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

Kerry A Howard1,2, Wes Anderson3, Jagdeep T Podichetty3

  • 1Department of Public Health Sciences, Clemson University, Clemson, SC 29634, USA.

International Journal of Environmental Research and Public Health
|April 26, 2025
PubMed
Summary
This summary is machine-generated.

Utilizing the CURE ID platform, researchers identified key predictors of COVID-19 mortality, including age and comorbidities. This data accelerates drug-repurposing research for infectious diseases.

Keywords:
clinical researchfactor selectionleast absolute shrinkage and selection operator (LASSO) regressionreal-world data

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Area of Science:

  • Clinical Research
  • Infectious Diseases
  • Drug Discovery

Background:

  • Data-driven clinical research is vital for infectious disease understanding and treatment.
  • Challenges like data validity, collaboration, and rare diseases impede research progress.
  • Innovative methods are needed to organize, accelerate, and share clinical data for research.

Purpose of the Study:

  • To investigate the CURE ID platform's potential for accelerating drug-repurposing research.
  • To use COVID-19 as a case study for identifying mortality predictors.
  • To demonstrate the value of shared, robust datasets in prognostic research.

Main Methods:

  • Analysis of data from eight US health systems.
  • Application of least absolute shrinkage and selection operator (LASSO) regression.
  • Identification of key predictors of 28-day all-cause mortality in COVID-19 patients.

Main Results:

  • Key predictors of mortality included age, laboratory measures, illness severity, oxygen support, and comorbidities.
  • Findings align with previous studies on COVID-19 mortality factors.
  • The study highlights the significance of factor selection in identifying crucial variables.

Conclusions:

  • Collaborative repositories like CURE ID provide valuable datasets for prognostic research.
  • Streamlining research and drug-repurposing efforts is achievable through organized data sharing.
  • Identifying key mortality predictors aids future research and therapeutic development.