Toward Smarter Lumping and Smarter Splitting: Rethinking Strategies for Sepsis and Acute Respiratory Distress Syndrome Clinical Trial Design

Abstract

Both quality improvement and clinical research efforts over the past few decades have focused on consensus definition of sepsis and acute respiratory distress syndrome (ARDS). Although clinical definitions based on readily available clinical data have advanced recognition and timely use of broad supportive treatments, they likely hinder the identification of more targeted therapies that manipulate select biological mechanisms underlying critical illness. Sepsis and ARDS are by definition heterogeneous, and patients vary in both their underlying biology and their severity of illness. We have long been able to identify subtypes of sepsis and ARDS that confer different prognoses. The key is that we are now on the verge of identifying subtypes that may confer different response to therapy. In this perspective, inspired by a 2015 American Thoracic Society International Conference Symposium entitled "Lumpers and Splitters: Phenotyping in Critical Illness," we highlight promising approaches to uncovering patient subtypes that may predict treatment responsiveness and not just differences in prognosis. We then discuss how this information can be leveraged to improve the success and translatability of clinical trials by using predictive enrichment and other design strategies. Last, we discuss the challenges and limitations to identifying biomarkers and endotypes and incorporating them into routine clinical practice.

Keywords: acute respiratory distress syndrome; endotype; predictive enrichment; prognostic enrichment; sepsis.

Figure 1.

The relationship between predictive and prognostic enrichment strategies. The likelihood that a patient will have an outcome of interest (study endpoint) and the likelihood that a patient will respond to treatment operate on different axes, and both affect our ability to detect treatment benefit. Some biomarkers increase both the likelihood of having the study endpoint and the likelihood of responding to treatment. For example, lower Pa_O2/Fi_O2 (P/F) ratio identifies patients with a higher risk of dying from hypoxemic respiratory failure, and it also identifies patients with greater lung weight who are more likely to benefit from lung recruitment. Some biomarkers increase the likelihood of having a disease-related event but not the likelihood of responding to treatment. For example, preoperative prostate-specific antigen (PSA) velocity is a prognostic marker only; it is associated with a significantly elevated risk of death from prostate cancer but does not predict the likelihood of responding to a particular treatment. Some markers operate on both axes. Some markers may operate in different directions across the two axes. For example, a G551D mutation is associated with a milder clinical phenotype than F508del homozygotes, but it strongly predicts whether a patient will benefit from ivacaftor. ARDS = acute respiratory distress syndrome; ARR = absolute risk reduction; PEEP = positive end-expiratory pressure; RRR = relative risk reduction.