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...

Full description

Saved in:
Bibliographic Details
Published in:PloS one Vol. 20; no. 3; p. e0316434
Main Authors: 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.
Format: Journal Article
Language:English
Published: United States Public Library of Science 19.03.2025
Public Library of Science (PLoS)
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography: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