A Bayesian Adaptive Platform Trial for Evaluating Mechanical Ventilation Strategies Using Patient-Centered Endpoints in Heterogeneous ICU Populations: SMART-VENT

Abstract

Introduction Acute respiratory distress syndrome (ARDS) and acute respiratory failure exhibit profound heterogeneity in clinical presentation, lung mechanics, and individual response to intensive care treatments. Traditional fixed-design randomized trials are poorly tailored to evaluate the efficacy of dynamic, multistage therapeutic strategies, failing to account for both the sequential nature of clinical decision-making and the underlying biological heterogeneity of patients.

Objectives To introduce SMART-Vent, an innovative design that embeds a Sequential Multiple Assignment Randomized Trial (SMART) within a Bayesian adaptive platform. The objective is to optimize phenotype-driven mechanical ventilation sequences in the intensive care unit, addressing the heterogeneity of treatment effects in an efficient and ethically sound manner.

Methods A Monte Carlo simulation engine was developed to evaluate the operating characteristics of the SMART-Vent design across complex clinical scenarios with total sample sizes ranging from N=200 to N=1500. Simulated patients were stratified into two primary clinical phenotypes (ARDS and non-ARDS) and assigned to a two-stage ventilatory pathway (first-line therapies, and rescue strategies for non-responders). The statistical framework relied on Bayesian hierarchical models estimated via MCMC algorithms. The design incorporated Response-Adaptive Randomization (RAR) to dynamically update allocation weights and a decision rule driven by a 95% Highest Density Interval (HDI). Performance metrics were benchmarked directly against a standard, multiplicity-adjusted frequentist design.

Results Simulations demonstrated that SMART-Vent significantly outperforms frequentist benchmarks in identifying optimal dynamic treatment regimes. Utilizing the 95% HDI decision rule successfully circumvented the severe sample-size inflation imposed by classical $\alpha$-error corrections, achieving target statistical power with smaller sample cohorts. Despite sequential interim analyses and adaptive allocation, the Bayesian False Discovery Rate (FDR) remained inherently self-calibrated and strictly bound around the nominal 5% threshold across all strata. From an ethical perspective, the RAR algorithm optimized patient care, progressively allocating up to 80% of responsive subjects to the truly superior ventilatory strategy. Sensitivity analyses confirmed that incorporating informative prior distributions accelerated early signal detection in initially underpowered subgroups (e.g., ARDS non-responders) without compromising the data-driven asymptotic integrity of the model.

Conclusions By seamlessly merging SMART methodology with a Bayesian adaptive platform, the SMART-Vent design maximizes statistical efficiency, maintains rigorous control over false-positive rates, and dynamically minimizes patient exposure to sub-optimal ventilatory pathways. This framework provides a methodologically robust and ethically grounded solution for conducting future precision medicine trials and Complex Innovative Designs (CID) in critical care.