Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study.

Journal: Journal of diabetes science and technology
PMID: 36629330

Abstract

BACKGROUND: The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time <70 mg/dL for each individual WISDM participant.

Authors

  • Anna R Kahkoska
    Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina.
  • Kushal S Shah
    Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Michael R Kosorok
    Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC.
  • Kellee M Miller
    Jaeb Center for Health Research, Tampa, FL, USA.
  • Michael Rickels
    Rodebaugh Diabetes Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
  • Ruth S Weinstock
    Division of Endocrinology, Diabetes, and Metabolism, SUNY Upstate Medical University, Syracuse, NY, USA.
  • Laura A Young
    Division of Endocrinology and Metabolism, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Richard E Pratley
    AdventHealth Translational Research Institute, Orlando, FL, USA.

Keywords

External Resources

Popular Topics
Recent Journals