1. Predicting Discharge Disposition in Trauma Patients: Development, Validation, and Generalization of a Model Using the National Trauma Data Bank
- Author
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Priti Parikh, Elliott R. Haut, Mary C. McCarthy, Sagar Hirpara, Mitchell Graham, and Pratik J. Parikh
- Subjects
Adult ,Male ,medicine.medical_specialty ,National trauma data bank ,Logistic regression ,03 medical and health sciences ,0302 clinical medicine ,Trauma Centers ,Predictive Value of Tests ,medicine ,Humans ,Generalizability theory ,Registries ,Early discharge ,Aged ,Trauma Severity Indices ,business.industry ,Trauma center ,Discharge disposition ,030208 emergency & critical care medicine ,General Medicine ,Disposition ,Middle Aged ,medicine.disease ,Patient Discharge ,United States ,Substance abuse ,Models, Organizational ,030220 oncology & carcinogenesis ,Emergency medicine ,Wounds and Injuries ,Female ,business - Abstract
Background 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. Methods 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. Results 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. Conclusion 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.
- Published
- 2020