NHS Digital Data Release Register - reformatted

Cambridge University Hospitals NHS Foundation Trust projects

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

Melanoma PREDICT Tool - Individualising prognostic stratification in melanoma ODR1920_246 — DARS-NIC-681965-Y4H5P

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: Non-Sensitive

When:DSA runs 2023-08-23 — 2026-08-22

Access method: One-Off

Data-controller type: CAMBRIDGE UNIVERSITY HOSPITALS NHS FOUNDATION TRUST

Sublicensing allowed: No

Datasets:

  1. NDRS Cancer Registrations
  2. NDRS Linked DIDs
  3. NDRS Linked HES APC
  4. NDRS Linked HES Outpatient
  5. NDRS National Radiotherapy Dataset (RTDS)
  6. NDRS Somatic Molecular Dataset
  7. NDRS Systemic Anti-Cancer Therapy Dataset (SACT)

Objectives:

Cambridge University Hospitals NHS Foundation Trust (CUHFT) aim to gain access to data on patients who have been diagnosed with cutaneous melanoma between 1995 and 2018. The purpose of this project is to create an online, interactive tool ‘PREDICT Melanoma tool’. The tool is designed for patients diagnosed with cutaneous melanoma and the healthcare professionals providing their care. This tool will provide predictions, based on a national population level dataset, to assist in making decisions about further investigations and treatments that are advised by current clinical guidelines currently available based on the specifics of that individual and the type of melanoma. The tool will present this information in a format that is easy to understand and will be interactive, such that patients will be able to toggle various treatment options to see how this affects their ongoing outcome. Similar tools already exist for both breast and prostate cancer and are used widely. The use of these tools has prompted further updates to increase the amount of information and detail available through them.

The work for the Breast and Prostate tools has been performed at University of Cambridge since 2007. The lead investigator has been integral in the creation of the PREDICT Breast and Prostate tools which have been endorsed by the American Joint Committee on Cancer (AJCC) and National Institute for Health and Care Excellence (NICE). CUHFT have received funding from the Addenbrooke’s Charitable Trust and the Royal College of Surgeons to create a similar tool for those patients diagnosed with cutaneous melanoma.

The data being requested under this agreement is solely to be used for the purposes of creating the PREDICT Melanoma tool. As with the previous PREDICT tools, as more patient, research and specific clinical trial data becomes available it will be prudent for CUHFT to run further analysis to ensure that the model is as accurate and relevant as possible. It is necessary that the study team have specific information about the various known risk factors for melanoma which include:

• Age
• Sex
• Ethnicity
• Body site/area – affected by melanoma
• Date of diagnosis
• Date of surgery
• Histological information about tumour
o Excision margin
o Lesion thickness
o TNM stage
o Presence or absence of ulceration
o Presence of absence of lymphovascular invasion
• Lymph node status
• Presence of absence of regional or metastatic disease
• Co-morbidities
• Primary treatment received
o Any subsequent treatments

To support this work the CUHFT requests pseudonymised sub-sets of the following datasets:

• NDRS Linked Hospital Episode Statistics (HES)- inclusive of the Admitted Patient Care and Outpatient subsets
• NDRS Linked Diagnostic Imaging Data Set (DIDS)
• NDRS Linked Cancer Registry
• NDRS Linked Radiotherapy Dataset (RTDS)
• NDRS Linked Systemic Anti-Cancer Therapy Dataset (SACT)
• NDRS Linked Somatic Molecular Tables

The quantum of data requested is the minimum necessary and could not be further reduced without impacting the ability to achieve the stated aims. The justification for the data products requested is that the study team need to acquire data which will provide a detailed patient level picture of the overall pathway of melanoma care. This includes admitted patient care which refers to surgical care and recurrence picked up through imaging or outpatient assessment. All these factors impact overall survival outcomes. All the above datasets will aid in determining to what extent each known risk factor contributes to the patient outcome and will undergo a statistical analysis. This initial analysis will allow CUHFT to generate a mathematical model which the study team can use to predict the outcome of new patients, based on their individual characteristics and the characteristics of their melanoma.

Once this initial model has been generated, CUHFT intend to test it on a further set of patient data from this request to check that its predictions are accurate and valid. Any necessary adjustments will be made to the model to increase its accuracy and further testing will take place. When the model has been demonstrated to be accurate and valid it will serve as the backend of an online and interactive tool that will be available to the public and clinicians. Here individuals will be able to enter information about themselves and their melanoma. This information will be run through the model and its predictions presented in an easy to understand and interactive manner. This information can be used in conjunction with advice and assistance from clinicians to make decisions about further investigations and treatment. Should further data from NDRS be required for this purpose then a separate application would be made at that point as was deemed necessary. Further changes to this model in the future may require updated data which would necessitate another application.

To address the UK General Data Protection Regulation (GDPR) Principal of Data Minimisation CUHFT intend to be specific in requesting data only on patients with the appropriate diagnosis and in a time period where CUHFT believe the information available will be informative to the project and hence to future patients and the clinicians involved in their care. The data requested is limited to individuals aged over 18 with a diagnosis of Cutaneous Melanoma for the period from 1995 – 2018.

CUHFT require record level, Pseudonymised data for as UK wide an area as is possible. Pseudonymised record level data is necessary to provide the study team with as many data points for each risk factor and outcome measure as possible. This will enable the PREDICT model to be more accurate, providing better predictions to those who use the tool in the future. The study team have chosen the period 1995-2018 because they believe that this represents a period in which many current practices for treating melanoma were in use, necessary data points will have been recorded and outcomes will be applicable to those who may use the prediction tool in the coming years. A population level dataset will provide the greatest degree of detail for predicting the outcomes of future patients in the United Kingdom. It will also allow the study team to make the tool accurate across the areas that the NHS operates.

The study team are conducting an analysis of risk factors and how these appear to influence patient outcomes. As such, CUHFT are only requesting data items which the study team believe is necessary to create an effective prediction tool. No patient identifiable information will be requested in this application. The identity of the individual patient is not relevant to the study and the study protocol does not require such information to provide the suggested outcomes. Data is not being requested that would make it easy to identify individual patients, and there is no requirement to reidentify individuals and there will be no attempt to do so.

There are no alternative or less intrusive ways of creating a prediction tool of this type with specific detail suggested by the project aims. The study are taking the appropriate steps to obtain HRA approval.

The lawful basis for processing falls under the following UK General Data Protection (GDPR) Articles:

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. Justification being on the basis that tools have been created for such purposes for patients with other types of cancer (breast: https://breast.predict.nhs.uk/ and prostate: https://prostate.predict.nhs.uk/). Both projects have utilised population level data sets to conduct appropriate data analysis in production of these tools. They are widely used and provide valuable information both to patients and clinicians alike. CUHFT tool, like the current predict breast and prostate tools, will provide a valuable resource for individual patients to learn about their disease and gain insight into their individual risk factors and prognostic information. This will assist in the decision-making process for further investigations and treatments and will present information to patients in a way that is both interactive and easy to understand.

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 project is deemed to be in the public interest because as approximately 16,000 people are diagnosed with cutaneous melanoma in the UK each year and this number is rising. The online tool that this project aims to create will be of benefit to every one of them. They will have more information about their condition and the expected outcomes of it and some treatments that will be offered to them. This will enable patients to make more informed decisions about which treatments and investigations they wish to undergo and what the likely impact of these will be on them and the chance of effectively treating the melanoma they have been diagnosed with.

Cambridge University Hospital NHS Foundation Trust is the sole data controller and sole data processor for this project. Data will be processed only for the purposes outlined within this Agreement. Addenbrooke’s Charitable Trust (ACT) and Royal College of Surgeons of England both are providing financial support for the project. Neither will be involved in the storing or processing of the data requested under this application and neither have contributed to the purpose or scope of this project.

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

Expected Benefits:

The Melanoma PREDICT tool, once made available will be the first individual patient prognostic tool created for melanoma based on a population level dataset from the UK. Currently there isn’t an accessible and reliable way of predicting individual patient outcomes based on their specific pathological factors for a UK cohort of patients. The Melanoma PREDICT tool will inform the patient in their specific cancer and given condition what the likely outcomes will be, with the recommended treatment options. Once the initial statistical models are developed, the study team will be engaging with PPI partnerships to develop the interactive tools that patients may be using in the future. Some of this background work has already been done as part of PREDICT Breast and PREDICT prostate tools. Online, it is hoped to be regularly used by clinicians and patients as part of the decision-making regarding investigation and treatment of melanoma. Benefits include:

• The ability to estimate melanoma specific survival.
• To provide a valuable resource for individual patients to learn about their disease and gain insight into their individual risk factors and prognostic information.
• Assist in the decision-making process for further investigations and treatments
• Will present information to patients in a way that is both interactive and easy to understand.

The melanoma PREDICT tool is hoped to be a powerful aid for these patients to recognise their individual risk factors and prognostication. Additionally, patients often find it hard to comprehend the volume of information dispensed at a time of cancer diagnoses. It is hoped this tool will allow patients to visualise prospective care pathways and choices which are individualised in a way which is palatable, interactive, and easy to understand.

In 2020, the PREDICT breast tool was accessed by 30,000 users per month and used by well over 100,000 women in the UK, of whom at least 18,500 have been able to make better decisions about whether adjuvant chemotherapy is right for them. Since 2019 the Prostate PREDICT tool was visited by 40,000 users from over 110 countries. Approximately 16,000 people are diagnosed with cutaneous melanoma in the UK each year and this number is rising. It is hoped that the Melanoma PREDICT tool will follow a similar trajectory as the Prostate and Breast tools in helping patients and clinicians make better informed decisions about which treatment options will provide the greatest chance of extending or increasing quality of life.

Outputs:

The initial expected outputs of the processing will be to create a mathematical model to enable outcome predictions given for specific patient and melanoma factors. It is anticipated that the model will be available by October 2023. This tool is expected to be utilised as a resource for health research and in ultimately in routine clinical practice. A study to assess utilisation will be planned once the initial tool is available. The study team would expect that such a utilisation study could be performed and have preliminary results by October 2024. Between 2024-2025 further external validation work of the mathematical model will be undertaken using external datasets and cohorts to test the model validity. Once the tool is validated and safety implications approved it can be presented as a tool to be used in the public domain.

Although NHS England Data isn’t used as an ‘output’, the data will be aggregated in the sense that the model is using those data points aggregated across a core. For example, for a patient aged 50 – the mathematical model will aggregate all patients aged 50 to predict the prognostic likelihood of that individual with their type of melanoma. No individual data point is used, it is aggregated datapoints only.

The mathematical model will be trained on the data disseminated under this agreement to make a prediction on the patient outcome trajectory. None of the data provided to CUHFT will be provided as an ‘output’. Once the mathematical model is completed and validated it will not be linking back to the dataset. It will be a standalone algorithm. It is hoped the Melanoma PREDICT tool can be implemented from 2025 once externally validated and endorsement from clinically approved national organisations like NICE is complete.

The tool, once made available online is aimed to be regularly used by clinicians and patients as part of the decision making regarding investigation and treatment of melanoma. It is also expected that it will be used in skin cancer multidisciplinary team meetings to enable prediction of outcomes for patients on a nationwide scale. The PREDICT website is widely known amongst clinicians and is already endorsed by NICE and The American Joint Committee on Cancer (AJCC). This creates precedence for this and would be a key future goal for this project.

From 2025 CUHFT aim to present ‘The Melanoma PREDICT tool’ at two of the biggest national conferences which deal with melanoma care in the UK. Specifically, The British Association of Dermatology (BAD) and the British Association of Plastic Reconstructive Surgery (BAPRAS). Publications will arise from the conferences for wider dissemination amongst the clinical teams. These include journals such as the British Journal of Cancer (BJC). Additionally, public awareness will also be sought through charities like Melanoma UK.

Processing:

There is no flow of data into NHS England to support this request.

NHS England will flow pseudonymised sub-sets of the following datasets to the CUHFT via a secure file transfer system (SEFT)

• NDRS Linked Hospital Episode Statistics (HES)- inclusive of the Admitted Patient Care and Outpatient subsets
• NDRS Linked Diagnostic Imaging Data Set (DIDS)
• NDRS Linked Cancer Registry
• NDRS Linked Radiotherapy Dataset (RTDS)
• NDRS Linked Systemic Anti-Cancer Therapy Dataset (SACT)
• NDRS Linked Somatic Molecular Tables

There will be no subsequent flows of data and data will not be linked to datasets not already referenced within the agreement. Linkage will only be undertaken by the data custodian NHS England.

The study team will conduct an analysis of patient risk factors and how these appear to influence patient outcomes in cutaneous Melanoma. The disseminated data will be divided into two main groups. The data will be divided at random. A piece of software will pull out a random cohort of a certain size. One of these groups will serve to provide data for analysis and to determine the mathematical model to be used for prediction. The second group will be utilised as the validation group. This group will serve as a test data set for the model, to confirm the accuracy and validity of its predictions. Ultimately the analysis will allow CUHFT to generate a mathematical model which can be used to predict the outcome of new patients, based on their individual characteristics and the characteristics of their melanoma.

All those processing the data disseminated under this Agreement will be substantive employees of the CUHFT or undertake duties via an honorary contract. The PI of this project is employed by the University of Cambridge but working under an honorary contract with CUHFT for the purposes of the project. Those accessing data will receive appropriate data protection and confidentiality training. CUHFT will act as sole processor at all stages of the project. Data will be processed by project team members only, and only for the purposes outlined within this DSA.

Data will be downloaded directly to the Cambridge University Hospital NHS Foundation Trust (CUHFT) secure servers and will remain stored there for the duration of the study. Analysts will conduct all data processing including the statistical analyses to fulfil the project aims in this secure environment. Accessed to the secure system will be via direct login to the CUHFT computer system and remotely via a secure VPN system. This agreement prohibits downloading or copying data to local devices. All IT infrastructure is maintained by the CUHFT IT service team. The project team members undertake annual governance/ data protection training from CUHFT and will comply with the Data Protection Act and the CUHFT’s information Security and Data Protection Policies. Data backups are not held in the cloud as standard and utilise Trust storage solutions.

Prophylaxis for patients at risk of COVID-19 infection (PROTECT-V) — DARS-NIC-383356-N8J6Z

Type of data: information not disclosed for TRE projects

Opt outs honoured: Anonymised - ICO Code Compliant (Consent (Reasonable Expectation))

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

Purposes: Yes (NHS Trust)

Sensitive: Sensitive

When:DSA runs 2023-03-30 — 2026-03-29

Access method: One-Off

Data-controller type: CAMBRIDGE UNIVERSITY HOSPITALS NHS FOUNDATION TRUST, UNIVERSITY OF CAMBRIDGE

Sublicensing allowed: No

Datasets:

  1. Civil Registrations of Death

Objectives:

OVERVIEW OF THE TRIAL
COVID-19 is an international health emergency which continues to impact upon the lives of all individuals in the UK. There is an ongoing need for medicines that can prevent or treat the infection. There are several clinical trials of treatments for COVID-19 underway in the UK, but many of these exclude patients with complex medical conditions or advanced kidney disease. Reasons for exclusion include the presence of existing illness or risk factors, which may make it difficult to interpret results, concerns about potential interactions of a trial drug with usual essential medications, and difficulties finding the correct dose of a trial drug, if it is removed by the kidneys. However, because kidney disease and/or many of the medications used to treat patients with kidney diseases or vasculitis affect the function of the immune system, these individuals may be at increased risk of contracting or becoming very ill from COVID-19. The study team only have limited information available about how COVID-19 affects immunosuppressed patients and patients on dialysis, but they suspect that if an infection occurs, symptoms are more commonly seen than in the general public and therefore, it is important to test medications that may prevent COVID-19 infection specifically in these groups of patients. The PROTECT-V trial enrols patients on dialysis or receiving immunosuppressive medications. The focus of this trial is on prevention of disease, rather than treatment once disease occurs. This will be measured by comparing if COVID-19 develops in people who take the trial treatment against those who receive a placebo (“dummy”) treatment.

The drug tested in the PROTECT-V trial is niclosamide, a common safe drug that has been used in tablet form for tapeworm infections for decades. Preliminary research has shown it may also help protect against COVID-19 infection. However, niclosamide tablets are poorly absorbed from the gut into the bloodstream. Therefore, in the PROTECT-V trial, niclosamide was administered by a nasal spray directly to the lining of the nose, which is where the virus that causes COVID-19 infection usually first takes hold. This formulation of the drug is unlicensed, but has been shown to be safe and well tolerated in a study of healthy volunteers. In the PROTECT-V trial, participants received a total daily dose of 5.6mg of niclosamide administered through a nasal spray (one spray for each nostril) twice daily. This is a much lower dose than the single 2000mg tablet which is taken for tapeworm infections.


ORGANISATIONAL ROLES
Cambridge University Hospitals NHS Foundation Trust (CUH) and The University of Cambridge (UoC) are the Sponsors for this clinical trial based in the UK. In the context of a clinical trial, “Sponsor” means the organisation(s) ultimately responsible for the conduct of the trial and thus under this agreement Cambridge University Hospitals NHS Foundation Trust and The University of Cambridge are Data Controllers. Both organisations are also Data Processors as they have substantively employed individuals involved in the PROTECT-V Trial Team. There is an overarching Research Governance Framework agreement between CUH and UoC regarding their responsibilities related to the research.

FUNDING
PROTECT-V is funded by LifeArc (a UK life science medical research charity), Addenbrookes Charitable Trust (ACT), Kidney Research UK, and UNION Therapeutics (a Denmark-based pharmaceutical company). UNION Therapeutics will also provide packaged study medication (Niclosamide) for the study. None of these funders will have access to record-level NHS England data. None of the funders determines what data is collected, how this data is collected and for which purpose in this trial, and are therefore not considered Data Controllers in this agreement.

PATIENT AND PUBLIC INVOLVEMENT AND ENGAGEMENT (PPIE)
Prior to its start, the study was submitted and received feedback from the Cambridge University Hospitals NHS Foundation Trust Patient and Public Involvement (PPI) panel, which is coordinated by the NIHR Cambridge BRC PPI team. The trial was viewed positively and panel comments and suggestions were worked into the resulting consent materials.

COHORT
The PROTECT-V study has multiple arms of study, investigating Niclosamide, Ciclesonide, and Sotrovimab. This application refers only to the Niclosamide arm of the study. The first participant was recruited on the 19-Feb-2021 and the last UK participant recruited was the 22-Nov-2022. The trial closed to recruitment on the 28-Nov-2022. All participants have consented to follow-up data from NHS England for up to 9 months following the Date of Randomisation.

DATA REQUESTED
The University of Cambridge and Cambridge University Hospitals NHS Foundation Trust to obtain a one-off drop of record-level data linked to a cohort of approximately 1,200 individuals for Civil Registrations (Deaths) data. Data fields have been limited to only those required to undertake a sensitive analysis of the data for Primary Outcomes measures.
Civil Registrations (Deaths) data is required for the study to undertake calculation for time to COVID-19 infection by 9 months. For participants without COVID-19 infection by 9 months, they will be censored at the date of death (if the date of death is less than 9 months) or at 9 months (no death).
The primary outcome is the time to COVID-19 infection. If patients pass away before having gotten to the end of the time in the trial, their data will be censored at the time of death. Otherwise the study would have to assume the individual got to 9 months without infection (which they would not have - for example, a patient could pass away after 3 months, so their timeframe without COVID-19 would actually be 90 days as opposed to 270 days). Without the death data to calculate this timeframe the study would be introducing a significant bias to the time to COVID-19 infection analysis (the primary outcome for the trial) and this would significantly affect the reliability of the trial results.

COMMON LAW
NHS England have satisfied themselves that the consent materials supplied to participants are compatible with consent (reasonable expectations) and that this request is appropriate, necessary and proportionate for the performance of the task described in the Purpose statement and that there is no other reasonable means for the data processor to achieve their purpose that is less intrusive to the data subjects.

COMMERCIAL PURPOSE
In the interests of Transparency, the study acknowledge that whilst the aim of PROTECT-V is to investigate the effectiveness of specific treatments (for the purposes of this agreement, Niclosamide) on patients on dialysis or receiving immunosuppressive medications in preventing the spread of COVID-19 - if proven safe and effective - UNION Therapeutics will benefit from the potential increased use of Niclosamide.

This an investigator initiated trial with a purpose of investigating the effectiveness of specific treatments on patients on dialysis or receiving immunosuppressive medications in preventing the spread of COVID-19. UNION Therapeutics will also provide packaged study medication (Niclosamide) for the study. If the results show that Niclosamide is safe and effective this will be used to evidence their licencing application of Niclosamide and therefore could also increase revenue for the manufacturer and funder UNION Therapeutics (as the manufacturer of the drug). However this is not the primary purpose of PROTECT-V.

Whilst not directly relevant to this data sharing agreement, it is noted here in the interests of transparency that GSK PLC are funding a separate drug arm of the overarching PROTECT-V study for a drug called Sotrovimab. Should Sotrovimab be shown to be effective, data from that arm of the trial may be used to support GSK’s licencing application for Sotrovimab as a prophylactic agent and therefore could also increase revenue for GSK PLC (as the manufacturer of the drug).

The data received by Cambridge University Hospitals NHS Foundation Trust (CUH) and The University of Cambridge (UoC) will not be used for any purpose other than to meet objectives as stated in this Data Sharing Agreement and will not be shared with any other third party, funder or organisation not already stated within this agreement unless first aggregated with small number suppression applied as per the relevant data set disclosure rules.

Expected Benefits:

If demonstrated, use of Niclosamide acting as a prophylactic treatment of COVID-19 would significantly benefit those immunocompromised individuals that have been shown to have a poor vaccine response. There are no drugs proven to prevent COVID-19 or to reduce the severity of illness if given as prophylaxis. Although vaccines are now available, there remains a need for other prophylactic agents, particularly in immunocompromised individuals for whom vaccine responses may be suboptimal.

The results of the trial aim to be provided to the Department of Health and Social Care (DHSC). It is hoped that, using the results of this trial, they will be able to influence policy guidelines and other guidelines regarding the clinical practice within the patient population. 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.

It is hoped that through publication of findings in appropriate media (publications, conferences and other media communication i.e. PR releases, news interviews etc.), 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. As the trial has closed to recruitment the final data analysis aims to be completed this year (2023) and could have significant impact on clinical practice.

One of the trial funders is the Kidney Research UK (KRUK). The KRUK will be provided results of the trial and will directly support the patients in these immunocompromised populations.

Outputs:

On completion of the Niclosamide intervention of the trial, the data will be analysed and tabulated and an Intervention Specific Final Trial Report prepared. The results of this trial may be published or presented at scientific meetings. Any reported serious breaches will be detailed in all publications in line with regulatory requirements.

Authorship of final trial outputs will be assigned and funding acknowledged in accordance with guidelines set out by the International Committee of Medical Journal Editors.

As the trial has closed to recruitment and the last patient last visit will be in January 2023, submissions to peer reviewed journals and conference submissions are planned for 2023. As well as reporting the results directly to the Department of Health and Social Care (DHSC) to support policy making decisions.

All data will be reported in aggregated form with small number suppression applied as per the HES analysis guide.

Results hope to be made available through publications and peer-reviewed medical journals and used for medical presentations and conferences. They also hope to be published on the various trial registry websites.

Results will also be made available to Kidney Research UK, who will help distribute the information through their usual network of professionals and patients.

Processing:

Under this agreement, one extract of Civil Registrations (Deaths) data linked to the PROTECT-V cohort of participants (approximately 1,200 individuals) will be requested from NHS England. The cohort will include all participants from the Niclosamide arm of the PROTECT-V study, randomised in England/Wales.

The cohort identifiers for linkage purposes will include PROTECT-V study number (Pseudonymised Study ID), NHS Number, First name, Surname, Date of Birth, Post Code and Date of Randomisation. The Civil Registrations (Deaths) dataset will be minimised by the NHS England Data Production team using the Date of Randomisation to produce an extract of data for those cohort members who have sadly died within 9 months of the Date of Randomisation.

After receipt of the cohort details and respective linkage, NHS England will provide a linked record-level extract of the Civil Registrations (Deaths) dataset on a one-off basis as soon as possible.

The data file will be transferred via NHS England's Secure Electronic File Transfer System (SEFT).

All data processing will be carried out by authorised members of the PROTECT-V study team who are substantive employees of the University of Cambridge and/or Cambridge University Hospitals NHS Foundation Trust. All investigators and trial site staff involved in this trial must comply with the requirements of the Data Protection Act 2018 and Trust Policy with regards to the collection, storage, processing and disclosure of personal information, have received appropriate training in data protection and confidentiality.

Personal identifiable data (PID) will be stored separately from pseudonymised study data on a secure server hosted within University of Cambridge School of Clinical Medicine Secure Data Hosting Service. PID will be accessible to the PROTECT-V trial team within the Cambridge Clinical Trials Unit, monitors, auditors and inspectors as required. It is necessary to:
1) perform validation of NHS Numbers and linkage to routinely collected datasets, and
2) to generate datasets with participant details for mail merge creation of questionnaires, and is therefore imperative to the conduct of the study.

Statistical data analysis will be carried out via devices owned and managed by the University of Cambridge by connecting to internal networks where the data are stored securely, either directly in person or remotely inline with NHS England's remote access policy, using an appropriate statistical package. To remotely access the devices requires a secure 2-factor authenticator (VPN) and users are then able to securely access the secure server on the University’s IT framework. All data analysis will be conducted within the confines of the University’s secure server, and will not be downloaded to remote devices for storage or processing.

By signing the Data Sharing Agreement, all organisations party to this agreement must comply with the Data Sharing Framework Contract, including requirements on 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).

The Data Controller should ensure appropriate data processing agreements are in place with all data processors contracted to undertaking work referenced within this agreement.

Upon instruction from NHS England, a Certificate of Data Destruction must be completed by the Data Controller confirming the data has been appropriately disposed of following use.

The data received by Cambridge University Hospitals NHS Foundation Trust (CUH) and The University of Cambridge (UoC) will not be used for any purpose other than to meet objectives as stated in this Data Sharing Agreement and will not be shared with any other third party, funder or organisation not already stated within this agreement unless first aggregated with small number suppression applied as per the relevant data set disclosure rules.

MR1474 - UK-PBC Project - cohort datasets — DARS-NIC-360208-K1T4F

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - consent provided by participants of research study, Identifiable, No (Consent (Reasonable Expectation))

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

Purposes: No (NHS Trust)

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

When:DSA runs 2019-05-20 — 2022-05-22 2019.07 — 2023.10.

Access method: One-Off, Ongoing

Data-controller type: CAMBRIDGE UNIVERSITY HOSPITALS NHS FOUNDATION TRUST, UNIVERSITY OF CAMBRIDGE

Sublicensing allowed: No

Datasets:

  1. MRIS - List Cleaning Report
  2. MRIS - Flagging Current Status Report
  3. MRIS - Cause of Death Report
  4. Hospital Episode Statistics Outpatients
  5. Hospital Episode Statistics Accident and Emergency
  6. Hospital Episode Statistics Admitted Patient Care
  7. Bridge file: Hospital Episode Statistics to Diagnostic Imaging Dataset
  8. Diagnostic Imaging Dataset
  9. Diagnostic Imaging Data Set (DID)
  10. Hospital Episode Statistics Accident and Emergency (HES A and E)
  11. Hospital Episode Statistics Admitted Patient Care (HES APC)
  12. Hospital Episode Statistics Outpatients (HES OP)
  13. Civil Registrations of Death
  14. Emergency Care Data Set (ECDS)
  15. Medicines dispensed in Primary Care (NHSBSA data)

Objectives:

Primary biliary cholangitis (PBC, formerly primary biliary cirrhosis) is a rare, chronic liver disease characterised by autoimmune destruction of the small, intrahepatic bile ducts. PBC eventually leads to end-stage liver disease in a substantial proportion of cases. The disease affects up to 20,000 people in the UK, where it remains a leading indication for liver transplantation (LT).

UK-PBC is a UK-wide precision medicine initiative that is aimed at improving understanding of PBC and developing precision medicine for PBC. It was established with a Stratified Medicine Award from the Medical Research Council (MRC). UK-PBC is led by Newcastle University, Imperial College, London, the University of Birmingham and the University of Cambridge. More than 150 NHS Trusts or Health Boards across the UK are collaborating in the project. UK-PBC is divided into 3 Work Strands (WS):
WS1 is led from the Academic Department of Medical Genetics at the University of Cambridge. The focus of WS1 is to recruit and characterise a large, prospective PBC cohort (the UK-PBC Research Cohort). This involves the collection of detailed clinical information including results of medical investigations, important clinical events and life events (date of death, cause of death and date of birth) from participants and collaborating centres. These data are stored in the UK-PBC Database, located on an NHS server behind the N3 firewall at Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust (CUH); they are used for statistical modelling of disease. For avoidance of doubt, data from NHS Digital will never be uploaded into the UK-PBC Database; these data will be stored separately on NHS CUH servers behind N3 firewall for the agreed duration; they will be used to identify discrepancies or missing data in the clinical data already collected by UK-PBC for the purpose of statistical modelling of disease.
WS2 of UK-PBC is focused on the mechanistic basis of PBC. This work is informed by the statistical models of disease derived by WS1. For the avoidance of doubt, WS2 has NO access to clinical data collected and curated by WS1. WS2 will have NO access to NHS Digital data.
WS3 is focussed on clinical trial design and patient education. Clinical trial design by WS3 is informed by the statistical models of disease derived by WS1. For the avoidance of doubt, WS3 has NO access to clinical data collected and curated by WS1. There will be NO sharing of NHS Digital data with WS3. This data sharing agreement (DSA) is with the University of Cambridge, which is the Joint Data Controller for WS1 of UK-PBC, together with CUHFT. As stated above, there will be NO linkage of NHS Digital data to other UK-PBC datasets, and there will be NO sharing of NHS Digital data to any other work strands in UK-PBC.

WS1 of UK-PBC is now funded via Immune-Mediated Inflammatory Disease Biobanks – UK (IMIDBio-UK). IMIDBio-UK is a multi-centre collaboration between academia, the NHS, industry and patient groups that is aimed at cross-disease meta-analysis of existing and future research datasets across diverse autoimmune and auto-inflammatory disease to identify shared and unique mechanisms for autoimmunity. IMIDBio-UK is funded by the MRC. The lead research organisation is the University of Glasgow. Cambridge, representing WS1 of UK-PBC, is a named collaborator in IMIDBio-UK, one of five key academic partners who will receive funding under the IMIDBio-UK MRC Award (ref MR/R014191/1).
For avoidance of doubt:
1) IMID-Bio UK does not itself generate research data. The aim of IMIDBio-UK is to provide a platform for the sharing of research data generated by fully- independent, ethically approved research projects, each having its own ethics approval and study documentation (e.g. participant information sheet and informed consent form). There is no requirement for the ethics approvals and study documentation of the respective, independent research projects to be aligned - there is only a requirement for the participants in the respective projects to have consented to the sharing of pseudonymised research data (for the avoidance of doubt, however, it is re-iterated that there is NO onward access to NHS Digital Data from UK-PBC to IMIDBio-UK or any other organisation. There is only sharing of research data generated by UK-PBC [e.g. transcriptional datasets]).
2) The respective research projects generating research data that will be shared with IMIDBio- UK are completely independent of one another. Thus, Cambridge/UK-PBC is not influenced by any of the other academic partners collaborating in IMIDBio-UK. These other academic partners have no say in how UK-PBC-related research data are collected, generated, organised or stored by Cambridge. They have no special access to research data collected and curated by UK-PBC. Likewise, Cambridge/UK-PBC does not influence the research projects of these other academic partners. Furthermore, as stated above, there is NO onward access of NHS Digital Data from UK-PBC to IMIDBio-UK; other academic centres collaborating in IMIDBio-UK, or any other organisation. Thus, because Cambridge/UK-PBC is completely independent of the other academic partners collaborating in IMIDBio-UK and because no onward access of NHS Digital Data is granted to any other organisation, there is no need for a data controller in the other academic centres listed in the funding award letter.
IMIDBio-UK was awarded £1,707,539 by the MRC. Of this, £221,276 was allocated to Cambridge to support UK-PBC-related research activities (see page 1, paragraph 2 of the funding award letter). The remainder (~£1.5M) was divided between the other academic centres to support their respective research activities. As stated above, the respective research projects supported by the IMIDBio-UK award are completely independent of one another. Thus, the research activities undertaken by one academic partner are not influenced by any other academic partners. The implication is that the other academic partners collaborating in IMIDBio-UK have nothing to do with this application. The role of UK-PBC in IMIDBio-UK is to share existing and future research datasets (for example, genetic or transcriptional data) for cross-disease meta-analysis, as described above. To reiterate, however, and for the avoidance of doubt, data derived from NHS Digital will NOT be shared with IMIDBio-UK; there will be NO onward access to data derived from NHS Digital.
3) The applicant apologises for the confusing and similar terms. IMIDBio-UK has two Work Streams, Work Stream 1 and Work Stream 2. Work Stream 1 of IMIDBio-UK is completely separate to Work Strand 1 of UK-PBC.
4) Several Pharmaceutical companies are also named collaborators in IMIDBio-UK. This is because Pharma are interested in finding new indications for existing immune-modulatory agents, as well as the identification of new therapeutic targets in autoimmune conditions. The MRC, which funds IMIDBio-UK, actively encourages collaboration with Industry to ensure that research findings are rapidly translated into clinical practice. For the avoidance of doubt, however, Cambridge (representing WS1 of UK-PBC) does NOT receive funding from the Pharmaceutical companies collaborating in IMIDBio-UK. These companies have no influence on UK-PBC and no special access to research data collected and curated by UK-PBC.
NHS Digital datasets offer important information corresponding to the clinical data already captured from participants and collaborating centres in this study. The important clinical events and investigations to be collected exist within: Hospital Episodes Statistics (HES); Inpatient episodes, Outpatient appointments, Accident and Emergency attendances, Diagnostic Imaging Dataset, Cancer data and Mortality data. These products are listing all the specific variables that would help link PBC medical information with important health events, part of the purpose of the project, as outlined above.

Expected Benefits:

UK-PBC is aimed at developing a precision medicine approach to PBC treatment; delivering the right treatment to the right patient at the right time. This will be achieved by identification of patient groups within the PBC patient cohort who are more likely to respond to one type of therapy or another and to identify groups of patients at the point of diagnosis who are likely to have more aggressive disease and need closer monitoring and surveillance. Identification of patient sub-groups promptly at the point of diagnosis will allow patients at highest risk of progressive liver disease to be promptly treated with second line therapy, or newer agents and/or to be included into medical trials. This will also enable these high-risk groups to be included in closer surveillance and clinical follow-up within the healthcare setting.
The goals:
1) Establishment of the stratified therapy model in PBC. Whereby patients with the greatest need receive the most appropriate treatment at the point of diagnosis.
2) Increase understanding of the mechanism(s) of UDCA (ursodeoxycholic acid, normally present in the bile) non-response through a systematic programme of innovative research.
3) To develop markers to identify patients likely to respond poorly or not at all to treatment, allowing these poor-responders and non-responders to be followed-up closely.

To achieve the goals, complete clinical characterisation of patients using detailed clinical information including medical investigations and important clinical events (including the date and cause of death) is key. Data from NHS Digital will allow the project to verify the existing collected clinical characteristic data for accurate identification of sub groups of patients within the PBC cohort and treatment stratification so that patients with the greatest need are started on the most appropriate treatment and receive the greatest benefit. This stratified treatment approach will lead to improved disease outcomes and a more cost- effective approach to patient care. This will have a major impact in PBC enabling accurate identification of patients with sub-phenotypes, who might then be prioritised for mechanistic studies and clinical trials. Second-line treatment for PBC is expensive, e.g. obeticholic acid (OCA) at a standard dose of 5- 10mg once daily costs ~£80 per day (£29,000 per year). A large prospective cohort that is well-characterised in terms of disease severity, healthcare utilization, symptoms and health utility is essential for accurate health economic modelling and informed health economic opinion. This will benefit patients within the UK with PBC which at present is estimated at 20,000.

Outputs:

The verified clinical characteristic data will be presented in an aggregated format with small numbers suppressed in line with the HES Analysis Guide, within scientific journals dependent on research submission and acceptance for publication. Research will be presented at conferences and meetings such as; British Association for the Study of Liver Disease (BASL) this is an annual conference within the UK for the study of liver disease, European Association for the Study of Liver Disease (EASL) this is an annual conference within Europe for the study of liver disease and American Association for the Study of Liver Disease (AASLD) this is an annual conference within America for the study of liver disease. The findings will also be presented at meetings of the PBC Foundation (the leading PBC support group in the UK), and the newsletters of the PBC Foundation and the UK-PBC project.
The following general principles apply:
- All outputs and publications contain only aggregated data with small numbers suppressed in line with the HES Analysis Guide.
- Within summary tables, numbers will be rounded to the nearest 10 observations.
- All outputs will be checked by the UK-PBC Data Management Committee to ensure that no subject is identifiable from the information presented.
- Individual level data will never be presented or published. Only summary data will be presented or published.
- Data from NHS Digital will be used as part of the work with Work Strand 1 within Cambridge to complete missing data and resolve discrepancies between participant and clinician completed questionnaires.
- List of fields of data already collected have been supplied to NHS Digital. All data requested from NHS Digital is already captured as part of the data capture. The data already captured will be downloaded from the UK-PBC Database on NHS computers behind the N3 firewall.
- Any discrepancies or missing data in the project's data capture will be verified and completed using NHS Digital data.

Outputs: Publication of the research is dependent on the timeline it takes for the data to be sent. The project envisages approximately 16-18 months from the date the data is received; however it is dependent on the data being received.

Processing:

The Academic Department of Medical Genetics at the University of Cambridge, Work strand 1 of UK-PBC, will securely transfer the list of NHS numbers and study IDs to the NHS Digital team (these are the only identifiers allowed to be shared by the project's consent materials) for all participants (currently 4,448) who have signed the Consent Form version 5. The NHS numbers will be collected by the UK-PBC research nurses at the English Trusts and Welsh Health Boards, as part of the study.

With regard to the list cleaning purpose Work Strand 1 of UK-PBC within the University of Cambridge will supply NHS number and Study ID. This information will be reviewed by NHS Digital for participants in UK-PBC. This list will be cleaned for the purpose of updating and filling in information not provided by cohort when they signed up to the study. This will allow updating of information so linkage to other data sets will be possible for the whole participant cohort.

Once the data has been extracted by NHS Digital team, data with only the study id and date of birth and no other direct patient identifiers will be returned to University of Cambridge. Work Strand 1 of UK-PBC within the University of Cambridge will then receive a dataset with the Health Data, while the Study ID and date of birth will be the only identifiers, and the NHS number omitted.
The transfer, to NHS Digital and back, will take place using a secure system, SEFT. The traffic will be directly between the Data Manager of the study and NHS Digital.

The only individuals accessing the NHS Digital data are the lead investigators and their teams who are substantive employees of the University of Cambridge and who have honorary contracts with the Cambridge University Hospitals NHS Trust. NHS Digital Data will be accessed from NHS Trust computers located within the Academic Department of Medical Genetics at the University of Cambridge. As per completed Security Questions Section, there will be no data storage on laptops or mobile devices; data will only be accessed from NHS Trust computers located within the Academic Department of Medical Genetics at the University of Cambridge.

For the avoidance of doubt, data from NHS Digital will be stored on CUHFT servers behind N3 but it will never be uploaded into the UK-PBC Database, nor will it be used to correct information already contained in the UK-PBC Database. This is to ensure that there is no onward access to NHS Digital datasets, in any form.
Data from NHS Digital will be used solely by the UK-PBC research team within Work Strand 1 at the University of Cambridge for prognostic modelling and will subsequently be electronically shredded.
The findings will be presented in aggregated format with small numbers suppressed at medical or scientific conferences, and meetings of the PBC Foundation (the leading PBC support group in the UK). Furthermore, findings will be published in medical and scientific journals, and the newsletters of the PBC Foundation and the UK-PBC project. Please note that participant identifiable details will never be presented or published.

All outputs will be restricted to aggregate data with small numbers suppressed in line with the HES Analysis Guide.

The data from NHS Digital will not be used for any other purpose other than that outlined in this Agreement.

NHS Digital reminds all organisations party to this agreement of the need to 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 ie: employees, agents and contractors of the Data Recipient who may have access to that data)

The participant’s title, full name and address (including post-code) are details collected by the research team upon enrolment (only NHS number is allowed to be transferred to NHS Digital by the consent materials). As a result, there is no requirement for NHS Digital to provide them. Also, UK-PBC researchers have selected the fewest possible variables that focus on diagnoses, investigations, procedures and treatments. Dates requested are very specific and essential to prognostic modelling, in terms of identifying inter-correlations between such important events and the point in time they happened.

However, the UK-PBC project intends to collect data only from the date of diagnosis (PBC) until the date of data linkage. The date of diagnosis is sometimes known to be several decades back for some participants, therefore, all data product periods have been selected, in an attempt to collect data as close to the date of diagnosis as possible. Data updates for the cohort would ideally be provided on an annual basis, after the first data extract by NHS Digital.

Please note that record level data will not be onwardly shared. Only aggregated data will get published with small numbers suppressed in line with the HES Analysis Guide.

MR1268 - Evaluation of the Role of Inflammation in non pulmonary disease manifestations in Chronic Airways — DARS-NIC-147978-LZDFC

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - consent provided by participants of research study, Identifiable, Yes, No (Consent (Reasonable Expectation), , )

Legal basis: Informed Patient consent to permit the receipt, processing and release of data by the HSCIC, Health and Social Care Act 2012 – s261(2)(c), , Informed Patient consent to permit the receipt, processing and release of data by NHS Digital, Health and Social Care Act 2012 – s261(2)(c)

Purposes: No (NHS Trust)

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

When:DSA runs 2012-02-22 — 2027-12-31 2016.06 — 2021.03.

Access method: Ongoing, One-Off

Data-controller type: CAMBRIDGE UNIVERSITY HOSPITALS NHS FOUNDATION TRUST, CAMBRIDGE UNIVERSITY HOSPITALS NHS FOUNDATION TRUST, UNIVERSITY OF CAMBRIDGE

Sublicensing allowed: No

Datasets:

  1. MRIS - Members and Postings Report
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Accident and Emergency
  4. MRIS - Cause of Death Report
  5. MRIS - Cohort Event Notification Report
  6. Civil Registration - Deaths
  7. Demographics
  8. MRIS - Flagging Current Status Report
  9. MRIS - Personal Demographics Service
  10. MRIS - Scottish NHS / Registration
  11. Hospital Episode Statistics Accident and Emergency (HES A and E)
  12. Hospital Episode Statistics Admitted Patient Care (HES APC)
  13. Civil Registrations of Death

Objectives:

Chronic Obstruction Pulmonary Disease (COPD) is the fourth leading cause of death globally and is predicted to increase in the coming decades. Capturing systemic (outside the lung) manifestations, which are often found in COPD patients, and assessing the predictive value of cardiovascular abnormalities, skeletal muscle weakness and plasma biomarkers − a characteristic by which a medical state can be observed from outside the patient − for hospital admission and mortality in COPD are recognised to be of increasing clinical importance.

The ERICA study − a multi-center observational, non-interventional, epidemiological cohort study − is interested in identifying/developing new biomarkers for COPD. The primary biomarkers of interest are fibrinogen (a key regulator of inflammation), pulse wave velocity (a measure of arterial stiffness), and quadriceps maximum voluntary contraction, which have a known relationship with inflammation and may cause muscle or cardiovascular problems in COPD patients. The applicant wants to explore these inter-relationships and determine if and how fibrinogen and other parameters; carotid intima-media thickness test (a measure to diagnose the extent of carotid atherosclerotic vascular disease), spirometry (a test used in the diagnoses of lung disease), a range of plasma and urine biomarkers, and questionnaire data can predict the longer-term outcomes in COPD patients.

The study objectives are to:
(i) Compare the reliability of the linked electronic health records hospital episode (HES) data with that of self-reported hospital admission data collected via the ERICA study questionnaire for the incidence (frequency) of COPD exacerbations (worsening of disease) requiring hospital admission, and hospitalisations for selected cardiovascular disease.
(ii) Conduct a literature review summarising existing knowledge about the relationship between selected cardiovascular and musculoskeletal phenotypes (set of observable characteristics of an individual) and outcomes in COPD related to both COPD exacerbations, cardiovascular events and mortality and determine predictors of future events of hospital admissions.
(iii) Determine new biomarkers and evaluate the relationship between baseline cardiovascular and musculoskeletal phenotypes and longitudinal outcomes of (a) hospital admissions for COPD (b) hospital admissions for selected cardiovascular diagnoses, (c) hospital-admissions related to frailty, falls and fracture, and (d) mortality events during follow-up using hospitals admission data.

The overarching aim of the ERICA consortium, which includes additional cohort studies such as ECLIPSE and ARCADE (though data from these studies are not linked to ERICA study data), is to relate systemic inflammation to non-pulmonary disease manifestations in COPD identified by candidate bedside biomarkers of cardiovascular and muscle function. Outcomes of this research will not only extend the understanding of these biomarkers through cross-sectional evaluation of subjects recruited from existing well-characterised cohorts in the UK and using experimental medicine hypothesis-testing trials in patients with evidence of systemic inflammation, but also:

• Help doctors determine the best type of treatment for newly diagnosed COPD patients.
• Reduce failures of new medicines (by generating evidence on stratification and efficacy of biomarkers to facilitate the design of smaller, more efficient Phase I-III clinical trials of medicines targeting inflammatory COPD subsets).
• Support the development of new therapies with improved health outcomes.

To answer these study objectives linkage data within The ERICA study − a dataset containing numerous biomarkers and socio-demographic data – with mortality data from the Office of National Statistics (ONS) and Hospital Episode Statistics (HES) from NHS Digital is required.

The ERICA study is a cohort study with a sample size of 734 patients with COPD. Five UK centres with an interest in COPD undertook this study, which ran from 2011-2013 with participants consenting to their identifiers being used. These centres are based in Cambridge, Edinburgh, Cardiff, Nottingham and London. The study includes data measured at baseline and 6 monthly follow up to two years. A detailed study protocol by Mohan et al. (2014) “Evaluating the Role of Inflammation in Chronic Airways Disease: The ERICA Study” can be found at http://dx.doi.org/10.3109/15412555.2014.898031

The ERICA study is already receiving death registration data (from the Patient Demographic System ) including: registration district, sub district, number and entry number, date, cause (text) and place of death (text), ICD coding, date and place of birth, occupation and address, for which there is an existing agreement with NHS Digital.

Yielded Benefits:

Delays in accessing the data have been experienced, which has meant analysis has also been delayed. The findings from this analysis will help identify test(s) that can easily be measured in clinical practice to capture manifestations and that support early stage detection. Currently no individual biomarker is able to reliably identify or predict clinical adverse outcomes. More so, in the past few decades no new classes of drugs have entered the market for COPD treatment. Results are expected to help doctors determine which type of treatment is best for newly diagnosed COPD patients in the future and outcomes are expected to facilitate the development of new therapies with improved health outcomes.

Expected Benefits:

A majority of studies assessing extra-pulmonary manifestations include only small sample sizes, are cross-sectional, have short follow-up periods, lack generalizability to a 'real world population' or are limited to inflammatory markers only failing to assess other cardiovascular and musculoskeletal biomarkers. The systematic review and meta-analysis, and assessing the longitudinal outcomes of selected cardiovascular and musculoskeletal phenotypes in COPD patients using the ERICA cohort data combined with HES and ONS data will help the applicant to understand if and to what extent existing and novel biomarkers and questionnaire data can predict the longer-term outcomes (i.e. COPD exacerbation, hospitalisation, death) in COPD patients.

When HES data are obtained the reliability of self-reported clinical outcomes measured through questionnaires will be compared with clinical outcomes recorded in electronic health records HES. Findings will help determine how reliable self-reported clinical outcomes measured are and may provide recommendations for future assessment of clinical outcomes in such a population.

The current number of deaths in the cohort has prevented the applicant for making any meaningful analysis using the ONS data. Causes of death in COPD are thought to frequently be related to respiratory disease with simultaneously a large portion attributed to cardiovascular disease. In the ERICA cohort, however, only a small proportion had a cardiac cause of death. It might be that globally the cause of death within COPD has changed over several decades with increased numbers of cardiac causes of deaths but data from the ERICA study does not indicate as many cardiac deaths and this trend might at least exclude the UK and warrants further exploration.

Findings will help identify test(s) that can easily be measured in clinical practice to capture manifestations and that support early stage detection. Currently no individual biomarker is able to reliably identify or predict clinical adverse outcomes. More so, in the past few decades no new classes of drugs have entered the market for COPD treatment. Results are expected to help doctors determine which type of treatment is best for newly diagnosed COPD patients in the future and outcomes are expected to facilitate the development of new therapies with improved health outcomes.

Outputs:

Though the reports from PDS have been collected since 2012, the few number of events has prevented the applicant from making any meaningful analysis using the ONS data, apart from being used to run preliminary survival analysis. Syntax with statistical code is written allowing to quickly re-run the survival analysis once the ONS update is provided.

Proposal findings will be published and disseminated beyond the proposal team. The study is expected to result in a PhD. During the PhD multiple publications are expected to result from this project including:
• A systematic literature review & meta-analysis of selected cardiovascular disease and musculoskeletal biomarkers in COPD. Expected target date manuscript journal submission is May 2017.
• A paper on longitudinal outcomes of selected cardiovascular and musculoskeletal phenotypes in COPD patients. Expected target date manuscript journal submission is August 2017.
• A paper assessing the reliability of self-reported hospital admission data compared to electronic health records hospital episode data. Expected target date manuscript journal submission is November 2017.
• A risk model predicting future events of hospital admissions in COPD. Expected target date manuscript journal submission is April 2018.
• The analysis of all the study objectives are expected to be completed at the end of the PhD. Expected target date is January 2019.

It is aimed to submit research findings to leading clinical open-access journals such as The Lancet Respiratory Medicine, Thorax, and the European Respiratory Journal. Readers of these journals include clinicians, decision-makers and academic scientists.

Research findings will be submitted to major and internationally leading conferences such as the International Conference on Lung Health and Diseases, the British Thoracic Society, and the European Respiratory Society. These world-leading events on lung health bring together clinicians, academic scientists, decision-makers, industrial partners and other disciplines sharing research findings and advances in medical care promoting the improvement of lung disease and care.

In addition to paper submissions to scientific journals, throughout the project the ERICA study website http://ericacopd.org will be used to disseminate research findings and study progression. When sharing research findings, results will be displayed as group results only, therefore individual data cannot be recognised.

The ERICA consortium considers Patient and Public Involvement important and has worked with the British Lung Foundation https://www.blf.org.uk in the design of the project and to update patients and the public on its work.

Processing:

Linking Hospital Episode Statistics (HES) and Office of National Statistics (ONS) to the ERICA dataset enables answering the previously mentioned study objectives.

Data is stored and processed entirely within the NHS trust, and held on a secure NHS server. Only staff at the Trust will access and analyse the data, and no record level data will be shared with any third party. All outputs will be aggregated and anonymised in line with the HES analysis guide.

The data flow and processing activities of data received from NHS Digital are as follows:

(i) Cambridge University Hospital: The ERICA study data controller sends NHS Digital the cohorts patient identifiable information (i.e. forename, surname, date of birth, postcode, NHS number, sex and study ID) for linkage to Hospital Episode Statistics (Admitted Patient Care and Accident & Emergency), as well as matching to the Patient Demographic System (PDS) for cause of death, members and postings and cohort event notification reports. Informed consent is the legal basis for sending data to the NHS Digital.

(ii) NHS Digital: cohort identifiers used to link to the HES data, identifiers stripped with study ID remaining. PDS used to retrieve death details including date and causes of death (ONS data), plus latest identifiers. HES Data returned back to ERICA study data controller, with ONS data returned to separate contact within the Trust.

(iii) Cambridge University Hospital: The ERICA study data controller receives the HES data, which will be handled and stored according to local NHS Trust security policies and procedures on the study database. The ONS data is received by a separate contact within the NHS Trust and will continue to be stored in the same separate database.

(v) Cambridge University Hospital: The linked HES and ONS data will be accessed by a PhD candidate, for data analysis including the examination of associations, regression and survival analysis, and risk prediction. Study findings using HES/ONS data will be published according to the agreement.

ONS Terms and Conditions will be adhered to regarding the processing of the data provided.