Abstract
The objective of this study was to assess the reliability of individual risk predictions based on routinely collected data considering the heterogeneity between clinical sites in data and populations. Cardiovascular disease (CVD) risk prediction with QRISK3 was used as exemplar. The study included 3.6 million patients in 392 sites from the Clinical Practice Research Datalink. Cox models with QRISK3 predictors and a frailty (random effect) term for each site were used to incorporate unmeasured site variability. There was considerable variation in data recording between general practices (missingness of body mass index ranged from 18.7% to 60.1%). Incidence rates varied considerably between practices (from 0.4 to 1.3 CVD events per 100 patient-years). Individual CVD risk predictions with the random effect model were inconsistent with the QRISK3 predictions. For patients with QRISK3 predicted risk of 10%, the 95% range of predicted risks were between 7.2% and 13.7% with the random effects model. Random variability only explained a small part of this. The random effects model was equivalent to QRISK3 for discrimination and calibration. Risk prediction models based on routinely collected health data perform well for populations but with great uncertainty for individuals. Clinicians and patients need to understand this uncertainty.
Highlights
Cardiovascular disease (CVD) was the primary cause of death in USA, Europe and China in 20171
This study was restricted to 392 general practices that have been linked to Hospital Episode Statistics (HES), Office for National Statistics (ONS) and Townsend scores[7]
This study found that incorporating practice variability in a risk prediction model substantially affected the predicted CVD risks of individual patients
Summary
Cardiovascular disease (CVD) was the primary cause of death in USA, Europe and China in 20171. Risk prediction models are often used to predict CVD risk for individual patients[5]. Examples are the Framingham risk score (FRS) and QRISK which provide risks of developing CVD in the 10 years. QRISK is based on routinely collected data from general practices in the UK7. The patient case-mix (referring to a variation in risk factors for disease) may vary between practices. This variability in the underlying data sources is currently not routinely www.nature.com/scientificreports/. The objective of this study was to assess the level of generalisability of risk prediction models that are based on routinely collected data from EHRs, and to measure the effects of practice heterogeneity on the individual predictions of risk. The QRISK3 prediction model (for the 10 year risk of CVD) was used as an exemplar
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