Comparisons of risk prediction algorithms using three clinical research databases (QResearch, CPRD Aurum and CPRD Gold)

QResearch, CPRD Gold and CPRD Aurum databases are three large general practice databases which are widely used for research. The CPRD databases are similar to the QResearch database but contain a different group of practices and are linked to different external data sources.

Our study will compare characteristics of the database such including how common various diseases are and how well the data are recorded on each. We will develop new prediction algorithms and compare them with existing algorithms. We will then check to see how well various risk algorithms work in each data source. For example we will develop algorithms on QResearch and test them on the both CPRD databases.

Risk algorithms are tools which work out the chances that a patient has got or might develop a disease in the future (such as diabetes or cancer), based on information about them such as their age, sex, ethnicity and illnesses and treatments. In clinical practice, such tools can be used to help patients understand their risk of different diseases and identify those who might need help to reduce their risk or referral to hospital for tests. In this study, we want to see how well these algorithms work on each database and to understand similarities and differences between the databases which will help us interpret the results.

We will undertake cohort studies in a large population of primary care patients from an open cohort using data from all three database - CPRD Gold and Aurum databases and QResearch. We will include all practices which have been using their current GP clinical computer system for at least a year. We will use the latest data available from each database at the time of the analysis. We will identify cohorts from each database which will include patients registered with practices on or after 01 Jan 1998 until the latest date for which data are available at the time of the study.

We will use the GP data linked to hospital episode statistics, cancer registry and mortality.

1. GP DATA
demographics including age, sex, ethnicity, deprivation, region
clinical diagnoses - major chronic diseases e.g. diabetes, cardiovascular disease, thromboembolism, cancer, fracture, haemorrhage
clinical values e.g. body mass index, smoking, alcohol
laboratory investigations e.g. full blood count, electrolytes, liver function tests, CA125
commonly prescribed medication

2. HES Data
HES data to identify outcomes of interest e.g. diabetes, cardiovascular disease, thromboembolism, cancer, fracture, haemorrhage

3. Mortality Data
mortality data to identify outcomes of interest on the death certificate e.g. diabetes, cardiovascular disease, thromboembolism, cancer, fracture, haemorrhage and cancer treatments.

4. Cancer Registry Data
Cancer registry data to identify characteristics of cancers e.g. type, location, stage, grade, route to diagnosis, treatments (e.g. chemotherapy, radiotherapy, hormonal, surgery)

Julia Hippisley-Cox, University of Oxford

Carol AC Coupland,  University of Oxford

Mona Bafadhel, King’s College London

Richard EK Russell, King’s College London

Aziz Sheikh, University of Edinburgh

Peter Brindle, University of Bristol

Keith M. Channon, University of Oxford