Wrangling Real-World Data: Optimizing Clinical Research Through Factor Selection with LASSO Regression

Kerry A. Howard, Wes Anderson (), Jagdeep T. Podichetty, Ruth Gould, Danielle Boyce, Pam Dasher, Laura Evans, Cindy Kao, Vishakha K. Kumar, Chase Hamilton, Ewy Mathé, Philippe J. Guerin, Kenneth Dodd, Aneesh K. Mehta, Chris Ortman, Namrata Patil, Jeselyn Rhodes, Matthew Robinson, Heather Stone and Smith F. Heavner
Additional contact information
Kerry A. Howard: Department of Public Health Sciences, Clemson University, Clemson, SC 29634, USA
Wes Anderson: Critical Path Institute, Tucson, AZ 85718, USA
Jagdeep T. Podichetty: Critical Path Institute, Tucson, AZ 85718, USA
Ruth Gould: Centers of Disease Control and Prevention, Atlanta, GA 30329, USA
Danielle Boyce: Tufts University School of Medicine, Tufts University, Medford, MA 02155, USA
Pam Dasher: Critical Path Institute, Tucson, AZ 85718, USA
Laura Evans: Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA 98195, USA
Cindy Kao: IR Research & Academic Systems, University of Texas Southwestern, Dallas, TX 75390, USA
Vishakha K. Kumar: Society of Critical Care Medicine, Mount Prospect, IL 60056, USA
Chase Hamilton: Society of Critical Care Medicine, Mount Prospect, IL 60056, USA
Ewy Mathé: National Institutes of Health National Center for Advancing Translational Sciences (NCATS), Rockville, MD 20850, USA
Philippe J. Guerin: Infectious Diseases Data Observatory (IDDO), Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire OX3 LF, UK
Kenneth Dodd: Department of Emergency Medicine, Advocate Christ Medical Center, Oak Lawn, IL 60453, USA
Aneesh K. Mehta: Department of Medicine, Emory University, Atlanta, GA 30322, USA
Chris Ortman: Institute for Translational and Clinical Science, University of Iowa, Iowa City, IA 52242, USA
Namrata Patil: Brigham and Women’s Hospital, Boston, MA 02115, USA
Jeselyn Rhodes: Department of Medicine, Emory University, Atlanta, GA 30322, USA
Matthew Robinson: Division of Infectious Diseases, Johns Hopkins University, Baltimore, MD 21205, USA
Heather Stone: US Food and Drug Administration, Silver Spring, MD 20993, USA
Smith F. Heavner: Department of Public Health Sciences, Clemson University, Clemson, SC 29634, USA

IJERPH, 2025, vol. 22, issue 4, 1-13

Abstract: Data-driven approaches to clinical research are necessary for understanding and effectively treating infectious diseases. However, challenges such as issues with data validity, lack of collaboration, and difficult-to-treat infectious diseases (e.g., those that are rare or newly emerging) hinder research. Prioritizing innovative methods to facilitate the continued use of data generated during routine clinical care for research, but in an organized, accelerated, and shared manner, is crucial. This study investigates the potential of CURE ID, an open-source platform to accelerate drug-repurposing research for difficult-to-treat diseases, with COVID-19 as a use case. Data from eight US health systems were analyzed using least absolute shrinkage and selection operator (LASSO) regression to identify key predictors of 28-day all-cause mortality in COVID-19 patients, including demographics, comorbidities, treatments, and laboratory measurements captured during the first two days of hospitalization. Key findings indicate that age, laboratory measures, severity of illness indicators, oxygen support administration, and comorbidities significantly influenced all-cause 28-day mortality, aligning with previous studies. This work underscores the value of collaborative repositories like CURE ID in providing robust datasets for prognostic research and the importance of factor selection in identifying key variables, helping to streamline future research and drug-repurposing efforts.

Keywords: clinical research; real-world data; factor selection; least absolute shrinkage and selection operator (LASSO) regression (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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