Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data

作者：Pratik Sinha, Matthew M. Churpek, Carolyn S. Calfee · 发表于：American Journal of Respiratory and Critical Care Medicine · 年份：2020 · DOI：10.1164/rccm.202002-0347oc · 被引用次数：221 · 研究领域：Respiratory Support and Mechanisms、Sepsis Diagnosis and Treatment、Neonatal Respiratory Health Research

Abstract Rationale Two distinct phenotypes of acute respiratory distress syndrome (ARDS) with differential clinical outcomes and responses to randomly assigned treatment have consistently been identified in randomized controlled trial cohorts using latent class analysis. Plasma biomarkers, key components in phenotype identification, currently lack point-of-care assays and represent a barrier to the clinical implementation of phenotypes. Objectives The objective of this study was to develop models to classify ARDS phenotypes using readily available clinical data only. Methods Three randomized controlled trial cohorts served as the training data set (ARMA [High vs. Low Vt], ALVEOLI [Assessment of Low Vt and Elevated End-Expiratory Pressure to Obviate Lung Injury], and FACTT [Fluids and Catheter Treatment Trial]; n = 2,022), and a fourth served as the validation data set (SAILS [Statins for Acutely Injured Lungs from Sepsis]; n = 745). A gradient-boosted machine algorithm was used to develop classifier models using 24 variables (demographics, vital signs, laboratory, and respiratory variables) at enrollment. In two secondary analyses, the ALVEOLI and FACTT cohorts each, individually, served as the validation data set, and the remaining combined cohorts formed the training data set for each analysis. Model performance was evaluated against the latent class analysis–derived phenotype. Measurements and Main Results For the primary analysis, the model accurately classified the pheno...