REadmission PREvention in SepSis: Development and Validation of a Prediction Model

Ami A. Grek, Emily R. Rogers, Sarah H. Peacock, Tonja M. Hartjes, Launia J. White, Zhuo Li, James M. Naessens, Pablo M. Franco

Research output: Contribution to journalArticlepeer-review

Abstract

Hospital 30-day readmissions remain a major quality and cost indicator. Traditional readmission risk scores, such as LACE (length of stay, acuity of admission, Charlson comorbidity index, and emergency department visits), may be suboptimal in special patient populations, such as those with sepsis. As sepsis survivorship improves, there is a need to determine which variables might be associated with a decrease in 30-day readmission. We completed a retrospective analysis reviewing patients with sepsis who had unplanned 30-day readmissions. Multivariate regression analysis was performed for the REadmission PREvention in SepSis (REPRESS) model, which evaluated age, length of stay, Charlson disease count, Richmond Agitation–Sedation Scale score, discharge to a skilled nursing facility, and mobility for predictive significance in hospital readmission. Our REPRESS model performed better when compared with LACE for predicting readmission risk in a sepsis population.

Original languageEnglish (US)
Pages (from-to)161-168
Number of pages8
JournalJournal for Healthcare Quality
Volume44
Issue number3
DOIs
StatePublished - May 1 2022

Keywords

  • bundle compliance
  • healthcare quality
  • hospital readmission
  • Sep-1
  • sepsis bundle

ASJC Scopus subject areas

  • Health Policy
  • Public Health, Environmental and Occupational Health

Fingerprint

Dive into the research topics of 'REadmission PREvention in SepSis: Development and Validation of a Prediction Model'. Together they form a unique fingerprint.

Cite this