Clinical and non-clinical factors that predict discharge disposition after a fall. Issue 5 (May 2018)

• Record Type: Journal Article
• Authors: James, Melissa K.; Robitsek, R. Jonathan; Saghir, Syed M.; Gentile, Patricia A.; Ramos, Marylin; Perez, Frances
• Is Part Of: Injury. Volume 49:Issue 5(2018)
• Page Start: 975
• Page End: 982
• Publication Date: 2018-05
• Subjects: Falls -- Discharge planning -- Rehabilitation -- Discharge disposition -- Patient discharge -- Discharge location; Wounds and injuries -- Surgery -- Periodicals; Accidents -- Periodicals; Wounds and Injuries -- surgery -- Periodicals; Lésions et blessures -- Chirurgie -- Périodiques; Electronic journals; Electronic journals; 617.1
• DOI: 10.1016/j.injury.2018.02.014
• Languages: English
• ISSNs: 0020-1383

Abstract

Abstract: Background: Falls can result in injuries that require rehabilitation and long-term care after hospital discharge. Identifying factors that contribute to prediction of discharge disposition is crucial for efficient resource utilization and reducing cost. Several factors may influence discharge location after hospitalization for a fall. The aim of this study was to examine clinical and non-clinical factors that may predict discharge disposition after a fall. We hypothesized that age, injury type, insurance type, and functional status would affect discharge location. Methods: This two-year retrospective study was performed at an urban, adult level-1 trauma center. Fall patients who were discharged home or to a facility after hospital admission were included in the study. Data was obtained from the trauma registry and electronic medical records. Logistic regression modeling was used to assess independent predictors. Results: A total of 1, 121 fallers were included in the study. 621 (55.4%) were discharged home and 500 (44.6%) to inpatient rehabilitation (IRF)/skilled nursing facility (SNF). The median age was 64 years (IQR: 49–79) and 48.4% (543) were male. The median length of hospital stay was 5 days (IQR: 2.5–8). Increasing age (p < 0.001), length of stay in the ICU (p < 0.001), injury severity (p < 0.001), number of comorbidities (p = 0.038), having Medicare insurance (p = 0.025), having a fracture at any body region (p < 0.001), and ambulation status (p = 0.025) significantly increased the odds of being discharged to IRF/SNF compared to home. The removal of injury severity score and ICU length of stay from the "late/regular discharge" model, to create an "early discharge" model, decreased the accuracy of the prediction rate from 78.5% to 74.9% (p < 0.001). Conclusion: A combination of demographic, clinical, social, economic, and functional factors can together predict discharge disposition after a fall. The majority of these factors can be assessed early in the hospital stay, which may facilitate a timely discharge plan and shorter stays in the hospital.