The efficacy of CGM driven machine learning algorithms in predicting hypoglycemia in patients with T1DM: a systematic review and meta-analysis.

PURPOSE: An important function of continuous glucose monitoring (CGM) is to alert individuals with type one diabetes mellitus (T1DM) to impending hypoglycemia, however, it lacks the ability to predict episodes beyond 30 min. Machine learning (ML) algorithms incorporating other contextual data can be used to overcome this deficiency. This study aims to quantitatively evaluate the diagnostic accuracy of these algorithms. METHODS: A systematic search of databases following PRISMA guidelines identified relevant studies that trained and assessed ML algorithms (PROSPERO CRD42024588619). The set of 2 × 2 data (i.e., number of true positives, false positives, true negatives, and false negatives) was extracted and meta-analyzed using a generalized linear mixed model to calculate pooled estimates of sensitivity and specificity and construct a summary receiver operating characteristic curve. A two-sided p-value of < 0.05 was deemed significant. RESULTS: Of 611 studies screened, 20 met the inclusion criteria. The pooled point estimates (95% CI) were 80% (71-87%), 89% (78-95%), 7.27 (3.96-14.20) and 0.25 (0.14-0.37) for sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR), respectively. CONCLUSIONS: Current ML algorithms have a substantial ability to predict hypoglycemia in patients with T1DM according to the Users' Guide to Medical Literature on diagnostic tests where PLR should be ≥ 5 and NLR should be ≤ 0.2 for moderate reliability. The incorporation of other inputs such as insulin, carbohydrates and physical activity have enhanced prediction accuracy. The clinical utility of these algorithms, however, should be evaluated as per the patient's daily hypoglycemic risk profile due to the moderate risk of false positives. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40200-025-01820-4.