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- Title
Predicting Discharge Disposition in Trauma Patients: Development, Validation, and Generalization of a Model Using the National Trauma Data Bank.
- Authors
Graham, Mitchell; Parikh, Pratik; Hirpara, Sagar; McCarthy, Mary C.; Haut, Elliott R.; Parikh, Priti P.
- Abstract
<bold>Background: </bold>Limited work has been done in predicting discharge disposition in trauma patients; most studies use single institutional data and have limited generalizability. This study develops and validates a model to predict, at admission, trauma patients' discharge disposition using NTDB, transforms the model into an easy-to-use score, and subsequently evaluates its generalizability on institutional data.<bold>Methods: </bold>NTDB data were used to build and validate a binary logistic regression model using derivation-validation (ie, train-test) approach to predict patient disposition location (home vs non-home) upon admission. The model was then converted into a trauma disposition score (TDS) using an optimization-based approach. The generalizability of TDS was evaluated on institutional data from a single Level I trauma center in the U.S.<bold>Results: </bold>A total of 614 625 patients in the NTDB were included in the study; 212 684 (34.6%) went to a non-home location. Patients with a non-home disposition compared to home had significantly higher age (69 ± 19.7 vs 48.3 ± 20.3) and ISS (11.2 ± 8.2 vs 8.2 ± 6.3); P < .001. Older age, female sex, higher ISS, comorbidities (cancer, cardiovascular, coagulopathy, diabetes, hepatic, neurological, psychiatric, renal, substance abuse), and Medicare insurance were independent predictors of non-home discharge. The logistic regression model's AUC was 0.8; TDS achieved a correlation of 0.99 and performed similarly well on institutional data (n = 3161); AUC = 0.8.<bold>Conclusion: </bold>We developed a score based on a large national trauma database that has acceptable performance on local institutions to predict patient discharge disposition at the time of admission. TDS can aid in early discharge preparation for likely-to-be non-home patients and may improve hospital efficiency.
- Subjects
UNITED States; DATABASES; HOSPITAL admission &; discharge; GENERALIZATION; REGRESSION analysis; LOGISTIC regression analysis; WOUND care; PREDICTIVE tests; TRAUMA centers; ACQUISITION of data; TRAUMA severity indices; MANAGEMENT; DISCHARGE planning
- Publication
American Surgeon, 2020, Vol 86, Issue 12, p1703
- ISSN
0003-1348
- Publication type
journal article
- DOI
10.1177/0003134820949523