ArticleScientific reports2019
Do population-level risk prediction models that use routinely collected health data reliably predict individual risks?
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and Challenges of Cardiovascular Disease Risk Prediction for Primary Prevention Using Machine Learning and Electronic Health Records: A Systematic Review.Reviews in cardiovascular medicine · 2025Pooled it
- A novel monocyte-based biomarker of cardiovascular risk: comparison with traditional cardiovascular risk calculators.American journal of preventive cardiology · 2026Article
- Predicting acute postsurgical pain in the postanesthesia care unit: risk tool development and internal validation.Pain reports · 2025Article
- Identify the underlying true model from other models for clinical practice using model performance measures.BMC medical research methodology · 2025Article
- Mind the Gaps: Literature Survey Reveals Shortcomings in Handling Missing Data in Clinical Practice Research Datalink (CPRD), a UK Primary Care Health Records Database.Clinical epidemiology · 2025Article
- The Role of Artificial Intelligence in Cardiovascular Disease Risk Prediction: An Updated Review on Current Understanding and Future Research.Current cardiology reviews · 2025Review
- Clinical Algorithms and the Legacy of Race-Based Correction: Historical Errors, Contemporary Revisions and Equity-Oriented Methodologies for Epidemiologists.Clinical epidemiology · 2025Review
- Improving event prediction using general practitioner clinical judgement in a digital risk stratification model: a pilot study.BMC medical informatics and decision making · 2024Article
- An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems.Respiratory research · 2023Article
- Interoperable and explainable machine learning models to predict morbidity and mortality in acute neurological injury in the pediatric intensive care unit: secondary analysis of the TOPICC study.Frontiers in pediatrics · 2023Article
- Understanding the risk of developing weight-related complications associated with different body mass index categories: a systematic review.Diabetology & metabolic syndrome · 2022Review
- Breaking bad news in the era of artificial intelligence and algorithmic medicine: an exploration of disclosure and its ethical justification using the hedonic calculus.AI and ethics · 2022Article
- Highlighting uncertainty in clinical risk prediction using a model of emergency laparotomy mortality risk.NPJ digital medicine · 2022Article
- Developing a Smart Home Technology Innovation for People With Physical and Mental Health Problems: Considerations and Recommendations.JMIR mHealth and uHealth · 2022Article
- The importance of blood pressure thresholds versus predicted cardiovascular risk on subsequent rates of cardiovascular disease: a cohort study in English primary care.The lancet. Healthy longevity · 2022Article
- Cardiovascular Disease Risk Modeling for Astronauts: Making the Leap From Earth to Space.Frontiers in cardiovascular medicine · 2022Review
- Effect of competing mortality risks on predictive performance of the QRISK3 cardiovascular risk prediction tool in older people and those with comorbidity: external validation population cohort study.The lancet. Healthy longevity · 2021Article
- Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar.BMJ (Clinical research ed.) · 2020Article
- External validation of a United Kingdom primary-care Cushing's prediction tool in a population of referred Dutch dogs.Journal of veterinary internal medicineArticle
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Authors and funding
6 authors.
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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.
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