Identification of phenotypes in paediatric patients with acute respiratory distress syndrome: a latent class analysis

作者：Mary K. Dahmer, Guangyu Yang, Min Zhang, Michael W. Quasney, Anil Sapru, Heidi M. Weeks, Pratik Sinha, Martha A. Q. Curley, Kevin Delucchi, Carolyn S. Calfee, Heidi R. Flori, investigators, RESTORE and BALI study, Michael A. Matthay, Scot T. Bateman, Marc Berg, Santiago Borasino, G. Kris Bysani, Allison S. Cowl, Cindy Darnell Bowens, Vincent S Faustino, Lori D. Fineman, Aaron J. Godshall, Eliotte L. Hirshberg, Aileen L. Kirby, Gwenn E. McLaughlin, Shivanand Medar, Phineas P. Oren, James B. Schneider, Adam J. Schwarz, Thomas P. Shanley, Lauren R Source, Edward J. Truemper, Michele A Vender Heyden, Kimberly Wittmayer, Athena F. Zuppa, David Wypij, Network, Pediatric Acute Lung Injury and Sepsis Investigators · 发表于：eScholarship (California Digital Library) · 年份：2022 · 被引用次数：101 · 研究领域：Respiratory Support and Mechanisms、Sepsis Diagnosis and Treatment、Neonatal Respiratory Health Research

BackgroundPrevious latent class analysis of adults with acute respiratory distress syndrome (ARDS) identified two phenotypes, distinguished by the degree of inflammation. We aimed to identify phenotypes in children with ARDS in whom developmental differences might be important, using a latent class analysis approach similar to that used in adults.MethodsThis study was a secondary analysis of data aggregated from the Randomized Evaluation of Sedation Titration for Respiratory Failure (RESTORE) clinical trial and the Genetic Variation and Biomarkers in Children with Acute Lung Injury (BALI) ancillary study. We used latent class analysis, which included demographic, clinical, and plasma biomarker variables, to identify paediatric ARDS (PARDS) phenotypes within a cohort of children included in the RESTORE and BALI studies. The association of phenotypes with clinically relevant outcomes and the performance of paediatric data in adult ARDS classification algorithms were also assessed.Findings304 children with PARDS were included in this secondary analysis. Using latent class analysis, a two-class model was a better fit for the cohort than a one-class model (p<0·001). Latent class analysis identified two classes: class 1 (181 [60%] of 304 patients with PARDS) and class 2 (123 [40%] of 304 patients with PARDS), referred to as phenotype 1 and 2 hereafter. Phenotype 2 was characterised by higher concentrations of inflammatory biomarkers, a higher incidence of vasopressor use, and more ...