Control of nocturnal hypoglycemia by CGM-based AI-enabled nocturnal hypoglycemia prediction: A retrospective analysis of real-world data

AIMS: To evaluate the effect of the Accu-Chek® SmartGuide Predict app Night Low Predict (NLP) feature use on nocturnal hypoglycemia in people with diabetes in a real-world setting. METHODS: This retrospective analysis used deidentified, aggregated data from Predict app users. Outcomes included hypoglycemic events of any severity (glucose < 70 mg/dL), level 2 (L2) events (glucose < 54 mg/dL), hyperglycemic events (glucose > 180 mg/dL), time below 70 mg/dL (TB70), time below 54 mg/dL (TB54), and time above 180 mg/dL (TAR). Users without nocturnal hypoglycemic events were excluded. Nights when predictions were received were compared with nights when predictions were not generated. RESULTS: Data from 249 people (aged 20-84 years) showed NLP use was associated with 20.0 % lower odds of hypoglycemic events of any severity occurring in a given night and 31.2 % lower odds of any TB70 occurring. Data from 159 people showed NLP use was associated with 31.0 % lower odds of L2 events occurring and 39.2 % lower odds of any TB54 occurring. Hyperglycemic events and TAR were not affected by NLP use. CONCLUSION: NLP use is associated with lower odds of nocturnal hypoglycemia in people with diabetes without raising odds of hyperglycemia, offering valuable self-management support.