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Heterogeneity in COVID-19 Patients at Multiple Levels of Granularity: From Biclusters to Clinical Interventions

Suresh K Bhavnani1,2, Erich Kummerfeld3, Weibin Zhang1

  • 1Preventive Medicine and Population Health.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|August 30, 2021
PubMed

Insights

COVID-19 patients with pre-existing conditions face worse outcomes. This study uses network analysis to identify distinct patient subgroups based on co-occurring comorbidities, enabling more targeted clinical care strategies.

Area of Science:

  • Medical Informatics
  • Network Science
  • Epidemiology

Background:

  • COVID-19 disproportionately affects older adults and minorities with comorbidities.
  • Significant heterogeneity exists in comorbidity profiles, yet patterns of co-occurrence are poorly understood.
  • Understanding comorbidity patterns is crucial for developing targeted COVID-19 care strategies.

Purpose of the Study:

  • To quantitatively and visually analyze heterogeneity in comorbidity profiles among COVID-19 inpatients.
  • To identify distinct patient subgroups based on co-occurring comorbidities.
  • To inform the design of targeted clinical interventions for COVID-19 patients.

Main Methods:

  • Utilized bipartite networks to analyze comorbidity data from electronic health records.
  • Examined data from 12 hospitals and 60 clinics in the Minneapolis region.
  • Employed a multigranular approach to analyze heterogeneity at cohort, subgroup, and patient levels.

Main Results:

  • Successfully visualized and quantified heterogeneity in COVID-19 patient comorbidity profiles.
  • Identified distinct patient subgroups based on co-occurring conditions.
  • Demonstrated a framework for rapid translation of findings into clinical interventions.

Conclusions:

  • Bipartite network analysis effectively reveals complex comorbidity patterns in COVID-19 patients.
  • The multigranular framework facilitates targeted clinical care design.
  • This approach can be extended to analyze other biomedical phenomena for accelerated clinical translation.

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