NHS Digital Data Release Register - reformatted

Imperial College Healthcare NHS Trust projects

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


Braina CaVa: Care, variation, outcomes, and costs in patients with brain tumours in England. (ODR1819_236) — DARS-NIC-656838-J7H7S

Type of data: information not disclosed for TRE projects

Opt outs honoured: Anonymised - ICO Code Compliant (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 – s261(2)(a)

Purposes: No (NHS Trust)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2023-06-26 — 2024-06-25

Access method: One-Off

Data-controller type: IMPERIAL COLLEGE LONDON

Sublicensing allowed: No

Datasets:

  1. NDRS Cancer Registrations
  2. NDRS Linked Cancer Waiting Times (Treatments only)
  3. NDRS Linked DIDs
  4. NDRS Linked HES AE
  5. NDRS Linked HES APC
  6. NDRS Linked HES Outpatient
  7. NDRS National Cancer Patient Experience Survey (CPES)
  8. NDRS National Radiotherapy Dataset (RTDS)
  9. NDRS Systemic Anti-Cancer Therapy Dataset (SACT)

Objectives:

Primary brain tumours are the leading cause of cancer death in the under the 40s and have the highest average number of years of life lost. Although primary brain metastases are rare, they are the leading cause of cancer death in the under the 40s, and 10 – 15% of all patients with extra-cranial cancers develop brain metastases, with a poor prognosis.
The aims of this project are to:
• To provide a comprehensive view of patterns of care (surgery, chemotherapy & radiotherapy), patient events (hospital admissions, death), outcomes (overall survival and novel outcomes) and costs of care (in-patient, outpatient; direct and indirect care costs) in adult patients with primary Central Nervous System (CNS) tumours in England.
• To assess variations in care, systematic drivers of variation in care, and associations between variations in care and outcomes and costs.
• To explore the correlation between biology and outcomes by comparing the relative impact of tumour biology vs. treatment effect on outcomes, admissions and costs.
Reviewed and Approved

In line with the National data opt-out policy, opt-outs are not applied because the data is not Confidential Patient Information as defined in section 251(10) and (11) of the National Health Service Act 2006

Where individuals have opted out of disease registration by the National Disease Registration Service (NDRS), their data has been permanently removed from the registry and therefore will not be disseminated under this Data Sharing Agreement (DSA). https://digital.nhs.uk/ndrs/patients/opting-out

Yielded Benefits:

The study team have explored: - costs of treatments and non treatments of patients diagnosed with a glioblastoma (WHO Grade IV brain tumours) or with a meningioma (WHO Grade I); - 30-day complication following a major resection; - the incidence, treatments and admissions of patients diagnosed with a glioblastoma; - end-of-life care for patients diagnosed with a primary brain tumour; - effect of dyads on outcomes and survival.

Expected Benefits:

To provide a comprehensive view of patterns of care (surgery, chemotherapy & radiotherapy), patient events (hospital admissions, death), outcomes (overall survival and novel outcomes) and costs of care (in-patient, outpatient; direct and indirect care costs) in adult patients with primary, secondary brain tumours or with a suspicion of brain tumour in England.
To assess variations in care, systematic drivers of variation in care, and associations between variations in care and outcomes and costs.
To explore the correlation between biology and outcomes by comparing the relative impact of tumour biology vs. treatment effect on outcomes, admissions and costs.

Outputs:

To provide a comprehensive view of patterns of care (surgery, chemotherapy & radiotherapy), patient events (hospital admissions, death), outcomes (overall survival and novel outcomes) and costs of care (in-patient, outpatient; direct and indirect care costs) in adult patients with primary, secondary brain tumours or with a suspicion of brain tumour in England.
To assess variations in care, systematic drivers of variation in care, and associations between variations in care and outcomes and costs.
To explore the correlation between biology and outcomes by comparing the relative impact of tumour biology vs. treatment effect on outcomes, admissions and costs.
Since the data extraction (August 2020), we had two more updates, in May 2021 and October 2022 as Public Health England, NHS Digital and NHS England realised the data sent was incomplete, hence this data extension.

Processing:

The study team will analyse summary treatment patterns at centre level and at centre-dyad level. Centre-dyads will be defined based on patterns of co-care; two centres that share >=33% of their patients is a centre-dyad. Each centre-dyad will be modelled as a single unit (the partner-actor interaction model is not appropriate here). Analyses will examine rates of histological diagnosis, surgical extent, 30 day mortality and prolonged admission (>75th centile duration of admission), rates of maximal treatment and patient safety events (Objectives A, B, C). Patient safety events will be based on the translated AHRQ patient safety indicators 14,15. Novel endpoints will be defined in conjunction with our charity partners, patients and carers.

The study team will conduct sensitivity analyses based on geographic patterns of referral (in-areas only vs. all) 16. Estimates of absolute cost but cannot be used to calculate relative cost-effectiveness; but within each pathway the study team will calculate the relative costs of treatment vs. non-treatment inpatient care (Objective D).


As an illustrative example, if the study team assume the impact on oncology volume is the same as the impact of surgical volume, the study team would expect to find a difference in HR of ~0.6 between low-volume (<20) and high volume (>80) centres 6. 12 month mortality then might be expected to vary between 50% and 30%, with 95% CIs of +/- < 4%. The study team are therefore confident that the study will have the power to detect differences that exist, in part due to the poor prognosis, and high event rates, for patients primary or secondary with brain tumours. In addition, these provide a direct route to improving outcomes in the short-term: this magnitude of difference would equate to ~ 200 patients per year in the GBM cohort alone.

7.2 Statistical methods

The study team will evaluate the impact of patient, tumour and treatment factors on outcomes in patients with primary or secondary brain tumours in England. The study team will carry out analyses using patient-level data, but analyse volume effects for centres and dyads using a centre/dyad volumes in a random-effects model; Assessment of risks and outcomes will be carried out at individual patient level on all the patients in the dataset 17.

The study team will include sex and exact age, tumour diagnosis using combined ICD-10 and ICD-O3 (5 categories), Charlson index derived from secondary ICD-10 diagnostic codes, presence of 16 comorbidities included in the Charlson index, ethnic group, deprivation quintile and distance from both oncology and surgical centre. All these data items are in, or derivable from, our linked dataset.

The study team will construct proportional hazards models. The models will be used to predict the n-year probability of event for every patient, and aggregated to construct n-year probabilities of event by (hospital/ surgeon/ region). The adequacy of the proportional hazards model will be assessed using martingale residual plots and Schoenberg residual plots. If there the model does not meet the criteria for a Cox model, we will consider other modelling approaches (e.g. AFT models); in all cases we will construct parsimonious models using Akaike’s information criterion. We will use binary indicators for mortality at 30 and 90 days after diagnosis and use regression coefficients to predict expected outcome.

The study team will assess the impact of biology and treatment (Objective E) in carefully defined sub-cohorts, then further adjusted using propensity methods. Comparisons of the consequences of a brain tumour diagnosis will be made between primary and secondary diagnosis with the comparison inversely weighted by a propensity score calculated from relevant confounding variables such as age and gender. The study team will use the inverse PS method as none of the groups are “unexposed”. The study will conduct multiple independent analyses in an attempt to reduce bias, and report methods and results in line with recommendations 17.

7.3 Health Economics

The study team will use reference costs from NHS Tariffs for 2015. We will estimate direct costs for 3 months before diagnosis and 12 months after diagnosis. We will distinguish between costs incurred in the in-patient and outpatient setting, planned and emergency care, during and after treatment. We will use NHS reference costs, length of stay and other measures of resource (e.g. intensive care days and treatment procedures) to estimate costs and will examine the impact on care and patient characteristics on total pathway costs.

The study team will distinguish direct treatment costs – neurosurgery, radiotherapy and chemotherapy from indirect treatment costs, based on procedure codes (HES) and data on chemotherapy and radiotherapy (RTDS and SACT)