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.

Anna R Kahkoska, Kushal S Shah, Michael R Kosorok, Kellee M Miller, Michael Rickels, Ruth S Weinstock, Laura A Young, Richard E Pratley

Journal: Journal of diabetes science and technology 2024;18(5):1079-1086

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.

METHOD

The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (<70 mg/dL), and the decision rule assigned participants to a subgroup reflecting the treatment estimated to minimize this outcome across all follow-up visits.

RESULTS

The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use.

CONCLUSIONS

The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia.

Address: Department of Nutrition, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.; UNC Center for Aging and Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.; Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.; Jaeb Center for Health Research, Tampa, FL, USA.; Rodebaugh Diabetes Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.; Division of Endocrinology, Diabetes, and Metabolism, SUNY Upstate Medical University, Syracuse, NY, USA.; Division of Endocrinology and Metabolism, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.; AdventHealth Translational Research Institute, Orlando, FL, USA.
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