Predicting Discharge Disposition in Trauma Patients: Development, Validation, and Generalization of a Model Using the National Trauma Data Bank

• Published in: The American surgeon 2020-12, Vol.86 (12), p.1703-1709
• Main Authors: Graham, Mitchell, Parikh, Pratik, Hirpara, Sagar, McCarthy, Mary C., Haut, Elliott R., Parikh, Priti P.
• Format: Article
• Language: English
• Subjects: Adult; Age; Aged; Cancer therapies; Cardiovascular disease; Chronic obstructive pulmonary disease; Comorbidity; Coronary vessels; Diabetes; Diabetes mellitus; Drug abuse; Female; Government programs; Humans; Male; Middle Aged; Models, Organizational; Optimization; Patient Discharge; Patients; Predictive Value of Tests; Registries; Regression models; Trauma; Trauma centers; Trauma Centers - organization & administration; Trauma Severity Indices; United States; Wounds and Injuries - therapy
• ISSN: 0003-1348; 1555-9823
• DOI: 10.1177/0003134820949523

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.