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Clinical Practice Research Datalink (Cprd)

Clinical Practice Research Datalink (CPRD)

Description

Clinical Practice Research Datalink (CPRD) is a real-world research service supporting retrospective and prospective public health and clinical studies. CPRD research data services are delivered by the Medicines and Healthcare products Regulatory Agency with support from the National Institute for Health and Care Research (NIHR), as part of the Department of Health and Social Care.

CPRD collects anonymised patient data from a network of GP practices across the UK. Primary care data are linked to a range of other health related data to provide a longitudinal, representative UK population health dataset. The data encompass 60 million patients, including 18 million currently registered patients.

For more than 30 years, research using CPRD data and services has informed clinical guidance and best practice, resulting in over 3,000 peer-reviewed publications investigating drug safety, use of medicines, effectiveness of health policy, health care delivery and disease risk factors.

Datasets & BioSamples (47)

Death Registration data for CPRD GOLD
Dataset population size: 1,158,177
Health and disease
Cancer registration data for CPRD Aurum
Dataset population size: 3,255,865
Health and disease
HES Accident and Emergency data for CPRD Aurum
Dataset population size: 34,416,869
Health and disease
Cancer registration data for CPRD GOLD
Dataset population size: 901,965
Health and disease
Quality of Life of Cancer Survivors: Pilot Patient Reported Outcomes(Aurum)
Dataset population size: 3,255,865
Health and disease
Cancer Patient Experience Survey (CPES) for CPRD GOLD
Dataset population size: 901,965
Health and disease

Publications (38)

Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study.
Banerjee A, Dashtban A, Chen S, Pasea L, Thygesen JH, Fatemifar G, Tyl B, Dyszynski T, Asselbergs FW, Lund LH, Lumbers T, Denaxas S, Hemingway H.
2023
Predicting the risk of acute kidney injury in primary care: derivation and validation of STRATIFY-AKI.
Koshiaris C, Archer L, Lay-Flurrie S, Snell KI, Riley RD, Stevens R, Banerjee A, Usher-Smith JA, Clegg A, Payne RA, Ogden M, Hobbs FR, McManus RJ, Sheppard JP.
2023
Translating and evaluating historic phenotyping algorithms using SNOMED CT.
Elkheder M, Gonzalez-Izquierdo A, Qummer Ul Arfeen M, Kuan V, Lumbers RT, Denaxas S, Shah AD.
2023
Using national electronic health records for pandemic preparedness: validation of a parsimonious model for predicting excess deaths among those with COVID-19-a data-driven retrospective cohort study.
Mizani MA, Dashtban A, Pasea L, Lai AG, Thygesen J, Tomlinson C, Handy A, Mamza JB, Morris T, Khalid S, Zaccardi F, Macleod MJ, Torabi F, Canoy D, Akbari A, Berry C, Bolton T, Nolan J, Khunti K, Denaxas S, Hemingway H, Sudlow C, Banerjee A, CVD-COVID-UK Consortium.
2023
Determining cardiovascular risk in patients with unattributed chest pain in UK primary care: an electronic health record study.
Jordan KP, Rathod-Mistry T, van der Windt DA, Bailey J, Chen Y, Clarson L, Denaxas S, Hayward RA, Hemingway H, Kyriacou T, Mamas MA.
2023