Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Objectives: To validate the performance of a set of risk prediction algorithms developed using the QResearch database, in an independent sample from general practices contributing to the Clinical Research Data Link (CPRD). Setting: Prospective open cohort study using practices contributing to the CPRD database and practices contributing to the QResearch database. Participants: The CPRD validation cohort consisted of 3.3 million patients, aged 25-99 years registered at 357 general practices between 1 Jan 1998 and 31 July 2012. The validation statistics for QResearch were obtained from the original published papers which used a one-third sample of practices separate to those used to derive the score. A cohort from QResearch was used to compare incidence rates and baseline characteristics and consisted of 6.8 million patients from 753 practices registered between 1 Jan 1998 and until 31 July 2013. Outcome measures: Incident events relating to seven different risk prediction scores: QRISK2 (cardiovascular disease); QStroke (ischaemic stroke); QDiabetes (type 2 diabetes); QFracture (osteoporotic fracture and hip fracture); QKidney (moderate and severe kidney failure); QThrombosis (venous thromboembolism); QBleed (intracranial bleed and upper gastrointestinal haemorrhage). Measures of discrimination and calibration were calculated. Results: Overall, the baseline characteristics of the CPRD and QResearch cohorts were similar though QResearch had higher recording levels for ethnicity and family history. The validation statistics for each of the risk prediction scores were very similar in the CPRD cohort compared with the published results from QResearch validation cohorts. For example, in women, the QDiabetes algorithm explained 50% of the variation within CPRD compared with 51% on QResearch and the receiver operator curve value was 0.85 on both databases. The scores were well calibrated in CPRD. Conclusions: Each of the algorithms performed practically as well in the external independent CPRD validation cohorts as they had in the original published QResearch validation cohorts.

Original publication




Journal article


BMJ Open

Publication Date