Intended for healthcare professionals

Research Methods & Reporting

Guide to presenting clinical prediction models for use in clinical settings

BMJ 2019; 365 doi: (Published 17 April 2019) Cite this as: BMJ 2019;365:l737
  1. Laura J Bonnett, tenure track fellow1,
  2. Kym I E Snell, research fellow in biostatistics2,
  3. Gary S Collins, professor of medical statistics3,
  4. Richard D Riley, professor of biostatistics2
  1. 1Department of Biostatistics, University of Liverpool, Liverpool L69 3GL, UK
  2. 2Centre for Prognosis Research, Research Institute for Primary Care and Health Sciences, Keele University, Keele, UK
  3. 3Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
  1. Correspondence to: L J Bonnett L.J.Bonnett{at}
  • Accepted 8 February 2019

Clinical prediction models estimate the risk of existing disease or future outcome for an individual, which is conditional on the values of multiple predictors such as age, sex, and biomarkers. In this article, Bonnett and colleagues provide a guide to presenting clinical prediction models so that they can be implemented in practice, if appropriate. They describe how to create four presentation formats and discuss the advantages and disadvantages of each format. A key message is the need for stakeholder engagement to determine the best presentation option in relation to the clinical context of use and the intended users

Summary points

  • Presentation format for clinical prediction models deemed suitable for use is important but receives relatively limited attention in the literature

  • Clear presentation of a prediction model is fundamental to ensure other researchers can independently validate the model, and that healthcare professionals and others can implement it within healthcare

  • Presentation of the full model equation is essential. There are many ways to present prediction models for end users, which range from points score systems and nomograms, to websites and mobile apps

  • The best presentation is user and environment specific, and it is preferably determined through engagement of stakeholders, including patients

  • If presentation requires a simplified version of the full model to be generated, then the predictive performance of this simplified model should also be validated and compared with that of the full model


Clinical prediction models estimate the risk of existing disease (diagnostic prediction model) or future outcome (prognostic prediction model) for an individual, which is conditional on the values of multiple predictors (prognostic or risk factors) such as age, sex, and biomarkers.1 A large number of prediction models are published in the medical literature each year,2 and most are developed using a regression framework such as logistic and Cox regression …

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