Characteristics associated with decreased or increased mortality risk from glycemic therapy among patients with type 2 diabetes and high cardiovascular risk:Machine learning analysis of the ACCORD trial

Abstract

OBJECTIVE Identifying patients who may experience decreased or increasedmortality risk from intensive glycemic therapy for type 2 diabetes remains an important clinical challenge. We sought to identify characteristics of patients at high cardiovascular risk with decreased or increased mortality risk fromglycemic therapy for type 2 diabetes using new methods to identify complex combinations of treatment effectmodifiers. RESEARCH DESIGN AND METHODS The machine learning method of gradient forest analysis was applied to understand the variation in all-causemortalitywithin the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (N = 10,251), whose participants were 40-79 years old with type 2 diabetes, hemoglobin A1c (HbA1c) ≥7.5% (58 mmol/mol), cardiovascular disease (CVD) or multiple CVD risk factors, and randomized to target HbA1c <6.0% (42 mmol/mol; intensive) or 7.0-7.9% (53-63 mmol/mol; standard). Covariates included demographics, BMI, hemoglobin glycosylation index (HGI; observed minus expected HbA1c derived from prerandomization fasting plasma glucose), other biomarkers, history, and medications. RESULTS The analysis identified four groups defined by age, BMI, and HGI with varied risk for mortality under intensive glycemic therapy. The lowest risk group (HGI <0.44, BMI <30 kg/m2, age <61 years) had an absolute mortality risk decrease of 2.3% attributable to intensive therapy (95% CI 0.2 to 4.5, P = 0.038; number needed to treat: 43), whereas the highest risk group (HGI ‡0.44) had an absolute mortality risk increase of 3.7% attributable to intensive therapy (95% CI 1.5 to 6.0; P < 0.001; number needed to harm: 27). CONCLUSIONS Age, BMI, and HGI may help individualize prediction of the benefit and harm from intensive glycemic therapy.