Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals. Issue 5 (May 2020)

• Record Type: Journal Article
• Authors: 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.;
• Is Part Of: Critical care medicine. Volume 48:Issue 5(2020)
• Publication Date: 2020-05
• Subjects: critical care -- pulmonary medicine -- prediction models -- sleep medicine; Critical care medicine -- Periodicals; Soins intensifs -- Périodiques; 616.028
• DOI: 10.1097/CCM.0000000000004246
• Languages: English
• ISSNs: 0090-3493

Abstract

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. Abstract : Supplemental Digital Content is available in the text.