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


METHODS
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.


METHODS
Acute care, nonfederal hospitals in New York, Maryland, Florida, Michigan, and Washington.


METHODS
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.


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.
1Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Medical University of South Carolina, Charleston, SC. 2Department of Healthcare Leadership and Management, College of Health Professions, Medical University of South Carolina, Charleston, SC.
Dee W
DW
Ford
Andrew J
AJ
Goodwin
Annie N
AN
Simpson
Emily
E
Johnson
Nandita
N
Nadig
Kit N
KN
Simpson
D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D003422:Critical Care; D005260:Female; D006801:Humans; D007348:Insurance, Health; D038801:International Classification of Diseases; D016013:Likelihood Functions; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D044469:Racial Groups; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment; D018805:Sepsis / Q000401:mortality*; D012720:Severity of Illness Index; D012737:Sex Factors; D014481:United States