, especially when looking at a subtype level. Understanding more about patient journeys through the secondary care system can help identify ways of improving diagnosis and treatment, as well as potentially providing evidence to support applications for novel therapies in this highly underserved disease area. This would potentially allow patients to get access to new treatment options, and provide health economic information to help design a more efficient care pathway for PAH patients. This more efficient care pathway could potentially lessen the burden on patients by reducing repeat visits during patient’s diagnostic pathways and supporting earlier diagnosis to improve patient treatment outcomes. In heritable forms of the disease benefits may well subsequently advantage patient’s family members. Specifically the outputs from each part of the research

Patient pathway analysis:

• The healthcare community & academia will gain a better understanding of the diagnosis and treatment of PAH patients in England, providing opportunities to identify areas to improve services, improve the patient journey, provide earlier treatment and to improve quality of life for patients.

• Furthermore participants and non-participants will have increased access to information about their disease from the production of publications of study findings, which will be made available through the listed IMS website (noted on the posters at the STHFT), and potentially other channels e.g. PHA UK who support this research

• The evidence produced will help inform research direction for novel treatment in this severely under-served disease.

Predictive algorithm outputs:

• An algorithm supporting earlier diagnosis would be of benefit to patients and the NHS if outcomes and patient experience (i.e. fewer hospital visits for diagnostics) can be improved.

• By supporting earlier diagnosis diagnostic costs per patient could be reduced which would benefit the NHS

• However total costs of treating this population could potentially rise. (This would need detailed health economic analysis to assess more fully – at this moment we are only speculating given the paucity of research of this nature in this condition).

• Finally a more rapidly diagnosed PAH population may benefit the multiple life science companies who are currently developing novel PAH therapies.


Ultimately the balance of these benefits would be dependent upon by the quality and interest of the descriptive findings, the robustness of the algorithm combined with any interventions put in place around it.

Outputs:

IMS Health Ltd expect to produce the following analyses:

• Analysis of the diagnostic and treatment pathways for different PAH subtypes– expected to be completed 3-6 months after HES data has been provided

• Investigate the predictive patient characteristics within the data environment to understand if IMS Health Ltd can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosed via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided


The target dissemination plan is as follows:

• The applicant will submit the findings of the research to a peer review journal e.g. Thorax - BMJ Journals.

• The applicant will submit and present on findings at the 2018 ATS conference, in addition to other important pulmonary conferences, in order to further the knowledge of other specialist physicians

• Published results will be shared with the PHA UK patient advocacy group

• Furthermore the abstracts and links to publications will be hosted on IMS Health Ltd’s online bibliography which is publically available

• Results will also be shared with other parties where appropriate e.g. Sharing results with other NHS trusts who also manage PAH patients or sharing with international centres which also diagnose and manage PAH patients


The current output of the algorithm generation is currently uncertain. However any implementation would need to be conducted by or with NHS bodies, because IMS are working with pseudonymous data and will not seek to re-identify patients at any stage. The nature of any implementation would need to be driven by the predictive sensitivity and specificity of the algorithm. In other words, the false positive and false negative detection rate. Implementing an algorithm with a high false positive rate would lead to many people tested with very few identified, conversely if the algorithm has a high false negative rate, it will likely miss many patients who should be tested for the disease. The health economics of the algorithm and any associated intervention would need to be carefully assessed prior to any implementation. Prior to any algorithm playing a role in supporting clinical practice / being implemented it will require peer review publication and broad acceptance before any uptake could be successful.

The algorithm will be free of charge and openly available. Access methods will be dependent on the strength of the algorithm but may include presentation at seminars, publications on risk factors or a clinical support tool provided directly to physicians (subject to any relevant approvals).

For an algorithm with weaker predictive potential IMS Health envisions the generation of publications in peer reviewed journals and generation of medical educational materials to use with clinical specialities who are potentially exposed to PAH patients. The literature will document the methodology used and the risk factors which would help to identify PAH patients earlier. These will potentially be presented at symposiums or other forums, depending on the findings.

If an algorithm with high predictive potential is generated, it could be used to create a clinical support tool for physicians to help diagnose patients, allowing the summarisation of large quantities of data in a more manageable format. This tool could support physicians by providing a risk score which they can interpret themselves to support clinician decisions.

If the applicant does not find any information of merit they will submit the methodology utilised in the research to a peer reviewed journal, this will allow other researchers to benefit from their research efforts. In addition the methodology will be shared via IMS Health Ltd’s online bibliography and which is publically available.

Processing:

To ensure the minimum amount of patient identifiable data is used and handled by the fewest people outside of the direct care team the following process is proposed:

1. STHFT shares with NHS Digital team, via a secure file transfer protocol, the NHS numbers of patients that have attended the SDPVU clinic since 2000, aligned to a generated study ID. The total number of this cohort is about 6500 patients

2. NHS Digital links to the identifiable cohort to data Admitted Patient Care, Outpatient and Accident & Emergency data, removes the NHS numbers and returned the de-identified extract (including study ID) to the STHFT informatics team which consist of patients in cohort 1. In addition, a pseudo-non sensitive extract is also provided consisting of the patients in cohort 2.

3. Patient data from STHFT is linked to the HES data via the generated study ID and done in compliance with all trust policies on patient data handling. This data is only accessible by the patient management team. Once linked the STHFT research informatics team will undertake the removal of all PID (including actual NHS number replaced with a pseudonymous NHS number). The linked pseudonymised data will then be loaded to a second logical environment also located within STHFT.

4. This environment will be remotely accessed within the STHFT DMZ by trained researchers (from IMS health, under confidentiality agreements). Access is granted using strong two factor authentication based on USB keys which produce one time use passwords (more information can be found at https://www.yubico.com/). The analysis conducted will be for the agreed research questions and will be performed only on pseudonymised patient information.

The applicant expects to conduct the following analysis with the data:
• Analysis of the diagnostic approach used in Sheffield and that used in other English specialist centers – expected to be completed 3-6 months after HES data has been provided

• Investigate the predictive patient characteristics within the data environment to understand if the applicant can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosis via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided

• In addition to investigating novel disease phenotypes – expected to be completed 18-24 months after HES data has been provided

Researchers who access the patient level HES data are logged on an access control register ensuring that it is possible to identify everyone with access to patient level information. Each researcher from IMS Health who will access the record level data has signed a user agreement that contains information on best practice and rules which must be abided by, rules in the agreement include the prevention of exporting any data from the Sheffield server that contravenes the HES small numbers protocol.

All individuals with access to the record level data are substantive employees of IMS Health Ltd save for researchers from other parts of the IMS group who may be required from time to time to provide expertise in analysis of the data. These individuals will work under an honorary contract to IMS Health Ltd. All individuals accessing the data under an honorary contract will be a substantive employee of the IMS company group. IMS Health Limited are not permitted to enter into honorary contracts with any individual who is not substantively employed by an IMS group company

During the analysis process of the anonymised and aggregated data there will be regular sessions with Sheffield and GSK clinical experts provide clinical perspective and impact of the results generated.

o The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis.

o For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health LTD from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease.

o A recent study of idiopathic PAH (IPAH) patients found that a significant delay of 3.9 years from symptom onset to a diagnosis of IPAH (Strange et al. 2013) 1. Indicating that a long time window is required and limiting that number of patients that will have the time window available for analysis.

o The phase 1 results indicated that there is a large variation in incidence/ diagnosis rates of iPAH, the Sheffield region diagnoses at a 4x higher rate compared to some other English regions, this means that there is potentially a high level of undiagnosed patients outside the Sheffield region
o To build the algorithm to support the diagnosis of patients nationally (not just in Sheffield) we require national data. The algorithm is built by looking at the healthcare interactions of the patient prior to diagnosis. There is a lot of regional variation on how a patient proceeds to diagnosis, driven by training, proximity to specialist centres, guidelines and various other factors. We want to build our model to account for this.


IMS Health Ltd will not in any circumstances attempt to re-identify the patients.

All outputs will be aggregated with small number suppressed in line with the HES analysis guide.


Any amendment to the collaboration agreement which affects the use of the HES data would require further application and approval by NHS Digital


Using Patient Data in Amyloidosis to Understand Complex Diagnosis Pathways and Treatment Patterns — DARS-NIC-60624-B1R2Q

Opt outs honoured: Y, Anonymised - ICO Code Compliant (Section 251 NHS Act 2006)

Legal basis: Health and Social Care Act 2012, Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Purposes: No, Yes (Supplier, Commercial)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2019-04-01 — 2020-03-31 2017.06 — 2017.08.

Access method: One-Off

Data-controller type: IQVIA LTD, IQVIA SOLUTIONS UK LIMITED, ROYAL FREE LONDON NHS FOUNDATION TRUST, IQVIA LTD, ROYAL FREE LONDON NHS FOUNDATION TRUST

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Accident and Emergency
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Outpatients

Objectives:

Amyloidosis is a very rare clinical disorder, caused by the deposition of insoluble misfolded proteins that aggregate in various tissues affecting their normal function. The disease consists of many different sub-types and the type of protein that is misfolded along with the organ or tissue in which the misfolded proteins are deposited determines the clinical manifestations of amyloidosis. Without treatment, amyloid fibrils accumulate and lead to organ impairment, failure, and ultimately death. The rarity of the disease and the multi system presentation of the disease are believed to lead to a large number of late or un-diagnosed patients. In subtypes such as AL amyloidosis, urgent diagnosis and treatment is essential to improve patient outcomes. Therefore finding new ways to help improve detection and diagnosis will greatly improve patient’s outcomes.

Parties involved:
Each party in the collaboration will have a different role during the research:
• The National Amyloidosis Centre (NAC) held at Royal Free London NHS Foundation Trust will support IMS Health Ltd’s ethical approvals activities; provide expert clinical insight on the research findings and support on NAC dataset de-identification in addition to supporting the publication of research findings.
• Glaxo Smith Kline (GSK) will provide expert clinical insight on the research findings in addition to supporting the publication and dissemination of research findings.
• IMS Health Ltd will conduct ethical approval activities; conduct the transformation and data processing of de-identified data into analysable format, and perform the analysis described in this document. IMS Health Ltd has significant experience with HES data, other retrospective databases, outcomes research expertise and advanced machine learning capabilities for predictive algorithm development.
• IMS Health Technology Services Limited will provide infrastructure and technical support to allow the hosting of de-identified HES and de-identified NAC data at the IMS Technology Services Staffordshire facility.

GSK is funding the research to better understand the therapy area in which they have medicines in development, expanding the pool of research in a relatively understudied disease area as well as supporting improved outcomes for patients via enhanced diagnosis procedures and practice. They approached the NAC for partnership due to their clinical expertise and clinical database which they hold. IMS Health Ltd has been asked to be involved in this partnership due their significant experience with HES data, other retrospective databases, outcomes research expertise and advanced machine learning capabilities for predictive algorithm development.

The NAC as England’s only amyloidosis diagnostic and treatment centres benefits from the research by furthering their understanding of patients’ journeys outside the NAC, supporting improvements in detection of amyloidosis and supporting better referral to the NAC by educating and sharing their learnings with other hospitals who may see undiagnosed amyloidosis patients.

Due to the nature of the research topics, findings which are controversial to either NAC or GSK are unlikely to arise. However to ensure findings are published fairly there will be a clinical interpretation group in place. This is comprised of 3 representatives of each GSK and the NAC and . The committee will perform the following functions:

1) Provide clinical interpretation of the results to support refinements of the analysis within the bounds of the protocol

2) Agree the dissemination / publication routes for research findings (e.g. conference posters vs peer review papers etc.) based on the nature and strength of findings. (Please see output section for further information).

No organisation on the clinical interpretation group will have the ability to suppress any of the findings or the outputs of the analysis.


Data controller/ processor justification:

IMS Health Technology Services Limited will have access to the data in order to provide technical support and apply derivations but will not otherwise process the data. Data access will otherwise be restricted to substantive employees of IMS Health Ltd. IMS Health Ltd has been labelled as both data processor and data controller. This is because IMS Health Ltd will be leading and conducting the analysis of the data based on the pre-defined protocol. They have this role due to their significant experience in patient pathway analytics, generation of predictive algorithms and working with HES data.

The analysis will be guided and conducted by IMS Health Ltd. This analysis will be conducted based of the pre-defined protocol, which members of the agreement are unable to alter as per the contractual agreements in place. IMS Health Ltd and IMS Health Technology Services Limited have capabilities in the area of predictive analytics and pathway analysis which GSK do not possess. IMS Health Ltd have developed bespoke sets of methodology which is expert driven from a group of employees with a strong academic and data science background. Critically they have focused on model interpretation which is not a priority in the machine learning field but in healthcare the interpretation is imperative. This means the GSK do not have the technical knowledge to guide and develop the analysis.

The patient selection criteria has been based on patients who attended NAC and those who have visited specialists which are frequented by patients with amyloidosis , this has been developed and chosen by IMS Health Ltd. The dissemination of findings have been pre-agreed and outlined in the outputs section.

Data retention times has been agreed in CAG, REC and in the data sharing agreement that will be in place with the NHS Digital upon approval of the application. If IMS health requires more time for the analysis they will request an extension on the agreement with NHS Digital.

The aims of the research are to:

• Understand the amyloidosis patient’s diagnostic pathway and outcomes. Including the implications of going through different routes to diagnosis, which can be used to develop materials which can help educate physicians on how to diagnose patients earlier;

• Identify barriers in the patient pathways to receiving diagnosis/treatment;

• Understand current coding in HES for different subtypes of amyloidosis, which can be used to support applications to change current ICD-10 coding practices in the UK and therefore enable capturing of more clinically accurate patient information nationally which can support future research efforts in this understudied condition;

• Develop a predictive algorithm which would be able to flag patients with a high probability of having amyloidosis (and subtypes) from their data “fingerprint”. This will support finding undiagnosed patients through developing a predictive algorithm.

In order to achieve the goals listed above, IMS Health Ltd proposes to link HES data to the National Amyloidosis Centre (NAC) dataset. This will allow IMS Health Ltd to create a combined dataset for research to better understand and improve the detection and treatment of amyloidosis.

Dissemination of results will be guided by the clinical interpretation group and if an effective predictive algorithm is produced, then efforts will be made to implement this in an appropriate manner given its capabilities.

Linking HES data with the NAC dataset will utilise the confirmed and sub-typed NAC patient diagnoses present in the NAC dataset, where the patient amyloid classification has been confirmed by world leading clinical experts. This will allow IMS Health Ltd to identify patients with confirmed amyloidosis (and subtypes of amyloidosis) within the HES data for investigation and analysis with high certainty.

Current ICD-10 coding (the International classification system for coding of disease types, maintained by the World Health Organisation) does not have a specific code for amyloidosis subtypes (i.e. Familial Amyloid Cardiomyopathy (FAC), Familial Amyloid Polyneuropathy (FAP), Amyloid light-chain (AL) amyloidosis), with multiple different subtypes coded under the same ICD-10 code. These subtypes have dramatically different outcomes and patient pathways and thus being able to differentiate the patients is key to the research.

The data requested will be filtered to;
1) Cohort A: Patients with confirmed amyloidosis, which consists of:
a) Consented Participants in the NAC database. Identifiers will be sent to NHS Digital in order to link study ID only to the HES data.
b) Patients who have an amyloidosis diagnosis code who have not attended the Royal Free (sourced from the HES database).

2) Cohort B: Patients with unconfirmed amyloidosis, which consists of the patients who visit specialities often visited by patients with an amyloidosis diagnosis (based on the presence of E85 ICD-10). Data will be restricted to only include patients holding 1 or more of 22 specialities of which have been visited at some point in time by the vast majority (>97%) of amyloidosis patients. This is required due to the rarity of the disease and research has found that patients can have a 7+ year diagnosis process due to the variety and complexity of symptoms. This will be sourced from the HES database.

The subset will exclude 8% of patients who attended the NAC since 2006 who have declined the use of their data for research, who will be highlighted by their NHS number shared from the NAC to NHS Digital.

IMS Health Ltd has selected a broad range of variables as when developing a predictive algorithm, the factors which may act as a “data fingerprint” are unclear until the process has started, removing particular variables thus can impact the power of the predictive algorithm, thus potentially detect patients with a high risk of having undiagnosed amyloidosis much later than if a full suite of variables was available.

IMS Health Ltd is requesting ~15 year historical extract of data for the amyloidosis project for both participants in the NAC database and patients who meet the criteria in the extract. The reason being that amyloidosis patient populations (especially at subtype level) are very small and the diagnosis pathway is a long multi-year processes and often complex. This requires HES data for a longer time period in order to capture sufficient patients for the analysis.

In amyloidosis physicians often do not initially attribute symptoms present to the rare disease in question. This means that patients are often misdiagnosed and seen by multiple physicians before an accurate diagnosis is made. In some cases patients will have a diagnosis process that takes years due to the variety and complexity of symptoms e.g. in SSA it has been shown that it can be 5.4 ± 4.4 years from onset to diagnosis (Nakagawa et al., 2016). >15 years of data will facilitate more robust and insightful analysis of these types of patient groups, and provide a better grounding for potential earlier diagnosis interventions in future.

Due to the complicated disease area and the need to create a sophisticated algorithm that has the potential to perform well in the live clinical environment, a large sample of data is required. Below is an overview of the reasons for the selection criteria:

1) Disease characteristics:

The aim is to create a predictive algorithm for multiple different amyloidosis subtypes (AL, FAP, FAC, Senile Systemic Amyloidosis (SSA)). Between these subtypes and even within these subtypes patients can exhibit large differences in clinical presentation. Even in the more defined FAC ATTR subtype, patients can exhibit GI and autonomic nervous system involvement in addition to the cardiac symptoms presented. Patients with FAP usually present between the ages of 20 – 40 whereas patients with SSA often present past the age of 70. This means that a large range of specialities, symptoms, procedures and demographics need to be assessed when generating algorithms and defining the cohorts. As each subtype will require their own comparison cohort, selected from cohort B.

2) Refining the comparison cohort (cohort B) based on clinical characteristics of the particular subtype:

In order to select the most appropriate cohort of patients to act as a comparison group to our confirmed amyloidosis patients (cohort A), we require to undergo analysis of the patient data, this is a data driven approach coupled with insights from the clinical specialists. As noted previously amyloidosis patients are often misdiagnosed as other conditions due to the rarity of the disease and huge range of clinical manifestations they can present with. The aim is to identify a cohort of patients which do not have a confirmed diagnosis but share very similar clinical features, have contaminant diagnoses, visit the same specialists etc. This allows the applicant to develop the algorithm on a comparison group as close to the real cases physicians experience in clinical practice as possible and thus ensure any algorithm developed is as robust as possible. For example QuintilesIMS created an algorithm to identify a rare disease population (Idiopathic Pulmonary Fibrosis), which manifests as a lung condition commonly misdiagnosed as asthma or COPD. To focus the algorithm on the clinical challenge we developed the algorithm to distinguish between IPF patients and those with COPD/Asthma. Amyloidosis is a significantly more complex disease than the previous example and requires a deep dive into the data to align the patient cohorts

3) Refining the cohorts (both A and B) based on availability of appropriate length of historic data:

The size of a cohort is limited not only by the number of patients with given diagnosis, but also the need to have available a sufficient time period both prior to the diagnosis (to observe baseline characteristics) as well as after the event (to observe relevant outcomes) for analysis. For example a recent project in Fabry Disease, one focus of analysis was to understand the diagnostic pathway, in order to identify any predictive signals/ markers which would allow earlier diagnosis of Fabry disease and thus slowing progression of the disease by allowing earlier treatment. The study by IMS Health Ltd identified 665 patients with suspected Fabry disease, of those patients only 90 patients had 3+ years of historical data available to allow analysis of the lead up to patient diagnosis (which was much shorter than desirable given the often 20 year symptom onset in this condition). This patient cohort size prevented IMS Health Ltd from having sufficient numbers to conduct robust predictive analytics on the data to find signals/ markers of disease.

Through the years of managing and diagnosing amyloidosis patients, the Honorary Consultant Nephrologist at the NAC, has noticed that > 50% of cases of patients with the FAC subtype of amyloidosis have prior carpel tunnel syndrome/ decompression occurrence in patients up to 10 years prior to diagnosis. It is IMS Health Ltd hypothesis that this in conjunction with other attributes may act as a predictive marker of early FAC disease. Due to the length of the timeframe IMS Health Ltd have requested >15 years of HES data.

4) Bringing the algorithm to clinical practice:

If the algorithm were to be implemented in a live clinical practice setting the algorithm can only run on patients who fit inclusion and exclusion criteria used to pull HES data. Therefore the narrower the patient sample we request means that the more limited real world sample that can be assessed for risk of disease. For example if we only requested a sample of HES data made up of male patients who are over 40 years old. This would mean that we could not expect the model to produce robust predictions for any female patients or patients under the age of 40 – limiting the potential benefits of the outputs.

References:
Nakagawa. M et al, Carpal tunnel syndrome: a common initial symptom of systemic wild-type ATTR (ATTRwt) amyloidosis, Amyloid. 2016;23(1):58-63. doi: 10.3109/13506129.2015.1135792. Epub 2016 Feb 8.

Yielded Benefits:

Based on the data provided for the previous agreement, IQVIA has developed a full set of analysis regarding the NAC patient pathway for Amyloidosis which have been discussed with both the NAC (the academic collaborators) and GSK (an industry partner who previously, but no longer, sponsored this research). As a result of these discussions, there is a considerably better understanding of the Amyloidosis patient pathway, including per subtype. For example, for ATTR-CM patients treated at the NAC: - There is a substantial delay in diagnosis following onset of symptoms, with patients using hospital services (either as an inpatient, outpatient or Accident & Emergency visit) a mean of 19.9 times during the 3 years before diagnosis; diagnosis of the wild-type form of ATTR-CM was delayed more than 4 years after onset of cardiac symptoms in 42% of cases - Hereditary cardiac amyloidosis patients with a certain genetic mutation (V122I) were more impaired functionally and had worse measures of cardiac disease at the time of diagnosis, and poorer survival compared to the other sub-groups (such as patients carrying a different genetic mutation, and patients with wild-type form of the disease) - Analysis of the diagnostic and treatment pathways for different amyloidosis subtypes has also been completed after HES data was provided, leading to improved knowledge and patient benefits. All parties now have a better understanding of treatments that occur outside of the NAC and patient outcomes. A set of papers from the outputs of the analysis have been already published as mentioned above or will be developed into publication and published in research journals, with one manuscript, entitled Natural history, quality of life and outcomes in cardiac ATTR amyloidosis, already published in the journal Circulation. Through publication, the results have been shared across the scientific community, increasing the body of knowledge on Amyloidosis disease and benefiting Amyloidosis patients with potential future treatment innovations.

Expected Benefits:

There are likely benefits from this research for patients, the NHS, academia and life sciences companies. Overall there are large gaps of knowledge within amyloidosis, especially when looking at a subtype level. Understanding more about patient journeys through the secondary care system can help identify ways of improving diagnosis and treatment, as well as potentially providing evidence to support applications for novel therapies in this highly under served disease area. This would potentially allow patients to get access to new treatment options, and provide health economic information to help design a more efficient care pathway for amyloidosis patients. This more efficient care pathway could potentially lessen the burden on patients by reducing repeat visits during patient’s diagnostic pathways and supporting earlier diagnosis to improve patient treatment outcomes. In heritable forms of the disease benefits may well subsequently advantage patient’s family members. Specifically the outputs from each part of the research.

Patient pathway analysis:

• The healthcare community & academia will gain a better understanding of the diagnosis and treatment of amyloidosis patients in England, providing opportunities to identify areas to improve services, improve the patient journey, provide earlier treatment and to improve quality of life for patients.

• Furthermore participants and non-participants will have increased access to information about their disease from the production of publications of study findings, which will be made available through the listed IMS website (noted on the posters at the NAC), and potentially other channels e.g. UKAAG who support this research

• The evidence produced will help inform research direction for novel treatment in this severely under-served disease.

Predictive algorithm outputs:

• An algorithm supporting earlier diagnosis would be of benefit to patients and the NHS if outcomes and patient experience (i.e. fewer hospital visits for diagnostics) can be improved.

• By supporting earlier diagnosis diagnostic costs per patient could be reduced which would benefit the NHS

• However total costs of treating this population could potentially rise. (This would need detailed health economic analysis to assess more fully – at this moment it can only be speculated given the paucity of research of this nature in this condition).

• Finally a more rapidly diagnosed amyloidosis population may benefit the multiple life science companies who are currently developing novel amyloidosis therapies.

Ultimately the balance of these benefits would be dependent upon by the quality and interest of the descriptive findings, the robustness of the algorithm combined with any interventions put in place around it.

Outputs:

IMS Health Ltd expect to produce the following analyses:
• Analysis of the diagnostic and treatment pathways for different amyloidosis subtypes– expected to be completed 3-6 months after HES data has been provided
• Investigate the predictive patient characteristics within the data environment to understand if IMS Health Ltd can support the flagging of patient earlier in their diagnostic pathway or flag patients who have not yet been diagnosed via the development of a predictive algorithm – expected to be completed 12-18 months after HES data has been provided.

The target dissemination plan is as follows:

• The applicant will submit the findings of the research to a peer review journal e.g. Rheumatology

• The applicant will submit and present on findings at a relevant amyloidosis conference e.g. 2018 International Symposium on Amyloidosis, in order to further the knowledge of other specialist physicians

• Published results will be shared with the UKAAG patient advocacy group

• Furthermore the abstracts and links to publications will be hosted on IMS Health Ltd’s online bibliography which is publically available

• Results will also be shared with other parties where appropriate e.g. Sharing results with other NHS trusts who also manage amyloidosis patients or sharing with international centres which also diagnose and manage amyloidosis patients

The output of the algorithm generation is currently uncertain. However any implementation would need to be conducted by or with NHS bodies, because we are working with pseudonymous data and will not seek to re-identify patients at any stage. The nature of any implementation would need to be driven by the predictive sensitivity and specificity of the algorithm. In other words, the false positive and false negative detection rate. Implementing an algorithm with a high false positive rate would lead to many people tested with very few identified, conversely if the algorithm has a high false negative rate, it will likely miss many patients who should be tested for the disease. The health economics of the algorithm and any associated intervention would need to be carefully assessed prior to any implementation. Prior to any algorithm playing a role in supporting clinical practice / being implemented it will require peer review publication and broad acceptance before any uptake could be successful.

For an algorithm with weaker predictive potential IMS Health envisions the generation of publications in peer reviewed journals and generation of medical educational materials to use with clinical specialities who are potentially exposed to amyloidosis patients. The literature will document the methodology used and the risk factors which would help to identify amyloidosis patients earlier. These will potentially be presented at symposiums or other forums, depending on the findings.

If an algorithm with high predictive potential is generated, it could be used to create a clinical support tool for physicians to help diagnose patients, allowing the summarisation of large quantities of data in a more manageable format. This tool could support physicians by providing a risk score which they can interpret themselves to support clinician decisions.

The algorithm will be free of charge and openly available. Access methods will be dependent on the strength of the algorithm but may include presentation at seminars, publications on risk factors or a clinical support tool provided directly to physicians (subject to any relevant approvals)

If no information of merit is found, the methodology utilised in the research will be documented and submitted to a peer reviewed journal. This will allow other researchers to benefit from the research efforts. In addition the methodology will be shared via IMS Health Ltd’s online bibliography and which is publically available. In all summaries, any data used will be aggregated with small numbers suppressed in line with the HES Analysis Guide.

No organisation on the clinical interpretation group will have the ability to suppress the dissemination of findings or outputs from this work.

Processing:

In order to link the NAC dataset & HES data identifiable information is required to be passed from the National Amyloidosis Centre (NAC) (Royal Free Foundation Trust) to NHS Digital, in the form of the patients’ NHS numbers.

To ensure the minimum amount of patient identifiable data is used and handled by the fewest people outside of the direct care team IMS Health Ltd propose the following process:

1) The NAC generates a study ID for each patient managed at the NAC. The NAC then shares with NHS Digital the NHS number and study IDs of the patients managed at the NAC excluding patients who withheld consent over the secure NHS N3 network. This is cohort A.

2) The NAC provides a separate list of NHS numbers of patients managed at the NAC who withheld consent.

3) NHS Digital identifies a second cohort (cohort B) of eligible patients who had episodes with specific ICD-10 codes or who attended a particular specialist indicating or potentially indicating an instance of amyloidosis.

4) NHS Digital removes from the second cohort (cohort B) any individuals whose NHS number was included in the second list (i.e. the list of NHS numbers of patients managed at the NAC who withheld consent).

5) NHS Digital merges the two cohort lists (cohort A and cohort B), links the NHS numbers and extracts the relevant HES records of these individuals. Study IDs will be included in the linked extract for any individuals in the first cohort (cohort A).

6) NHS Digital shares the pseudonymised, non-sensitive extracts of HES Admitted Patient Care, A&E and Outpatient data with IMS Health Technology Services Ltd including study ID. IMS Health Technology Services Ltd will then clean and apply derivations before passing control to IMS Health Ltd.

7) A pseudonymised subset of the National Amyloidosis Centre dataset is shared with IMS Health Ltd under appropriate collaboration agreement between IMS Health Ltd and the NAC (Royal Free Foundation Trust). This will contain no identifiers other than the study ID. The data is securely transferred to a secure server provided by IMS Health Technology Services Ltd.

8) IMS Health Ltd links the HES data extracts with the de-identified NAC dataset by matching study IDs incorporated into the HES extract shared by NHS Digital with those present in the NAC dataset. No data is flowing between institutions in this step.

IMS Health Technology Services Limited will provide infrastructure and support to allow the hosting of de-identified HES and de-identified NAC data at the IMS Staffordshire facility. IMS Health Technology Services Ltd is responsible for the enforcement of appropriate safeguards and processes. The NAC - HES linked dataset will be stored on the IMS Health Technology Services server in Stafford. IMS Health Technology Services Ltd is ISO 27001 security compliant. Once linked and once derivations have been applied (by IMS Health Technology Services Ltd), access to this database will be restricted to a named user list of IMS Health Ltd researchers in the London office, all of whom are substantive employees of IMS Health Ltd, and will be via VPN remote desk top into Stafford to access this server. All analysis will take place on this server. All research activities will be conducted on pseudonymised data at IMS Health Ltd. The HES linked data will not leave IMS Health Technology Services Ltd. Both IMS Health Ltd and IMS Health Technology Services Ltd follow NHS Digital HES analysis guidelines and required security policies to ensure that data is handled appropriately with all outputs being in aggregate form with small numbers suppressed in line with the HES Analysis Guide.

IMS Health Ltd and IMS Technology Services comply with all NHS Digital security requirements on HES data access, hardware security, data backup and secure hardware destruction. All employees requiring access have been given formal training in data security and ISO 27001 requirements. As the data processor, IMS Health Ltd will only process pseudonymised non-sensitive data.

IMS Health Ltd has an Information Security Management system in place which is compliant with ISO27001 standards and externally audited by BSI.

IMS Health Ltd employees who access the patient level HES data are logged on an access control register ensuring that it is possible to identify everyone with access to patient level information. Before being given access, the employees receive training on ISO27001 to teach best practice on information security. They also receive training on Hospital Episode Statistics, IMS Health's ethical and contractual obligations around the data and best practice for processing. Finally a user agreement is signed by each employee able to access patient level information containing information on best practice and rules which must be abided by.

Researchers who are not substantive employees of IMS Health must have an honorary contract in place in order to access HES record level data. All individuals accessing the data under an honorary contract will be a substantive employee of the IMS company group. IMS Health Limited are not permitted to enter into an honorary contract with any individual who is not substantively employed by an IMS group company.

The research conducted on this combined de-identified dataset will be for the agreed research questions and will be performed on de-identified patient information and shared in aggregated form, with small numbers suppressed in line with the HES Analysis Guide, with GSK and the NAC.

IMS Health Ltd will not in any circumstances attempt or even be able to re-identify the patients. The NAC de-identified data would not be significantly additive to re-identify patients when joined to HES data. IMS will not seek to re-identify the de-identified NAC data or the linked HES-NAC dataset.

GSK will not receive any patient level data. They will only receive aggregate data which will be used in the production of medical education and publications of findings. All data will be aggregated with small numbers suppressed in line with the HES small number guidelines before being moved off the server and presented. Data is only held and processed in the UK, whilst the aggregated outputs might be used internationally with small numbers suppressed.