Interdepartmental Division of Critical Care Medicine.
University Health Network/Sinai Health System.
Am J Respir Crit Care Med. 2023 Jun 1;207(11):1441-1450. doi: 10.1164/rccm.202206-1216CI.
ICU 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.
ICU 临床医生依靠床边的生理测量来为许多常规临床决策提供信息。由于生理机能紊乱通常与不良的临床结果相关,因此人们很容易假设操纵和干预生理参数可能会改善患者的预后。然而,通过生理学和结果之间关系的数学模型来检验这些假设提出了许多重要的方法学挑战。这些模型反映了研究人员的理论,因此可能会受到其假设和背景信念的严重影响。因此,在建模之前,模型构建必须非常谨慎和深思熟虑,因为如果不考虑相关的测量误差、混杂、耦合和时间依赖性源,或者不评估建模前感兴趣的关联的因果关系方向,可能会导致虚假结果。本文概述了分析和解释这些模型的主要挑战,并提供了潜在的解决方案来应对这些挑战。