Heterogeneous treatment effects of coronary artery bypass grafting in ischemic cardiomyopathy: A machine learning

Zhuoming Zhou1, Bohao Jian1, Xuanyu Chen2

  • 1Department of Cardiac Surgery, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

Insights

Coronary artery bypass grafting offers survival benefits for younger ischemic cardiomyopathy patients with severe left ventricular enlargement. Machine learning identified specific patient subgroups who benefit most from this procedure over medical therapy alone.

Area of Science:

  • Cardiology
  • Surgical Interventions
  • Medical Therapy

Background:

  • Ischemic cardiomyopathy presents complex treatment challenges.
  • Coronary artery bypass grafting (CABG) is a potential intervention.
  • Identifying optimal candidates for CABG versus medical therapy is crucial.

Purpose of the Study:

  • To evaluate heterogeneous treatment effects of CABG in ischemic cardiomyopathy patients.
  • To identify patient subgroups benefiting more from CABG compared to medical therapy alone.

Main Methods:

  • Utilized machine learning causal forest modeling on data from the Surgical Treatment for Ischemic Heart Failure trial.
  • Assessed risks of all-cause and cardiovascular death for CABG versus medical therapy in identified subgroups.

Main Results:

  • Left ventricular end-systolic volume index, serum creatinine, and age identified heterogeneous treatment effects.
  • Patients with LVESVI > 84 mL/m² and age ≤ 60.27 years showed significantly lower all-cause and cardiovascular death risk with CABG.
  • Benefits diminished in patients with LVESVI ≤ 84 mL/m² and serum creatinine ≤ 1.04 mg/dL, or LVESVI > 84 mL/m² and age > 60.27 years.

Conclusions:

  • Post hoc analysis of the Surgical Treatment for Ischemic Heart Failure trial revealed heterogeneous treatment effects for CABG in ischemic cardiomyopathy.
  • Younger patients with severe left ventricular enlargement are more likely to experience greater survival benefits from CABG.
Abstract

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