Good TREs work

Hull University Teaching Hospitals NHS Trust projects

• Assessing Diabetes’ Influence on Cardiovascular Health: A Machine Learning Analysis of NICOR Database Patients
• Evaluating the current standard of care for patients diagnosed with malignant melanoma of the head and neck – access to staging and surgical treatment. (ODR2122_2697) (partially via "system access")

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

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Assessing Diabetes’ Influence on Cardiovascular Health: A Machine Learning Analysis of NICOR Database Patients — DARS-NIC-719601-J7Z2S

Opt outs honoured: (Excuses: Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 – s261(2)(a); Other-GDPR does not apply to data solely relating to deceased individuals, Health and Social Care Act 2012 – s261(2)(a)

Purposes: No (NHS Trust)

Sensitive: Sensitive, and Non-Sensitive

When:DSA runs 2025-01 – 2028-01

Access method: One-Off

Data-controller type: HULL UNIVERSITY TEACHING HOSPITALS NHS TRUST

Sublicensing allowed: No

Datasets:

1. Civil Registrations of Death
2. Emergency Care Data Set (ECDS)
3. Hospital Episode Statistics Accident and Emergency (HES A and E)
4. Hospital Episode Statistics Admitted Patient Care (HES APC)

Type of data: Anonymised - ICO Code Compliant

Objectives:

Hull University Teaching Hospitals (HUTH) NHS Trust requires access to NHS England data for the purpose of the following research project:
Assessing Diabetes’ Influence on Cardiovascular Health: A Machine Learning Analysis of NICOR* Database Patients.

*NICOR: National Institute for Cardiovascular Outcomes Research

The following is a summary of the aims of the research project provided by the Controller:

People with diabetes have poor cardiovascular outcomes (survival, stent-restenosis, and recurrent myocardial infarction (MI)) as compared to those who do not have diabetes.

The Study's objective is to use various machine learning algorithms to understand the Relative Influence (RI)* of diabetes on survival, stent-stenosis, and recurrent myocardial infarction in people with cardiovascular disease (CVD).

* Relative Influence (RI) refers to a metric or analysis that quantifies the importance or contribution of each feature (input variable) in the model's predictions. RI helps data scientists and analysts understand which features have the most impact on the model's decision-making process.

Currently, there is a huge amount of data which show that the co-existence of diabetes is associated with poor cardiovascular outcomes in heart-failure, coronary revascularization and ischemic heart disease as compared to those who do not have diabetes.

However, many of these co-morbidities such as poor glycaemic control, hyperlipidaemia, hypertension, are often correlated and traditional regression models are unable to identify top predictors of outcomes in people with Cardio Vascular Disease (CVD).

Hence, HUTH NHS Trust aim to use various machine learning algorithms to identify the RI of diabetes on outcomes in heart-failure, revascularization, and ischemic heart disease.

HUTH NHS Trust will conduct an observational study using pseudonymised patient data from the NICOR database. The study population will include patients diagnosed with either an acute coronary syndrome (as registered with the Myocardial Ischaemia National Audit Project, MINAP), or heart failure (from the National Heart Failure Audit, NHFA) or those who undergo Percutaneous Coronary Intervention (PCI) (identified in the National Audit of Percutaneous Coronary Intervention, NAPCI) including elective, urgent and emergency procedures, stratified by the presence or absence of diabetes.

This study aims to find new associations between diabetes and cardiovascular disease to identify higher risk groups and to target therapies towards them to prevent adverse outcomes.

HUTH NHS Trust will use 9 machine learning algorithms for classification, assessing and obtaining the best model for ascertaining the RI of diabetes on various cardiovascular outcomes. HUTH NHS Trust, will utilise other machine learning tools to understand if any of the baseline medication or procedural data can be used to predict outcome measures in people living with diabetes.

The following NHS England Data will be accessed:

Hospital Episode Statistics:
• Admitted Patient Care (APC) – necessary to provide information on hospital admissions for certain cardiovascular conditions in the prescribed cohort (from NICOR) to compare inter-group variation stratified by the presence of diabetes.
HUTH NHS Trust will only be focussing on hospital presentations and admissions that have the diagnoses of Angina, Myocardial infarction (MI), Acute coronary syndrome (ACS), Decompensated Heart Failure, Ventricular Tachycardia (VT), complications of MI and cardiac arrest.

• Accident and Emergency (A&E) and Emergency Care Data Set (ECDS) – necessary to provide information on admissions that have the diagnoses of Angina, Myocardial infarction (MI), Acute coronary syndrome (ACS), Decompensated Heart Failure, Ventricular Tachycardia (VT), complications of MI and cardiac arrest.

• Civil Registration Mortality – necessary to identify patients as having had an initial episode of an MI, heart failure or coronary stenting who have subsequently died in the community.

HUTH NHS Trust would require the date of death to compare this with the time of their initial presentation and entry into NICOR. This would allow HUTH NHS Trust to assess how long on average it takes from an index clinical event to a mortality outcome and how it relates to patients with diabetes. Having the age at the time of death would serve to further stratify patients based on age.

HUTH NHS Trust aim is to evaluate the differences in modifiable and non-modifiable risk factors noted in the NICOR data and how they relate to mortality in patients stratified by the presence of diabetes.

The level of the Data will be:
Pseudonymised

The Data will be minimised as follows :
• Limited to a study cohort identified by NICOR*, for patients in the National Heart Failure Audit (NHFA) Myocardial Ischaemia National Audit Project (MINAP) and National Audit for Percutaneous Coronary Intervention (NAPCI). With ~100,000 annual records in both the MINAP and NAPCI registries and >80,000 participants in the NHFA. It is estimated that 1,000,000 individuals will be identified.

*NICOR is part of NHS England and the flow of identifiers is carried out internally within NHS England. As such, the requirement to address the common law duty of confidentiality is not needed as the flow of data is within NHS England.

• Limited to data between 2011/12 to latest available data to capture cardiovascular conditions and deaths. The data will be minimised further by being limited to begin at each individual’s inclusion date (for example, if a patient’s inclusion date is March 2018, data will be filtered to ensure only records on or after March 2018 is produced and disseminated). The data required will be all cause hospital admissions rather than selected cardiac events alone. This is so HUTH can identify if non-cardiac events influenced further cardiac recurrences.

The data disseminated by NHS England will be as described above.

The lawful basis for processing personal data under the United Kingdom (UK) GDPR is:

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 lawful basis for processing special category data under the UK GDPR is:

Article 9(2)(j) - processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.

This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research, which protects and promotes the interests of patients, service users and the public, and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.

The funding is provided by the University of Hull. The funding is specifically for the study described.

The funder will have no ability to suppress or otherwise limit the publication of findings.

No Public and Patient Engagement activities have been undertaken by HUTH NHS Trust.

Expected Benefits:

The aim of the study is to find novel associations between diabetes and different forms of heart disease using machine learning models. Traditionally statistical models which have been used for this purpose. Traditional statistical analysis is user dependent and depending on the choice of test used subject to errors which are largely alleviated when using machine learning. In addition, traditional statistical tests are better suited to linear models while diabetes and heart disease usually present with multiple other co-morbidites with a complex relationship which is dependent on multiple inter-related variables. Machine learning can capture these non-linear relationships better.

HUTH NHS Trust aim to identify the relative influence of different diabetes treatment options and other procedural and patient factors in affecting cardiovascular outcomes. The study will be looking at 3 groups of patients, based on their diagnosis they will be split into 3 groups of heart failure, myocardial infarction and/or angina who have undergone percutaneous coronary intervention. If successful in identifying new links between diabetes, its treatment and these conditions, our research should help generate hypothesis for future clinical trials and research to confirm these findings.

Identifying the significance of individual risk factors or new risk factors would help us better understand the health and care needs of individuals with these conditions. It would advance our comprehension of underlying disease processes allowing targeting higher risk groups with relevant preventative therapies. Lastly, downstream from such hypothesis generating studies, successful trials would ideally lead to identifying new targets for therapy to improve patient prognosis

It is hoped that through publication of findings in appropriate media, the outcomes of this research will add to the body of evidence that is considered by the organisations and individual care practitioners charged with making policy decisions for, those within the NHS, or treatment decisions in relation to specific patients.

Outputs:

The expected outputs of the processing will be:

• Submissions to peer reviewed journals European Heart Journal, British Journal of Diabetes, Diabetologia, European Heart Journal, British Journal of Cardiology, and European Medical Journal. approximately 4 months after receiving the data.

• Presentation at national and international conferences of the aforementioned research outputs by 30/12/2025.

Processing:

The NICOR team (within NHS England) will transfer the cohort identifiers to NHS England data production team. The data will consist of identifying details: NHS Number, Date of Birth, Postcode, Gender, Forename, Surname and a unique person ID to be linked with NHS England data.

The cohort will consist of patients who would have been recorded in the NICOR datasets as having had an initial episode of an MI, heart failure or coronary stenting.

NHS England will provide the relevant records from the HES, ECDS and mortality datasets to NICOR.

The Data will contain no direct identifying data items but will contain a unique person ID which can be used to link the Data with other record level data already held by the recipient (NICOR).

NICOR will link the HES, ECDS and mortality data with clinical data from the NHFA, MINAP and NAPCI audits specifically for the individuals in the cohort.

NICOR will fully pseudonymise and clean the data and send the complete pseudonymised record level data extract to HUTH NHS Trust.

The Data (that NICOR will disseminate to HUTH) will contain no direct identifying data items. The Data will be pseudonymised and individuals cannot be reidentified through linkage with other data in the possession of the recipient.

The Data will be stored on servers at HUTH NHS Trust.

The Data will be accessed onsite at the premises of HUTH NHS Trust only.

The Data will not leave England.

Access is restricted to employees of HUTH NHS Trust.

Employees of HUTH NHS Trust are permitted to access pseudonymised data only.

All personnel accessing the Data have been appropriately trained in data protection and confidentiality.

The Data will not be linked with any other data.

There will be no requirement and no attempt to reidentify individuals when using the Data.
All analyses will use the pseudonymised dataset.

Researchers from the HUTH NHS Trust will analyse the Data for the purposes described above.

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Evaluating the current standard of care for patients diagnosed with malignant melanoma of the head and neck – access to staging and surgical treatment. (ODR2122_2697) — DARS-NIC-682532-B4B5L

Opt outs honoured: unknown (Excuses: Does not include the flow of confidential data)

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

Purposes: No (NHS Trust)

Sensitive: Non-Sensitive

When:DSA runs 2024-10 – 2025-10 2024.11 — 2025.02.

Access method: System Access
(System access exclusively means data was not disseminated, but was accessed under supervision on NHS Digital's systems)

Data-controller type: HULL UNIVERSITY TEACHING HOSPITALS NHS TRUST

Sublicensing allowed: No

Datasets:

1. NDRS Cancer Consolidated Data Set

Type of data: Anonymised - ICO Code Compliant (note: this information not disclosed for TRE projects )

Objectives:

Hull University Teaching Hospitals NHS Trust (HUTH) requires access to NHS England data for the purpose of the following research project:

Evaluating the current standard of care for patients diagnosed with malignant melanoma of the head and neck – access to staging and surgical treatment.

Melanoma is the 5th most common cancer in the UK and is treated by removing the cancer with an operation. Early spread can be detected through sampling of nearby lymph nodes - sentinel lymph node biopsy (SLNB). SLNB is readily available for patients with melanoma of the torso and limbs, but not everywhere offers SLNB for head and neck melanoma, for a variety of reasons. However, this may result in some patients not having access to new, systemic treatments that prolong life.

The aim of this study is to examine how practice varies nationally, looking at who is offered SLNB after melanoma of the head or neck, what treatments they go on to have, and whether they recover from their melanoma or not with the aim of identifying whether SLNB should be made available to everyone in this group.

The following is a summary of the aims of the research project provided by HUTH:
• Overall aim: To evaluate the burden of disease of head and neck melanoma in the last 5 years, including additional treatments and access to SLNB.
• Primary outcomes:
- What proportion of patients diagnosed with cutaneous melanoma of the head and neck go on to have SLNB?
- How does this compare to the proportion of patients with cutaneous melanoma in other parts of the body that go on to have SLNB?
• Secondary outcomes
- What is the tumour stage of patients with cutaneous melanoma of the head and neck who have SLNB compared to those who do not?
- Do a higher proportion of patients with cutaneous melanoma of the head and neck who have had an SLNB have systemic anti-cancer therapies (or other treatments) for comparable T stage?
- Is there a variation in provision of SLNB for cutaneous melanoma of the head and neck across England?

The following NHS England Data will be accessed:
• (NDRS) Cancer Consolidated Dataset (Package 10 & 12).
– necessary as this will allow identification of patients diagnosed within the study period with malignant melanoma of the head and neck and which of those would be eligible, if available, for SLNB and allow the 1- and 3-year mortality for the relevant members of the study cohort.

The level of the Data will be:
• Pseudonymised

The Data will be minimised as follows:
• Limited to a study cohort identified by NHS England as meeting the following criteria: cancer patients over 18 years of age.
• Limited to data between 01/01/2016 and 31/12/2022.
• Limited to conditions relevant to the study identified by specific ICD codes.
• Limited to the geographic area of England.

HUTH is the controller who also processes the data as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.

The lawful basis for processing personal data under the UK GDPR is:
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 lawful basis for processing special category data under the UK GDPR is:
Article 9(2)(j) - processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.
This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research, which protects and promotes the interests of patients, service users and the public, and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.

The funding is provided by the British Association of Plastic, Reconstructive and Aesthetic Surgeons (BAPRAS) through a grant. The funder will have no ability to suppress or otherwise limit the publication of findings.

The Principal Investigator for this study is substantively employed by the University of Hull. This individual holds an honorary contract with Hull University Teaching Hospitals NHS Trust (the Controller). The University of Hull will have no access to the data, nor have any influence on the study, its results, or the dissemination of the results thereof.

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 section 251(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

Expected Benefits:

The findings of this research study are expected to contribute to evidence-based decision-making for policy-makers, local decision-makers such as doctors, and patients to inform best practice to improve the care, treatment and experience of health care users relevant to the subject matter of the study.

The use of the data could:
• Help the system to better understand the health and care needs of populations.
• Lead to the identification or improvement of treatments or interventions, or health and care system design to improve health and care outcomes or experience.
• Advance understanding of regional and national trends in health and social care needs.
• Inform planning health services and programmes, for example to improve equity of access, experience and outcomes.
• Inform decisions on how to effectively allocate and evaluate funding according to health needs.
• Provide a mechanism for checking the quality of care. This could include identifying areas of good practice to learn from, or areas of poorer practice which need to be addressed.
• Support knowledge creation or exploratory research (and the innovations and developments that might result from that exploratory work).

The dissemination plan above has a number of potential benefits. If the results show a geographical discrepancy in the provision of staging surgery for this common cancer, then the study will be in a strong position to advocate for clearer guidelines on the matter. At present, national guidelines allow for local variation in head and neck SLNB due to concern over greater operative difficulty and greater variability in lymphatic spread in this area. If the study is able to show through it's dataset that outcomes are improved for those who undergo SLNB, then it can advocated for wider access to this procedure for patients. This will be beneficial in both the provisioning of healthcare and is in the public interest (as the public stands to gain from wider access and improved outcomes).

The dissemination plan has been designed to maximise exposure for our results. By disseminating to melanoma-specific charities, as well as the scientific community, our results may be leveraged by charitable stakeholders as well as clinicians in the production of new/updated guidelines.

Displaying or disproving a benefit from access to SLNB, or showing geographical variance, stands to benefit large numbers of patients. Over 2,500 new cases of melanoma of the head and neck are diagnosed every year and though not all of these would be suitable for SLNB, hundreds of patients may be eligible for this staging procedure. Adequate staging may qualify them for further treatment and in turn confer a survival benefit. Furthermore, strong evidence for a geographical variance can be used to standardise procedures available to patients on the NHS (through training or service development in individual NHS trusts). In order to measure any benefit from the outcomes of this work, further prospective research may be needed.

It is hoped that through publication of findings in appropriate media, the findings of this research will add to the body of evidence that is considered by the bodies, organisations and individual care practitioners charged with making policy decisions for or within the NHS or treatment decisions in relation to specific patients.

Outputs:

The expected outputs of the processing will be:
• Annual reports of findings to the funder (BAPRAS).
• Submissions to open access peer reviewed journals.
• Presentations at appropriate national/international conferences.
• Reports to public bodies/stakeholders focussed on melanoma, or skin cancer as a whole.

The outputs will not contain NHS England Data and will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset(s) from which the information was derived.

The outputs will be communicated to relevant recipients through the following dissemination channels:
• Journals (such as British Journal of Dermatology, JAMA Dermatology)
• Conferences (such as BAPRAS annual conference, British Association of Dermatologists annual conference, the Society for Melanoma Research conference)
• Reports aimed at public bodies/stakeholders (such as Melanoma UK [Charity], Melanoma Focus [Charity], The Skin Cancer Foundation [Charity], NICE [Guideline producer], SIGN [Guideline Producer], British Association of Dermatologists [Professional Body], BAPRAS [Professional Body])

It is anticipated that results will be available for presentation at conferences by Q3 2025. It is also anticipated that results will be submitted for publication in peer reviewed journals by Q4 2025, and once accepted, reports will be prepared for the organisations outlined above.

Processing:

No data will flow to NHS England for the purposes of this Data Sharing Agreement (DSA).

NHS England will grant access to the Data via the Secure Data Environment (SDE). The SDE is a secure data and research analysis platform. It allows approved researchers with approved projects access to pseudonymised data and industry-leading analytics tools

NHS England will provide access to the relevant records Package 10 & 12 of the NDRS Cancer Consolidated Dataset to HUTH via the NHS England Secure Data Environment (SDE).

The Data will contain no direct identifying data items. The Data will be pseudonymised and individuals cannot be reidentified through linkage with other data in the possession of the recipient.

SDE users can request exportation of aggregated analysis results (suppressed and summarised according to the NHSE SDE Disclosure Control rules) subject to review and approval by the NHS England SDE Output Checking team. The SDE Output Checking team will ensure that no output contains information which could be used either on its own or in conjunction with other data to breach an individual's privacy.

Users must identify themselves via a multi-factor authentication mechanism and are only able to access the datasets detailed within this DSA. The access and use of the system is fully auditable, and all users must comply with the use of the Data as specified in this DSA.

Users are only authorised to access the Data specified in this DSA and can utilise a variety of analytical tools available within the SDE platform. Users are not permitted to export record-level data from the SDE.

The Data will be accessed by authorised personnel via remote access.

The Controller(s) must confirm and provide evidence upon audit by NHS England that access via any remote device complies with the data security obligations within this DSA and the Data Sharing Framework Contract.

For remote access:
• Remote access will only be from secure locations situated within the territory of use (as further restricted elsewhere within the DSA if so done) stated within this DSA;
• Access controls granting users the minimum level of access required are in place;
• Remote access is only via secure connections (e.g., VPNs or secure protocols) to protect data;
• Multifactor authentication (MFA) is required for remote access;
• Device security, including up-to-date software and operating systems, antivirus software, and enabled firewalls are utilised for the remote access;
• All remote access is undertaken within the scope of the organisation’s DSPT (or other security arrangements as per this DSA) and complies with the organisation’s remote access policy.
The above applies in addition to any condition set out elsewhere within the DSA (e.g. who may carry out processing, and for what purpose).

Remote processing will be from secure locations within England/Wales. The Data will not leave England/Wales at any time.

Access is restricted to employees of HUTH who have authorisation from the Principal Investigator and an individual working under honorary contract. All personnel accessing the Data have been appropriately trained in data protection and confidentiality. The Data will not be linked with any other data.

There will be no requirement and no attempt to reidentify individuals when using the Data.
Analysts/researchers from HUTH will process/analyse the Data for the purposes described above.

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