From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations for Developing Precision Medicine in the ICU

Abstract

Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. To impact clinical care, identification of subpopulations must do more than differentiate prognosis. It must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway, but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry, and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields.

Keywords: acute kidney injury; brain injury; precision medicine; respiratory distress syndrome; sepsis.

Figure 1.

Mediation analysis in a randomized controlled trial. In a randomized controlled trial, mediation analysis may be used to validate a surrogate endpoint by separating the total treatment effect of an intervention on an outcome into direct and indirect effects through a mediator. The direct effect (c) is the effect of the treatment on the outcome, independent of the effect of any mediating variable. The indirect, or mediation, effect (b) quantifies whether and how much of the effect of the treatment on the outcome goes through, or is mediated by, the surrogate endpoint. The total effect (d) is the effect of the treatment on the outcome while ignoring, or not adjusting for, the mediator variable. Path a and total effect (a × b + c = d) are assumed to be free of confounding because of randomization. However, path b may contain confounders because both the mediator and outcome are outcomes of randomization (108, 109), which may be important in explaining the mediating pathway (110). Adapted from Reference . This figure provides an illustrative and simplified mediation framework. More complex model construction is likely necessary to disentangle complex longitudinal relationships over time (112, 113). SOFA = Sequential Organ Failure Assessment.

Figure 2.

Proposed paradigm for iterative forward and reverse translation for studies in precision medicine. RCTs = randomized controlled trials.