ArticlePloS one2021
Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pathophysiological Mechanisms in Long COVID: A Mixed Method Systematic Review.International journal of environmental research and public health · 2024Pooled it
- Association between red blood cell distribution width and mortality and severity among patients with COVID-19: A systematic review and meta-analysis.Journal of medical virology · 2021Pooled it
- Efficacy of Losartan in Hospitalized Patients With COVID-19-Induced Lung Injury: A Randomized Clinical Trial.JAMA network open · 2022Trial
- Integrating Host Genetics and Clinical Setting in Machine Learning Models: Predicting COVID-19 Prognosis for Healthcare Decision-Making (The FeMiNa Study).Diagnostics (Basel, Switzerland) · 2026Article
- Concordance Between Survey and Electronic Health Record Data in the COVID-19 Citizen Science Study: Retrospective Cohort Analysis.JMIR formative research · 2025Article
- Unsupervised machine learning clustering approach for hospitalized COVID-19 pneumonia patients.BMC pulmonary medicine · 2025Article
- Clinical phenotyping uncovers heterogeneous associations between corticosteroid treatment and survival in critically ill COVID-19 patients.Intensive care medicine · 2024Article
- Article
- Feasibility characteristics of wrist-worn fitness trackers in health status monitoring for post-COVID patients in remote and rural areas.PLOS digital health · 2024Article
- Inflammatory Biomarkers for Assessing In-Hospital Mortality Risk in Severe COVID-19-A Retrospective Study.Journal of personalized medicine · 2024Article
- Cardiac macrophages in maintaining heart homeostasis and regulating ventricular remodeling of heart diseases.Frontiers in immunology · 2024Review
- External validation of unsupervised COVID-19 clinical phenotypes and their prognostic impact.Annals of medicine · 2023Article
- COVID-19 subphenotypes at hospital admission are associated with mortality: a cross-sectional study.Annals of medicine · 2023Article
- An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model.Human genomics · 2023Article
- Inhaled Nitric Oxide in Acute Respiratory Distress Syndrome Subsets: Rationale and Clinical Applications.Journal of aerosol medicine and pulmonary drug delivery · 2023Review
- Identification of Distinct Clinical Phenotypes of Critically Ill COVID-19 Patients: Results from a Cohort Observational Study.Journal of clinical medicine · 2023Article
- Clinical and temporal characterization of COVID-19 subgroups using patient vector embeddings of electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Interpretable clinical phenotypes among patients hospitalized with COVID-19 using cluster analysis.Frontiers in digital health · 2023Article
- Endometrial gene expression differences in women with coronavirus disease 2019.Fertility and sterility · 2022Article
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17 authors.
Funding
Abstract
purposeHeterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.
methodsThis is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes.
resultsThe database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III.
conclusionWe identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.
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