Improved low-glucose predictive alerts based on sustained hypoglycemia: Model development and validation study

Darpit Dave, Madhav Erraguntla, Mark Lawley, Daniel DeSalvo, Balakrishna Haridas, Siripoom McKay, Chester Koh

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Predictive alerts for impending hypoglycemic events enable persons with type 1 diabetes to take preventive actions and avoid serious consequences. Objective: This study aimed to develop a prediction model for hypoglycemic events with a low false alert rate, high sensitivity and specificity, and good generalizability to new patients and time periods. Methods: Performance improvement by focusing on sustained hypoglycemic events, defined as glucose values less than 70 mg/dL for at least 15 minutes, was explored. Two different modeling approaches were considered: (1) a classification-based method to directly predict sustained hypoglycemic events, and (2) a regression-based prediction of glucose at multiple time points in the prediction horizon and subsequent inference of sustained hypoglycemia. To address the generalizability and robustness of the model, two different validation mechanisms were considered: (1) patient-based validation (model performance was evaluated on new patients), and (2) time-based validation (model performance was evaluated on new time periods). Results: This study utilized data from 110 patients over 30-90 days comprising 1.6 million continuous glucose monitoring values under normal living conditions. The model accurately predicted sustained events with >97% sensitivity and specificity for both 30- and 60-minute prediction horizons. The false alert rate was kept to <25%. The results were consistent across patientand time-based validation strategies. Conclusions: Providing alerts focused on sustained events instead of all hypoglycemic events reduces the false alert rate and improves sensitivity and specificity. It also results in models that have better generalizability to new patients and time periods.

Original languageEnglish (US)
Article numbere26909
JournalJMIR Diabetes
Volume6
Issue number2
DOIs
StatePublished - Apr 2021

Keywords

  • Continuous glucose monitoring
  • Diabetes
  • False alert rate
  • Glucose monitoring
  • Hypoglycemia
  • Machine learning
  • Model generalizability
  • Quantile regression forests
  • Sustained hypoglycemia

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Health Informatics
  • Health Information Management
  • Biomedical Engineering

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