OBJECTIVES

: Despite substantial morbidity, mortality, and economic burden due to length of hospitalization (LOH) for sepsis patients, little is known about actionable variables to minimize its impact. This study aimed to identify unique patient clusters and assess feature’s influence on LOH.

METHODS

: Patients (≥18 years) with sepsis discharge diagnoses (ICD-9 or 10 codes) from 2012 to 2016 were identified in Premier, a U.S.-based hospital database. Characteristics included sociodemographic, sepsis class, organ failure, infection, hospital size and hospital ward location. Unsupervised k-means clustering identified unique patient clusters, (validated by depicting the first two principal components and with clustering labels) followed by partial least-squares regression (PLSR) to identify feature importance associated with LOH in each cluster. Feature importance was estimated from significant cross-validated regression components.

RESULTS

: CONCLUSIONS

: Results suggest that unique sepsis patient clusters exist. Further exploration will provide greater precision and delivery of personalized care with need for validation across patient populations and care settings to identify actionable insights for LOH reduction.