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Risk prediction models: II. External validation, model updating, and impact assessment
  1. Karel G M Moons1,
  2. Andre Pascal Kengne1,2,3,
  3. Diederick E Grobbee1,
  4. Patrick Royston4,
  5. Yvonne Vergouwe1,
  6. Douglas G Altman5,
  7. Mark Woodward2,6
  1. 1Julius Centre for Health Sciences and Primary Care, UMC Utrecht, Utrecht, The Netherlands
  2. 2Cardiovascular Division, The George Institute for Global Health, The University of Sydney, Sydney, Australia
  3. 3NCRP for Cardiovascular and Metabolic Diseases, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
  4. 4MRC Clinical Trials Unit, London, UK
  5. 5Centre for Statistics in Medicine, University of Oxford, Oxford, UK
  6. 6Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA
  1. Correspondence to Professor Karel Moons, Julius Centre for Health Sciences and Primary Care, UMC Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands; k.g.m.moons@umcutrecht.nl

Abstract

Clinical prediction models are increasingly used to complement clinical reasoning and decision-making in modern medicine, in general, and in the cardiovascular domain, in particular. To these ends, developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in the targeted individuals. Subsequently, the adoption of such models by professionals must guide their decision-making, and improve patient outcomes and the cost-effectiveness of care. In the first paper of this series of two companion papers, issues relating to prediction model development, their internal validation, and estimating the added value of a new (bio)marker to existing predictors were discussed. In this second paper, an overview is provided of the consecutive steps for the assessment of the model's predictive performance in new individuals (external validation studies), how to adjust or update existing models to local circumstances or with new predictors, and how to investigate the impact of the uptake of prediction models on clinical decision-making and patient outcomes (impact studies). Each step is illustrated with empirical examples from the cardiovascular field.

  • Prediction model
  • risk prediction
  • model validation
  • model updating
  • model impact assessment
  • obesity
  • clinical hypertension
  • prevention
  • diabetes
  • general practice
  • epidemiology

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Footnotes

  • KGMM and APK contributed equally.

  • Funding Karel GM Moons receives funding from the Netherlands Organisation for Scientific Research (project 9120.8004 and 918.10.615).

  • Competing interests None.

  • Provenance and peer review Commissioned; externally peer reviewed.

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