A scalable application of Artificial Intelligence-Driven Insulin Titration Program to transform Type 2 Diabetes Management.

Abstract

Despite new pharmacotherapy, most patients with long-term Type 2 Diabetes are still hyperglycemic. This could have been solved by insulin with its unlimited potential efficacy, but its dynamic physiology demands frequent titrations which are overdemanding. This report provides a real-life account for a scalable transformation of diabetes care in a community-based endocrinology center by harnessing AI-based autonomous insulin titration. The center embedded the d-Nav® technology and its dedicated clinical support. Reported outcomes include treatment efficacy/safety in the first 600 patients and use of cardiorenal-risk reduction pharmacotherapy. Patients used d-Nav for 8.2±3.0 months with 82% retention. Age was 67.1±11.5 years and duration of diabetes was 19.8±11.0 years. During the last 3 years before d-Nav, HbA1c had been overall higher than 8% and at the beginning of the program it was as high as 8.6%±2.1% with 29.3% of the patients with HbA1c>9%. With d-Nav, HbA1c decreased to 7.3%±1.2% with 5.7% of patients with HbA1c>9%. During the first 3 months, d-Nav reduced total daily dose of insulin in 1 of every 5 patients due to relatively low glucose levels to minimize the risk of hypoglycemia. GLP-1 or dual GLP-1 and GIP receptor agonists were prescribed in about a half of the patients and SGLT2 inhibitor in a third. The frequency of hypoglycemia (<54mg/dl) was 0.4±0.6/month and severe hypoglycemia 1.7/100-patient-years. The use of d-Nav allowed for improvement in overall diabetes management with appropriate use of both insulin and non-insulin pharmacologic agents in a scalable way.