COVID-19 patients can be categorised into three clinical phenotypes

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In a new study, researchers identify three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications and clinical outcomes. The three phenotypes are described in a paper by authors Elizabeth Lusczek and Nicholas Ingraham of University of Minnesota Medical School, and colleagues.

COVID-19 has infected more than 18m people and led to more than 700,000 deaths around the world. Emergency department presentation varies widely, suggesting that distinct clinical phenotypes exist and, importantly, that these distinct phenotypic presentations may respond differently to treatment.

In the new study, researchers analysed electronic health records (EHRs) from 14 hospitals in the midwestern US and from 60 primary care clinics in the state of Minnesota. Data were available for 7,538 patients with PCR-confirmed COVID-19 between 7 March and 24 August, 2020; 1,022 of these patients required hospital admission and were included in the study. Data on each patient included comorbidities, medications, lab values, clinic visits, hospital admission information, and patient demographics.

Most patients included in the study (613 patients, or 60%) presented with what the researchers dubbed “phenotype II.” 236 patients (23.1%) presented with “phenotype I,” or the “Adverse phenotype,” which was associated with the worst clinical outcomes; these patients had the highest level of hematologic, renal and cardiac comorbidities (all p<0.001) and were more likely to be non-White and non-English speaking. 173 patients (16.9%) presented with “phenotype III,” or the “Favourable phenotype,” which was associated with the best clinical outcomes; surprisingly, despite having the lowest complication rate and mortality, patients in this group had the highest rate of respiratory comorbidities (p=0.002) as well as a 10% greater risk of hospital readmission compared to the other phenotypes. Overall, phenotypes I and II were associated with 7.30-fold (95% CI 3.11-17.17, p<0.001) and 2.57-fold (95% CI 1.10-6.00, p=0.03) increases in hazard of death relative to phenotype III.

The authors conclude that phenotype-specific medical care could improve COVID-19 outcomes and suggest that future research is needed to determine the utility of these findings in clinical practice.

The authors add: “Patients do not suffer from COVID-19 in a uniform matter. By identifying similarly affected groups, we not only improve our understanding of the disease process, but this enables us to precisely target future interventions to the highest risk patients.”

 

Study details
Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles

Elizabeth R Lusczek, Nicholas E Ingraham, Basil S Karam, Jennifer Proper, Lianne Siegel, Erika S Helgeson, Sahar Lotfi-Emran, Emily J Zolfaghari, Emma Jones, Michael G Usher, Jeffrey G Chipman, R Adams Dudley, Bradley Benson, Genevieve B Melton, Anthony Charles, Monica I Lupei, Christopher J Tignanelli

Published in PLOS One on 31 March 2021

Abstract
Purpose
Heterogeneity 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.
Methods
This 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.
Results
The 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.
Conclusion
We 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.

 

PLOS One study (Open access)


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