NHS Digital Data Release Register - reformatted

Erasmus University Medical Centre projects

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


Metastatic cutaneous Squamous Cell Carcinoma (cSCC) in England 2013-2015– assessment of staging systems and histological risk factors for metastasis. ( ODR1819_225 ) — DARS-NIC-656837-J7G8S

Type of data: information not disclosed for TRE projects

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

Legal basis: Health and Social Care Act 2012 - s261(5)(d)

Purposes: No (Private Healthcare)

Sensitive: Non-Sensitive

When:DSA runs 2022-10-20 — 2023-10-19 2023.01 — 2023.02.

Access method: One-Off

Data-controller type: ERASMUS UNIVERSITY MEDICAL CENTRE

Sublicensing allowed: No

Datasets:

  1. NDRS Cancer Registry
  2. NDRS Cancer Registrations

Objectives:

Review of known cases of metastatic cSCC tumours with comparison to non-meta-static cSCC tumour characteristics such as thickness and diameter and comparison of current staging systems. Primary outcome will be risk of metastasis, secondary outcome will be risk of death in metastatic cases and potential improvements to cur-rent staging systems. Venables Z C will be collaborating with the team in Erasmus University, Rotterdam to analyse the data.

Yielded Benefits:

The risk prediction model has already been developed and validated using the data from ODR1819_225. It performs better than the current staging systems. We noticed during the analyses, that we overestimate the risk of metastasis, but we think that this is due to different inclusion criteria of the Dutch and UK cohorts. Therefore we need the requested variables, in order to make the cohorts more comparable and improve the validation analyses.

Expected Benefits:

Currently, cSCC patients at high risk of metastasis receive insufficient number of follow-up visits (Wakkee et al, Eur J Cancer 2019). Current staging systems are insufficient for patient stratification (Venables et al, BJD, 2022). Therefore an improved risk prediction model will be of added value and will help clinicians to decide about adequate follow-up care for patients at high risk of cSCC. It also means that patients at low risk of cSCC can have no or reduced follow-up visits, which may lead to more cost-efficient health care for those patients. At the date of publication (~Q1 2023), we wil also refer to a web application, which can be used by other researchers. The web application has already been developed. The model needs to be further validated by other researchers after publication before use in clinical practice is possible.

Outputs:

An risk prediction model, which can be used to calculate the absolute risk of metastasis among patients with cSCC.
Target date: the calculation of the weights is the last analyses , which is needed to validate the model. The article has been written already, thus submission of the article can be expected within a month after receiving the data.

Processing:

We have applied a nested case control design. This means that we have detailed information about all cases (patients with cutaneous squamous cell carcinoma [cSCC] with metastasis) and controls (patients with cSCC without metastasis).
These cases and controls were sampled from a cohort. The cohort consists of all cSCC between 2013-2015. We recently received data from the entire cohort. In the data analyses we need to assign a weight to each control. The weight corresponds to the probability of being samples as control from the entire cohort. This is what we already did with the data that we received. However, controls were sampled from 2013, not from the entire study period. This means, that the weights for each control may be different and we would like to know if this influences the outcome of the study.
Therefore we need to know whether a cSCC was diagnosed in 2013 or not, so we can adjust the weights accordingly.
Also to make the cohort comparable to the Dutch cohort and we need to know whether or not a patient was diagnosed with a cSCC before 2013.

General Data Protection Regulation Article 6 (1) (e)
General Data Protection Regulation Article 9 (2) (c)

To summarize we need the following 2 variables to calculate weights:
-Tumor variable: cSCC diagnosed in 2013 yes/no
-Patient variable: patient had cSCC before 2013 yes/no