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Dahmer MK, Yang G, Zhang M, Quasney MW, Sapru A, Weeks HM, Sinha P, Curley MAQ, Delucchi KL, Calfee CS, Flori H, Matthay MA, Bateman ST, Berg MD, Borasino S, Bysani GK, Cowl AS, Bowens CD, Faustino VS, Fineman LD, Godshall AJ, Hirshberg EL, Kirby AL, McLaughlin GE, Medar SS, Oren PP, Schneider JB, Schwarz AJ, Shanley TP, Source LR, Truemper EJ, Vender Heyden MA, Wittmayer K, Zuppa AF, Wypij D. Identification of phenotypes in paediatric patients with acute respiratory distress syndrome: a latent class analysis. Lancet Respir Med 2022; 10:289-297. [PMID: 34883088 PMCID: PMC8897230 DOI: 10.1016/s2213-2600(21)00382-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/04/2021] [Accepted: 08/09/2021] [Indexed: 10/19/2022]
Abstract
BACKGROUND Previous 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. METHODS This 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. FINDINGS 304 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 frequent diagnosis of sepsis, consistent with the adult hyperinflammatory phenotype. All levels of severity of PARDS were observed across both phenotypes. Children with the hyperinflammatory phenotype (phenotype 2) had worse clinical outcomes than those with the hypoinflammatory phenotype (phenotype 1), with a longer duration of mechanical ventilation (median 10·0 days [IQR 6·3-21·0] for phenotype 2 vs 6·6 days [4·1-10·8] for phenotype 1, p<0·0001), and higher incidence of mortality (17 [13·8%] of 123 patients vs four [2·2%] of 181 patients, p=0·0001). When using adult phenotype classification algorithms in children, the soluble tumour necrosis factor receptor-1 (sTNFr1), vasopressor use, and interleukin (IL)-6 variables gave an area under the curve (AUC) of 0·956, and the sTNFr1, vasopressor use, and IL-8 variables gave an AUC of 0·954, compared with the gold standard of latent class analysis. INTERPRETATION Latent class analysis identified two phenotypes in children with ARDS with characteristics similar to those in adults, including worse outcomes among patients with the hyperinflammatory phenotype. PARDS phenotypes should be considered in design and analysis of future clinical trials in children. FUNDING US National Institutes of Health.
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Affiliation(s)
- Mary K Dahmer
- Department of Pediatrics, Division of Critical Care Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Guangyu Yang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Min Zhang
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Michael W Quasney
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Anil Sapru
- Department of Pediatrics, University of California, Los Angeles, Los Angeles, CA
| | - Heidi M. Weeks
- Department of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Pratik Sinha
- Department of Anesthesia, Washington University, St. Louis, MO
| | - Martha AQ Curley
- Department of Family and Community Health (School of Nursing), Division of Anesthesia and Critical Care Medicine (Perelman School of Medicine) University of Pennsylvania, Philadelphia, PA; Research Institute; Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Kevin L Delucchi
- Department of Psychiatry & Behavioral Sciences, University of California, San Francisco, San Francisco, CA
| | - Carolyn S Calfee
- Department of Medicine, Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, University of California, San Francisco, San Francisco, CA
| | - Heidi Flori
- Department of Pediatrics, Division of Critical Care Medicine, University of Michigan, Ann Arbor, MI
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