A Severe Sepsis Mortality Prediction Model and Score for Use with Administrative Data

Dee W. Ford*, Andrew J. Goodwin, Annie N. Simpson, Emily Johnson, Nandita Nadig, Kit N. Simpson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

Objective: Administrative data are used for research, quality improvement, and health policy in severe sepsis. However, there is not a sepsis-specific tool applicable to administrative data with which to adjust for illness severity. Our objective was to develop, internally validate, and externally validate a severe sepsis mortality prediction model and associated mortality prediction score. Design: Retrospective cohort study using 2012 administrative data from five U.S. States. Three cohorts of patients with severe sepsis were created: 1) International Classification of Diseases, 9th Revision, Clinical Modification codes for severe sepsis/septic shock, 2) Martin approach, and 3) Angus approach. The model was developed and internally validated in International Classification of Diseases, 9th Revision, Clinical Modification, cohort and externally validated in other cohorts. Integer point values for each predictor variable were generated to create a sepsis severity score. Setting: Acute care, nonfederal hospitals in New York, Maryland, Florida, Michigan, and Washington. Subjects: Patients in one of three severe sepsis cohorts: 1) explicitly coded (n = 108,448), 2) Martin cohort (n = 139,094), and 3) Angus cohort (n = 523,637) Interventions: None. Measurements and Main Results: Maximum likelihood estimation logistic regression to develop a predictive model for in-hospital mortality. Model calibration and discrimination assessed via Hosmer-Lemeshow goodness-of-fit and C-statistics, respectively. Primary cohort subset into risk deciles and observed versus predicted mortality plotted. Goodness-of-fit demonstrated p value of more than 0.05 for each cohort demonstrating sound calibration. C-statistic ranged from low of 0.709 (sepsis severity score) to high of 0.838 (Angus cohort), suggesting good to excellent model discrimination. Comparison of observed versus expected mortality was robust although accuracy decreased in highest risk decile. Conclusions: Our sepsis severity model and score is a tool that provides reliable risk adjustment for administrative data.

Original languageEnglish (US)
Pages (from-to)319-327
Number of pages9
JournalCritical care medicine
Volume44
Issue number2
DOIs
StatePublished - Feb 1 2016
Externally publishedYes

Keywords

  • administrative data
  • critical care
  • health service research
  • intensive care
  • mortality prediction model
  • risk adjustment
  • sepsis
  • septic shock
  • severe sepsis
  • severity index
  • severity of illness
  • severity score

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine

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