A Scalable Application of Artificial Intelligence-Driven Insulin Titration Program to Transform Type 2 Diabetes Management

Abstract

Background:

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 artificial intelligence-based autonomous insulin titration.

Methods:

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.

Findings:

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, glycated hemoglobin (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 one of every five patients due to relatively low glucose levels to minimize the risk of hypoglycemia. Glucagon like peptide 1 (GLP-1) receptor agonists or dual GLP-1 and Glucose-dependent insulinotropic polypeptide (GIP) receptor agonists were prescribed in about a half of the patients and sodium glucose cotransporter 2 inhibitor in a third. The frequency of hypoglycemia (<54 mg/dL) was 0.4 ± 0.6/month and severe hypoglycemia 1.7/100-patient-years.

Interpretation:

The use of d-Nav allowed for improvement in overall diabetes management with appropriate use of both insulin and noninsulin pharmacologic agents in a scalable way.