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

Am J Respir Crit Care Med. 2016 Jul 15;194(2):147-55. doi: 10.1164/rccm.201512-2544CP.

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.

Publication types

  • Review
  • Research Support, N.I.H., Extramural

MeSH terms

  • Biomarkers
  • Clinical Trials as Topic / methods*
  • Humans
  • Prognosis
  • Research Design*
  • Respiratory Distress Syndrome, Adult / diagnosis*
  • Sepsis / diagnosis*

Substances

  • Biomarkers