Heterogeneous treatment effects of intensive glycemic control on major adverse cardiovascular events in the ACCORD and VADT trials: a machine-learning analysis

• Published in: Cardiovascular diabetology Vol. 21; no. 1; p. 58
• Main Authors: Edward, Justin A, Josey, Kevin, Bahn, Gideon, Caplan, Liron, Reusch, Jane E B, Reaven, Peter, Ghosh, Debashis, Raghavan, Sridharan
• Format: Journal Article
• Language: English
• Published: England BioMed Central 27.04.2022; BMC
• Subjects: Blood Glucose; Body mass index; Cardiovascular disease; Cardiovascular diseases; Cardiovascular Diseases - epidemiology; Cardiovascular Diseases - prevention & control; Cardiovascular system; Cholesterol; Clinical trials; Clinical Trials as Topic; Coronary vessels; Decision trees; Diabetes; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - drug therapy; Glomerular filtration rate; Glycemic Control; Glycosylation; Hemoglobin; Heterogeneity; Humans; Hypotheses; Learning algorithms; Machine Learning; Mortality; Patients; Risk Factors; Subgroup effects; Type 2 diabetes; Variables; Glycemic control; Type 2 diabetes; Heterogeneity; Subgroup effects; Machine learning
• ISSN: 1475-2840; 1475-2840
• DOI: 10.1186/s12933-022-01496-7

Evidence to guide type 2 diabetes treatment individualization is limited. We evaluated heterogeneous treatment effects (HTE) of intensive glycemic control in type 2 diabetes patients on major adverse cardiovascular events (MACE) in the Action to Control Cardiovascular Risk in Diabetes Study (ACCORD) and the Veterans Affairs Diabetes Trial (VADT). Causal forests machine learning analysis was performed using pooled individual data from two randomized trials (n = 12,042) to identify HTE of intensive versus standard glycemic control on MACE in patients with type 2 diabetes. We used variable prioritization from causal forests to build a summary decision tree and examined the risk difference of MACE between treatment arms in the resulting subgroups. A summary decision tree used five variables (hemoglobin glycation index, estimated glomerular filtration rate, fasting glucose, age, and body mass index) to define eight subgroups in which risk differences of MACE ranged from - 5.1% (95% CI - 8.7, - 1.5) to 3.1% (95% CI 0.2, 6.0) (negative values represent lower MACE associated with intensive glycemic control). Intensive glycemic control was associated with lower MACE in pooled study data in subgroups with low (- 4.2% [95% CI - 8.1, - 1.0]), intermediate (- 5.1% [95% CI - 8.7, - 1.5]), and high (- 4.3% [95% CI - 7.7, - 1.0]) MACE rates with consistent directions of effect in ACCORD and VADT alone. This data-driven analysis provides evidence supporting the diabetes treatment guideline recommendation of intensive glucose lowering in diabetes patients with low cardiovascular risk and additionally suggests potential benefits of intensive glycemic control in some individuals at higher cardiovascular risk.