Identifying and Analyzing Sepsis States: A Retrospective Study on Patients with Sepsis in ICUs

TL;DR

This study develops a computational framework to identify six distinct sepsis states using clinical data, revealing significant demographic and health profile differences among these states, which could inform future clinical strategies.

Contribution

The paper introduces a novel framework for classifying sepsis into six states based on clinical variables, enhancing understanding of disease heterogeneity.

Findings

Six distinct sepsis states identified.

Significant demographic differences across states.

Framework provides a basis for future clinical trials.

Abstract

Sepsis accounts for more than 50% of hospital deaths, and the associated cost ranks the highest among hospital admissions in the US. Improved understanding of disease states, severity, and clinical markers has the potential to significantly improve patient outcomes and reduce cost. We develop a computational framework that identifies disease states in sepsis using clinical variables and samples in the MIMIC-III database. We identify six distinct patient states in sepsis, each associated with different manifestations of organ dysfunction. We find that patients in different sepsis states are statistically significantly composed of distinct populations with disparate demographic and comorbidity profiles. Collectively, our framework provides a holistic view of sepsis, and our findings provide the basis for future development of clinical trials and therapeutic strategies for sepsis.