In clinical trials of therapies for acute respiratory distress syndrome (ARDS), the average treatment effect in the study population may be attenuated because individual patient responses vary widely. This inflates sample size requirements and increases the cost and difficulty of conducting successful clinical trials. One solution is to enrich the study population with patients most likely to benefit, based on predicted patient response to treatment (predictive enrichment). In this perspective, we apply the precision medicine paradigm to the emerging use of extracorporeal CO2 removal (ECCO2R) for ultraprotective ventilation in ARDS. ECCO2R enables reductions in tidal volume and driving pressure, key determinants of ventilator-induced lung injury. Using basic physiological concepts, we demonstrate that dead space and static compliance determine the effect of ECCO2R on driving pressure and mechanical power. This framework might enable prediction of individual treatment responses to ECCO2R. Enriching clinical trials by selectively enrolling patients with a significant predicted treatment response can increase treatment effect size and statistical power more efficiently than conventional enrichment strategies that restrict enrollment according to the baseline risk of death. To support this claim, we simulated the predicted effect of ECCO2R on driving pressure and mortality in a preexisting cohort of patients with ARDS. Our computations suggest that restricting enrollment to patients in whom ECCO2R allows driving pressure to be decreased by 5 cm H2O or more can reduce sample size requirement by more than 50% without increasing the total number of patients to be screened. We discuss potential implications for trial design based on this framework.