tetano
Editor, Senior Moderator
PLoS One
. 2025 Mar 19;20(3):e0316434.
doi: 10.1371/journal.pone.0316434. eCollection 2025. Three hospitalized non-critical COVID-19 subphenotypes and change in intubation or death over time: A latent class analysis with external and longitudinal validation
William S Stringer[SUP] 1 [/SUP], Amy S Labar[SUP] 2 [/SUP], Joshua D Geleris[SUP] 1 [/SUP], Evan V Sholle[SUP] 3 [/SUP], David A Berlin[SUP] 3 [/SUP], Claire M McGroder[SUP] 1 [/SUP], Matthew J Cummings[SUP] 1 [/SUP], Max R O'Donnell[SUP] 1 [/SUP], Haoyang Yi[SUP] 4 [/SUP], Xuehan Yang[SUP] 4 [/SUP], Ying Wei[SUP] 4 [/SUP], Edward J Schenck[SUP] 3 [/SUP], Matthew R Baldwin[SUP] 1 [/SUP]
Affiliations
Background: 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.
Objective: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
. 2025 Mar 19;20(3):e0316434.
doi: 10.1371/journal.pone.0316434. eCollection 2025. Three hospitalized non-critical COVID-19 subphenotypes and change in intubation or death over time: A latent class analysis with external and longitudinal validation
William S Stringer[SUP] 1 [/SUP], Amy S Labar[SUP] 2 [/SUP], Joshua D Geleris[SUP] 1 [/SUP], Evan V Sholle[SUP] 3 [/SUP], David A Berlin[SUP] 3 [/SUP], Claire M McGroder[SUP] 1 [/SUP], Matthew J Cummings[SUP] 1 [/SUP], Max R O'Donnell[SUP] 1 [/SUP], Haoyang Yi[SUP] 4 [/SUP], Xuehan Yang[SUP] 4 [/SUP], Ying Wei[SUP] 4 [/SUP], Edward J Schenck[SUP] 3 [/SUP], Matthew R Baldwin[SUP] 1 [/SUP]
Affiliations
- PMID: 40106751
- PMCID: PMC11922525
- DOI: 10.1371/journal.pone.0316434
Background: 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.
Objective: 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.
Methods: 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.
Results: 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.
Conclusion: 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.