  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.
