Identification of Endotypes of Hospitalized COVID-19 Patients

Benjamin L. Ranard, Murad Megjhani, Kalijah Terilli, Kevin Doyle, Jan Claassen, Michael R. Pinsky, Gilles Clermont, Yoram Vodovotz, Shadnaz Asgari, Soojin Park*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Background: Characterization of coronavirus disease 2019 (COVID-19) endotypes may help explain variable clinical presentations and response to treatments. While risk factors for COVID-19 have been described, COVID-19 endotypes have not been elucidated. Objectives: We sought to identify and describe COVID-19 endotypes of hospitalized patients. Methods: Consensus clustering (using the ensemble method) of patient age and laboratory values during admission identified endotypes. We analyzed data from 528 patients with COVID-19 who were admitted to telemetry capable beds at Columbia University Irving Medical Center and discharged between March 12 to July 15, 2020. Results: Four unique endotypes were identified and described by laboratory values, demographics, outcomes, and treatments. Endotypes 1 and 2 were comprised of low numbers of intubated patients (1 and 6%) and exhibited low mortality (1 and 6%), whereas endotypes 3 and 4 included high numbers of intubated patients (72 and 85%) with elevated mortality (21 and 43%). Endotypes 2 and 4 had the most comorbidities. Endotype 1 patients had low levels of inflammatory markers (ferritin, IL-6, CRP, LDH), low infectious markers (WBC, procalcitonin), and low degree of coagulopathy (PTT, PT), while endotype 4 had higher levels of those markers. Conclusions: Four unique endotypes of hospitalized patients with COVID-19 were identified, which segregated patients based on inflammatory markers, infectious markers, evidence of end-organ dysfunction, comorbidities, and outcomes. High comorbidities did not associate with poor outcome endotypes. Further work is needed to validate these endotypes in other cohorts and to study endotype differences to treatment responses.

Original languageEnglish
Article number770343
JournalFrontiers in Medicine
Volume8
DOIs
StatePublished - 11 Nov 2021
Externally publishedYes

Keywords

  • COVID-19
  • cluster analysis
  • endotype
  • machine learning
  • phenotype
  • survival
  • treatment

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