A multistate model predicting mortality, length of stay, and readmission for surgical patients.

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Publication Date



Surgery, CORE

Journal Title

Health services research

MeSH Headings

Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Comorbidity, Computer Simulation, Elective Surgical Procedures, Female, Humans, Length of Stay, Male, Middle Aged, Models, Statistical, Patient Discharge, Patient Readmission, Regression Analysis, Retrospective Studies, Risk Factors, Time Factors, Young Adult


OBJECTIVE: Simultaneously evaluate postoperative mortality, length of stay (LOS), and readmission.

DATA SOURCE: National Surgical Quality Improvement Program (NSQIP).

DESIGN: Retrospective cohort.

METHODS: Data from elective general surgical patients were obtained from the 2012 NSQIP Participant Use File. For each postoperative day, each patient's state was classified as index hospitalization, discharged home, discharged to long-term care (LTC), readmitted, or dead. Transition rates were estimated using exponential regression, assuming constant rates for specified time periods. These estimates were combined into a multistate model, simulated results of which were compared to observed outcomes.

FINDINGS: Age, comorbidities, more complex procedures, and longer index LOS were associated with lower rates of discharge home and higher rates of death, discharge to LTC, and readmission. The longer patients had been discharged, the less likely they were to die or be readmitted. The model predicted 30-day mortality 0.38 percent (95 percent CI: 0.36-0.41), index LOS 2.85 days (95 percent CI: 2.83-2.86), LTC discharge 2.76 percent (95 percent CI: 2.69-2.82), and readmissions 5.53 percent (95 percent CI: 5.43-5.62); observed values were 0.39 percent, 2.82 days, 2.87 percent, and 5.70 percent, respectively.

CONCLUSIONS: Multistate models can simultaneously predict postoperative mortality, LOS, discharge destination, and readmissions, which allows multidimensional comparison of surgical outcomes.



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