Predictive Modeling of Hypoglycemia Risk with Basal Insulin Use in Type 2 Diabetes: Use of Machine Learning in the LIGHTNING Study

INTRODUCTION: Hypoglycemia remains a global burden and a limiting factor in the glycemic management of people with diabetes using basal insulins or oral antihyperglycemic drugs. Hypoglycemia data gleaned from randomized controlled trials (RCTs) have limited generalizability, as the strict RCT methodology and inclusion criteria do not fully reflect the real-world clinical picture. Therefore, real-world evidence, gathered from sources including electronic health records (EHR), is increasingly recognized as an important adjunct to RCTs. AIMS AND METHODS: The LIGHTNING study applied advanced analytical methods, including machine learning (ML), to EHR data. The study aimed to predict hypoglycemic event rates in patients with type 2 diabetes (T2DM) receiving different basal insulin treatments to identify potential subgroups of patients who are at lower risk of hypoglycemia when treated with one basal insulin compared with another and to predict hypoglycemia-related cost savings in these subgroups. Here we provide an overview of the objectives, study design and methods, and validation approaches used in the LIGHTNING study. CONCLUSION: It is hoped that results of the LIGHTNING study will help facilitate real-world clinical decision-making in addition to providing a clinically relevant predictive model of hypoglycemia risk. FUNDING: Sanofi.