"Wrangling real-world data: Optimizing clinical research through factor" by Kerry A. Howard, Wes Anderson et al.
 

Wrangling real-world data: Optimizing clinical research through factor selection with LASSO regression

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, Advocate Health - MidwestFollow
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.

Affiliations

Advocate Christ Medical Center

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.

Document Type

Article

PubMed ID

40283693

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