Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals

Daniel E. Leisman, Michael O. Harhay, David J. Lederer, Michael Abramson, Alex A. Adjei, Jan Bakker, Zuhair K. Ballas, Esther Barreiro, Scott C. Bell, Rinaldo Bellomo, Jonathan A. Bernstein, Richard D. Branson, Vito Brusasco, James D. Chalmers, Sudhansu Chokroverty, Giuseppe Citerio, Nancy A. Collop, Colin R. Cooke, James D. Crapo, Gavin DonaldsonDominic A. Fitzgerald, Emma Grainger, Lauren Hale, Felix J. Herth, Patrick M. Kochanek, Guy Marks, J. Randall Moorman, David E. Ost, Michael Schatz, Aziz Sheikh, Alan R. Smyth, Iain Stewart, Paul W. Stewart, Erik R. Swenson, Ronald Szymusiak, Jean-Louis Teboul, Jean-Louis Vincent, Jadwiga A. Wedzicha, David M. Maslove

    Research output: Contribution to journalArticlepeer-review

    169 Citations (Scopus)
    246 Downloads (Pure)


    Prediction models aim to use available data to predict a health state or outcome that has not yet been observed. Prediction is primarily relevant to clinical practice, but is also used in research, and administration. While prediction modeling involves estimating the relationship between patient factors and outcomes, it is distinct from casual inference. Prediction modeling thus requires unique considerations for development, validation, and updating. This document represents an effort from editors at 31 respiratory, sleep, and critical care medicine journals to consolidate contemporary best practices and recommendations related to prediction study design, conduct, and reporting. Herein, we address issues commonly encountered in submissions to our various journals. Key topics include considerations for selecting predictor variables, operationalizing variables, dealing with missing data, the importance of appropriate validation, model performance measures and their interpretation, and good reporting practices. Supplemental discussion covers emerging topics such as model fairness, competing risks, pitfalls of "modifiable risk factors", measurement error, and risk for bias. This guidance is not meant to be overly prescriptive; we acknowledge that every study is different, and no set of rules will fit all cases. Additional best practices can be found in the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, to which we refer readers for further details.

    Original languageEnglish
    Pages (from-to)623-633
    Number of pages11
    JournalCritical Care Medicine
    Issue number5
    Early online date4 Mar 2020
    Publication statusPublished - May 2020


    • critical care
    • prediction models
    • pulmonary medicine
    • sleep medicine

    ASJC Scopus subject areas

    • Critical Care and Intensive Care Medicine


    Dive into the research topics of 'Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals'. Together they form a unique fingerprint.

    Cite this