Do population-level risk prediction models that use routinely collected health data reliably predict individual risks?
アブストラクト 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. ジャーナル名 Scientific reports 投稿日 2019/8/4 投稿者 Li, Yan; Sperrin, Matthew; Belmonte, Miguel; Pate, Alexander; Ashcroft, Darren M; van Staa, Tjeerd Pieter 組織名 Health e-Research Centre, School of Health Sciences, Faculty of Biology, Medicine;and Health, The University of Manchester, Manchester Academic Health Sciences;Centre (MAHSC), Oxford Road, Manchester, M13 9PL, UK.;Centre for Pharmacoepidemiology and Drug Safety, School of Health Sciences,;Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road,;Manchester, M13 9PL, UK.;NIHR Greater Manchester Patient Safety Translational Research Centre, School of;Health Sciences, Faculty of Biology, Medicine and Health, University of;Manchester, Oxford Road, Manchester, M13 9PL, UK.;firstname.lastname@example.org.;Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht,;Netherlands. email@example.com.;Alan Turing Institute, Headquartered at the British Library, London, UK. Pubmed リンク https://www.ncbi.nlm.nih.gov/pubmed/31375726/