Using Artificial Intelligence to Assess Treatment-Effect Heterogeneity in Pragmatic Cardiovascular Trials: Insights from TRANSFORM-HF

Abstract

Machine learning analyses within this pragmatic trial revealed substantial heterogeneity in the survival effects of torsemide versus furosemide. Torsemide appeared more beneficial among patients without atrial fibrillation and with lower BNP/NT-proBNP levels, whereas furosemide was favored in those with atrial fibrillation, higher BNP/NT-proBNP concentrations, and prior loop diuretic use. Exploratory analyses demonstrated that applying AI-driven methods to evaluate treatment effect heterogeneity within a pragmatic trial framework can leverage the inherent clinical diversity of pragmatic designs and potentially generate personalized and practice-relevant insights from pragmatic clinical research.

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