Towards better clinical prediction models: seven steps for development and an ABCD for validation

Eur Heart J. 2014 Aug 1;35(29):1925-31. doi: 10.1093/eurheartj/ehu207. Epub 2014 Jun 4.


Clinical prediction models provide risk estimates for the presence of disease (diagnosis) or an event in the future course of disease (prognosis) for individual patients. Although publications that present and evaluate such models are becoming more frequent, the methodology is often suboptimal. We propose that seven steps should be considered in developing prediction models: (i) consideration of the research question and initial data inspection; (ii) coding of predictors; (iii) model specification; (iv) model estimation; (v) evaluation of model performance; (vi) internal validation; and (vii) model presentation. The validity of a prediction model is ideally assessed in fully independent data, where we propose four key measures to evaluate model performance: calibration-in-the-large, or the model intercept (A); calibration slope (B); discrimination, with a concordance statistic (C); and clinical usefulness, with decision-curve analysis (D). As an application, we develop and validate prediction models for 30-day mortality in patients with an acute myocardial infarction. This illustrates the usefulness of the proposed framework to strengthen the methodological rigour and quality for prediction models in cardiovascular research.

Keywords: Calibration; Clinical usefulness; Discrimination; Missing values; Non-linearity; Prediction model; Shrinkage.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Calibration
  • Clinical Coding / methods
  • Decision Support Techniques
  • Diagnosis*
  • Humans
  • Models, Statistical*
  • Prognosis*
  • Reproducibility of Results
  • Risk Assessment / methods
  • Risk Assessment / standards