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

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

Authors

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 Donaldson

Dominic A. Fitzgerald

Emma Grainger

Lauren Hale

Felix J. Herth

Patrick M. Kochanek

Guy Marks

J. Randall Moorman

David E. Ost

Michael Schatz

Aziz Sheikh

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ALAN SMYTH alan.smyth@nottingham.ac.uk
Professor of Child Health

Iain Stewart

Paul W. Stewart

Erik R. Swenson

Ronald Szymusiak

Jean-Louis Teboul

Jean-Louis Vincent

Jadwiga A. Wedzicha

David M. Maslove



Abstract

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.

Citation

Leisman, D. E., Harhay, M. O., Lederer, D. J., Abramson, M., Adjei, A. A., Bakker, J., …Maslove, D. M. (2020). Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals. Critical Care Medicine, 48(5), 623-633. https://doi.org/10.1097/ccm.0000000000004246

Journal Article Type Article
Acceptance Date Mar 11, 2020
Publication Date 2020-05
Deposit Date Apr 20, 2020
Publicly Available Date Apr 20, 2020
Journal Critical Care Medicine
Print ISSN 0090-3493
Publisher Lippincott, Williams & Wilkins
Peer Reviewed Peer Reviewed
Volume 48
Issue 5
Pages 623-633
DOI https://doi.org/10.1097/ccm.0000000000004246
Keywords Critical Care and Intensive Care Medicine
Public URL https://nottingham-repository.worktribe.com/output/4319007
Publisher URL https://journals.lww.com/ccmjournal/Fulltext/2020/05000/Development_and_Reporting_of_Prediction_Models_.3.aspx

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