Unveiling sub-populations in critical care settings: a real-world data approach in COVID-19

Wesley Anderson; Ruth Gould; Namrata Patil; Nicholas Mohr; Kenneth Dodd; Danielle Boyce; Pam Dasher; Philippe J. Guerin; Reham Khan; Sreekanth Cheruku; Vishakha K. Kumar; Ewy Mathé; Aneesh K. Mehta; Andrew P. Michelson; Andrew Williams; Smith F. Heavner; Jagdeep T. Podichetty

doi:10.3389/fpubh.2025.1544904

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Unveiling sub-populations in critical care settings: a real-world data approach in COVID-19

Journal article

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Unveiling sub-populations in critical care settings: a real-world data approach in COVID-19

Wesley Anderson, Ruth Gould, Namrata Patil, Nicholas Mohr, Kenneth Dodd, Danielle Boyce, Pam Dasher, Philippe J. Guerin, Reham Khan, Sreekanth Cheruku, …

Frontiers in public health, Vol.13, 1544904

05/15/2025

DOI: 10.3389/fpubh.2025.1544904

PMCID: PMC12119499

PMID: 40443932

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Abstract

Background: Disease presentation and progression can vary greatly in heterogeneous diseases, such as COVID-19, with variability in patient outcomes, even within the hospital setting. This variability underscores the need for tailored treatment approaches based on distinct clinical subgroups. Objectives: This study aimed to identify COVID-19 patient subgroups with unique clinical characteristics using real-world data (RWD) from electronic health records (EHRs) to inform individualized treatment plans. Materials and methods: A Factor Analysis of Mixed Data (FAMD)-based agglomerative hierarchical clustering approach was employed to analyze the real-world data, enabling the identification of distinct patient subgroups. Statistical tests evaluated cluster differences, and machine learning models classified the identified subgroups. Results: Three clusters of COVID-19 in patients with unique clinical characteristics were identified. The analysis revealed significant differences in hospital stay durations and survival rates among the clusters, with more severe clinical features correlating with worse prognoses and machine learning classifiers achieving high accuracy in subgroup identification. Conclusion: By leveraging RWD and advanced clustering techniques, the study provides insights into the heterogeneity of COVID-19 presentations. The findings support the development of classification models that can inform more individualized and effective treatment plans, improving patient outcomes in the future.

real-world data

clustering analysis

factor analysis of mixed data

classification

critical care

Details

Title: Subtitle: Unveiling sub-populations in critical care settings: a real-world data approach in COVID-19
Creators: Wesley Anderson
Ruth Gould
Namrata Patil
Nicholas Mohr
Kenneth Dodd
Danielle Boyce
Pam Dasher
Philippe J. Guerin
Reham Khan
Sreekanth Cheruku
Vishakha K. Kumar
Ewy Mathé
Aneesh K. Mehta
Andrew P. Michelson
Andrew Williams
Smith F. Heavner
Jagdeep T. Podichetty
Resource Type: Journal article
Publication Details: Frontiers in public health, Vol.13, 1544904
DOI: 10.3389/fpubh.2025.1544904
PMID: 40443932
PMCID: PMC12119499
NLM abbreviation: Front Public Health
ISSN: 2296-2565
eISSN: 2296-2565
Publisher: Frontiers Media S.A.
Grant note: Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health: 1ZIATR000056-07 Food and Drug Administration (FDA) of the U.S. Department of Health and Human Services (HHS) - Office of the Secretary Patient-Centered Outcomes Research Trust Fund: 75F40121S35006 Office of Secretary Patient-Centered Outcomes Research Trust
The authors gratefully acknowledge the contributions and leadership of Heather Stone, MPH, and Leonard Sacks, MD.
Language: English
Date published: 05/15/2025
Academic Unit: Epidemiology; Emergency Medicine; Anesthesia; Injury Prevention Research Center
Record Identifier: 9984824294302771

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