Three hospitalized non-critical COVID-19 subphenotypes and change in intubation or death over time: A latent class analysis with external and longitudinal validation

There are two subphenotypes of COVID-19 acute respiratory distress syndrome with differential responses to corticosteroids, but whether similar subphenotypes of hospitalized non-critical COVID-19 patients exist remains unknown. To identify and validate non-critical COVID-19 subphenotypes at hospital...

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Vydáno v:	PloS one Ročník 20; číslo 3; s. e0316434
Hlavní autoři:	Stringer, William S., Labar, Amy S., Geleris, Joshua D., Sholle, Evan V., Berlin, David A., McGroder, Claire M., Cummings, Matthew J., O’Donnell, Max R., Yi, Haoyang, Yang, Xuehan, Wei, Ying, Schenck, Edward J., Baldwin, Matthew R.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 19.03.2025 Public Library of Science (PLoS)
Témata:	Acute respiratory distress syndrome Adult Adults Aged Analysis Biology and Life Sciences Biomarkers C-reactive protein Care and treatment Clinical trials Coronaviruses Corticoids Corticosteroids COVID-19 COVID-19 - mortality COVID-19 - pathology COVID-19 - therapy COVID-19 - virology Death Emergency medical care Entropy Female Fibrinolysis Forecasts and trends Health aspects Health care facilities Health services Heterogeneity Hospitalization Hospitals Humans Inflammation Intubation Latent Class Analysis Learning algorithms Longitudinal Studies Machine Learning Male Medical research Medicine and Health Sciences Medicine, Experimental Middle Aged Mortality Patients Phenotype Physical Sciences Probability Research and Analysis Methods Respiratory distress syndrome Retrospective Studies Risk factors Rivaroxaban SARS-CoV-2 - isolation & purification Severe acute respiratory syndrome Severe acute respiratory syndrome coronavirus 2 United States Variables Vital signs United States United Kingdom
ISSN:	1932-6203, 1932-6203
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Shrnutí:	There are two subphenotypes of COVID-19 acute respiratory distress syndrome with differential responses to corticosteroids, but whether similar subphenotypes of hospitalized non-critical COVID-19 patients exist remains unknown. To identify and validate non-critical COVID-19 subphenotypes at hospital admission that may elucidate pathobiology and facilitate heterogeneity-of-treatment effect analyses of clinical trials with non-critical COVID-19 patients. We conducted a multi-center retrospective cohort study of adults hospitalized with COVID-19 who were not intubated or did not die within 24 hours of admission. We derived and externally and longitudinally validated subphenotypes during the wild-type and delta severe-acute-respiratory-syndrome-coronavirus-2 (SARS-CoV2) waves via latent class analysis using clinical and laboratory data at hospital admission. We trained XGBoost machine learning models to predict subphenotype. We analyzed data for 4,827 hospitalized non-critical COVID-19 patients: 2,077 wild-type wave Columbia University Medical Center (CUMC) and affiliate hospital derivation cohort patients; 1,214 wild-type wave Cornell Medical Center and affiliate hospital external validation cohort patients; and 1,536 delta wave CUMC and affiliate hospital longitudinal validation cohort patients. A three-class latent class model best fit each cohort identifying subphenotypes that were low-inflammatory, intermediate-inflammatory, and high-inflammatory with fibrinolysis, with increasing 90-day risk of intubation or death across subphenotypes in the wild-type wave. However, in the delta wave, the intermediate-inflammatory subphenotype had the lowest 90-day risk of intubation or death. XGBoost model area under the receiver-operating-curve was 0.96 in the testing dataset, and biomarkers of inflammation and cardiorenal dysfunction were the strongest predictors of subphenotype. We identified three hospitalized non-critical COVID-19 subphenotypes that persisted through the wild-type and delta SARS-CoV2 waves. The intermediate-inflammatory subphenotype had the greatest relative improvement in intubation and survival over time with the standardized use of corticosteroids and other interventions. Our machine learning model can facilitate heterogeneity-of-treatment effect analyses of clinical trials of adults hospitalized with non-critical COVID-19.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 These authors co-primary authorship on this work. Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0316434