Intensive Care Medicine

, Volume 40, Issue 4, pp 513–527 | Cite as

How to derive and validate clinical prediction models for use in intensive care medicine

  • José LabarèreEmail author
  • Renaud Bertrand
  • Michael J. Fine
Statistics for Intensivists



Clinical prediction models are formal combinations of historical, physical examination and laboratory or radiographic test data elements designed to accurately estimate the probability that a specific illness is present (diagnostic model), will respond to a form of treatment (therapeutic model) or will have a well-defined outcome (prognostic model) in an individual patient. They are derived and validated using empirical data and used to assist physicians in their clinical decision-making that requires a quantitative assessment of diagnostic, therapeutic or prognostic probabilities at the bedside.


To provide intensivists with a comprehensive overview of the empirical development and testing phases that a clinical prediction model must satisfy before its implementation into clinical practice.


The development of a clinical prediction model encompasses three consecutive phases, namely derivation, (external) validation and impact analysis. The derivation phase consists of building a multivariable model, estimating its apparent predictive performance in terms of both calibration and discrimination, and assessing the potential for statistical over-fitting using internal validation techniques (i.e. split-sampling, cross-validation or bootstrapping). External validation consists of testing the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. Impact analysis involves comparative research [i.e. (cluster) randomized trials] to determine whether clinical use of a prediction model affects physician practices, patient outcomes or the cost of healthcare delivery.


This narrative review introduces a checklist of 19 items designed to help intensivists develop and transparently report valid clinical prediction models.


Clinical prediction models Clinical decision rule Prognosis Severity of illness index Intensive care 



This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. On behalf of all authors, the corresponding author states that there is no conflict of interest. The authors thank Linda Northrup from English Solutions for her assistance in editing the manuscript.

Conflicts of interest

The authors have no conflict of interest to report.


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Copyright information

© Springer-Verlag Berlin Heidelberg and ESICM 2014

Authors and Affiliations

  • José Labarère
    • 1
    • 2
    • 7
    Email author
  • Renaud Bertrand
    • 3
    • 4
  • Michael J. Fine
    • 5
    • 6
  1. 1.Quality of Care UnitUniversity HospitalGrenobleFrance
  2. 2.TIMC UMR 5525 CNRSUniversité Joseph Fourier–Grenoble 1GrenobleFrance
  3. 3.Emergency DepartmentCochin and Hôtel Dieu Hospitals, Assistance Publique-Hôpitaux de Paris (AP-HP)ParisFrance
  4. 4.Faculté de Médecine Paris DescartesParisFrance
  5. 5.Veterans Affairs Center for Health Equity and Research PromotionVA Pittsburgh Healthcare SystemPittsburghUSA
  6. 6.Division of General Internal MedicineUniversity of Pittsburgh Medical CenterPittsburghUSA
  7. 7.UQEMGrenobleFrance

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