Early Prediction of Trauma Patient Discharge DispositionDepartment of Surgery Faculty Publications
AbstractObjective: Do pre-injury and early hospital admission characteristics help predict patient discharge disposition? Background: Total one-year treatment cost of adult major trauma in the United States is estimated at $30 billion annually, with 58% of the cost due to the index hospitalization. Increased length of stay (LOS) increases morbidity and delays rehabilitation. Prediction of discharge to a location other than home through univariate risk factor methodology and development of a multivariable binary logistic regression model allows early discharge planning. Methods: A one-year Level 1 trauma center registry dataset was used to develop a predictive model of discharge disposition using 2836 trauma patients. Patients with a documented discharge location, comorbidities, injuries, vital statistics, and hospital stay information were included in the study. ANOVA and chi-square analysis were performed to determine univariate predictors of discharge to home vs. non-home (i.e., nursing home, hospice, long- term acute care unit). Multivariable binary logistic regression determined independent predictors for discharge to non-home locations. We developed two models: (i) a regular discharge (RD) model to predict discharge to non- home locations based on demographic and clinical characteristics at the completion of hospital stay and (ii) an admission planning discharge (APD) model based on data available shortly after admission. Results: For the RD model, increased age, female sex, longer ICU and hospital stays, and the comorbidities of neurologic deficiencies, diabetes, coagulopathy and obesity were independent predictors of non-home discharge. The RD model accounted for 56.2% of the variance in discharge to non-home and correctly predicted discharge to home and not home 87.2% of the time. For the APD model, increased age, female sex, and the comorbidities of neurologic deficiencies, diabetes, coagulopathy and obesity were independent predictors of non-home discharge. The APD model accounted for 39.4% of the variance in discharge to non-home and correctly predicted the discharge to home and not home 82.9% of the time. Further, the authors derived Clinical Decision Rules (CDRs) for both the RD and APD models. Conclusion: Demographic and clinical information for trauma patients predicts dispositions early and late in the hospital stay. If the clinical decision rules derived from this study are validated, steps can be taken by hospital staff to arrange placement earlier in the hospital stay, allowing for a smoother transition for the patient and cost savings.
Citation InformationRonald Beaulieu, Priti Parikh, Akpofure Peter Ekeh, Ronald J. Markert, et al.. "Early Prediction of Trauma Patient Discharge Disposition" (2013)
Available at: http://works.bepress.com/priti_parikh/22/