Darpit Dave 1 , Madhav Erraguntla 1 , Mark Lawley 1 , Daniel DeSalvo 2 , Balakrishna Haridas 3 , Siripoom McKay 2 , Chester Koh 4,5 Show Affiliations »
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 patient- and 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. ©Darpit Dave, Madhav Erraguntla, Mark Lawley, Daniel DeSalvo, Balakrishna Haridas, Siripoom McKay, Chester Koh. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 29.04.2021.
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 patient - and 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. ©Darpit Dave, Madhav Erraguntla, Mark Lawley, Daniel DeSalvo, Balakrishna Haridas, Siripoom McKay, Chester Koh. Originally published in JMIR Diabetes (https://diabetes .jmir.org), 29.04.2021.
Entities: Chemical
Disease
Gene
Species
Keywords:
continuous glucose monitoring; diabetes; false alert rate; glucose monitoring; hypoglycemia; machine learning; model generalizability; quantile regression forests; sustained hypoglycemia
Year: 2021
PMID： 33913816 DOI： 10.2196/26909
Source DB: PubMed Journal: JMIR Diabetes ISSN： 2371-4379