• Jose Dianti, Idunn S Morris, Martin Urner, Marcello Schmidt, George Tomlinson, AmatoMarcelo B PMBPLaboratório de Pneumologia LIM-09, Disciplina de Pneumologia, Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, Sao Paulo, Brazil., Lluis Blanch, Gordon Rubenfeld, and Ewan C Goligher.
• Interdepartmental Division of Critical Care Medicine.
• Am. J. Respir. Crit. Care Med. 2023 Jun 1; 207 (11): 144114501441-1450.

AbstractICU clinicians rely on bedside physiological measurements to inform many routine clinical decisions. Because deranged physiology is usually associated with poor clinical outcomes, it is tempting to hypothesize that manipulating and intervening on physiological parameters might improve outcomes for patients. However, testing these hypotheses through mathematical models of the relationship between physiology and outcomes presents a number of important methodological challenges. These models reflect the theories of the researcher and can therefore be heavily influenced by one's assumptions and background beliefs. Model building must therefore be approached with great care and forethought, because failure to consider relevant sources of measurement error, confounding, coupling, and time dependency or failure to assess the direction of causality for associations of interest before modeling may give rise to spurious results. This paper outlines the main challenges in analyzing and interpreting these models and offers potential solutions to address these challenges.

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