Enriching patient populations in ICU trials: reducing heterogeneity through machine learning

Despite the pivotal role of randomized controlled trials (RCTs) in critical care research, many have failed to demonstrate significant benefits, particularly in nutrition interventions. This review highlights how patient heterogeneity affects trial outcomes and explores how artificial intelligence and machine learning can address this issue by identifying subgroups with distinct treatment responses, improving trial design, and enhancing the precision of nutritional interventions.

RCTs estimate the average treatment effect, which can obscure heterogeneous treatment effects, where some patients benefit while others experience no effect or harm. Recent studies highlight that artificial intelligence techniques such as clustering, predictive modeling, causal artificial intelligence, and reinforcement learning have the potential to individualize treatments and decrease heterogeneity in trials. Digital twins and artificial intelligence-driven adaptive trial designs further enable personalized interventions, optimizing study populations and improving treatment precision.

The integration of artificial intelligence and machine learning into clinical trials offers a powerful strategy to refine patient selection, reduce variability, and enhance the detection of meaningful treatment effects. These advancements hold significant potential to transform critical care nutrition research, leading to more precise, personalized, and effective interventions.