Evidence-based personalised medicine in critical care: a framework for quantifying and applying individualised...

Clinicians aim to provide treatments that will result in the best outcome for each patient. Ideally, treatment decisions are based on evidence from randomised clinical trials. Randomised trials conventionally report an aggregated difference in outcomes between patients in each group, known as an average treatment effect. However, the actual effect of treatment on outcomes (treatment response) can vary considerably between individuals, and can differ substantially from the average treatment effect. This variation in response to treatment between patients-heterogeneity of treatment effect-is particularly important in critical care because common critical care syndromes (eg, sepsis and acute respiratory distress syndrome) are clinically and biologically heterogeneous. Statistical approaches have been developed to analyse heterogeneity of treatment effect and predict individualised treatment effects for each patient. In this Review, we outline a framework for deriving and validating individualised treatment effects and identify challenges to applying individualised treatment effect estimates to inform treatment decisions in clinical care.