Unveiling Sub-Populations in Critical Care Settings: A Real-World Data Approach in COVID-19

Wesley Anderson ^1* Ruth Gould ² Namrata Patil ³ Nicholas Mohr ⁴ Kenneth Dodd ⁵ Danielle Boyce ^6,7 Pam Dasher ¹ Philippe Guerin ⁸ Reham Khan ⁹ Sreekanth Cheruku ¹⁰ Vishakha Kumar ⁹ Ewy Mathe ¹¹ Aneesh Mehta ¹² Andrew Michelson ¹³ Andrew Williams ⁶ Smith F Heavner ^1,14,15 Jagdeep Podichetty ¹

• ¹ Critical Path Institute, Tucson, United States
• ² Centers for Disease Control and Prevention (Georgia), Atlanta, Georgia
• ³ Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
• ⁴ The University of Iowa, Iowa City, Iowa, United States
• ⁵ Advocate Aurora Health, Milwaukee, Wisconsin, United States
• ⁶ Tufts University, Medford, Massachusetts, United States
• ⁷ School of Medicine, Johns Hopkins University, Baltimore, Maryland, United States
• ⁸ Infectious Diseases Data Observatory, University of Oxford, Oxford, England, United Kingdom
• ⁹ Society of Critical Care Medicine, Mount Prospect, Illinois, United States
• ¹⁰ University of Texas Southwestern Medical Center, Dallas, Texas, United States
• ¹¹ National Center for Advancing Translational Sciences (NIH), Bethesda, Maryland, United States
• ¹² Emory University, Atlanta, Georgia, United States
• ¹³ School of Medicine, Washington University in St. Louis, St. Louis, Missouri, United States
• ¹⁴ Department of Public Health Sciences, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, South Carolina, United States
• ¹⁵ Department of Biomedical Sciences, School of Medicine Greenville, University of South Carolina, Greenville, South Carolina, United States

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 inpatients 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.Conclusions: 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.