PAPER 055: PREDICTING PATIENT DISCHARGE DISPOSITION FOLLOWING TOTAL JOINT ARTHROPLASTY

Abstract

Purpose: The current health care climate encourages an early discharge directly home. Efforts to increase efficiency and decrease length of stay require accurate pre-planning of patient discharge following total joint arthroplasty (TJA). The purpose of this study was to develop and evaluate an easily administered form to preoperatively predict patient discharge disposition following TJA.

Method: A form was generated by a multidisciplinary group of clinicians which identified a set of preoperative factors relevant to patient discharge including age, gender, body mass index, comorbidities, preoperative ambulatory status, projected postoperative weight bearing, home environment and location, and caregiver assistance. Data were collected from a retrospective review of 516 medical charts for patients that had undergone primary total knee arthroplasty (TKA) (n=103), revision TKA (n=104), bilateral TKA (n=102), primary total hip arthroplasty (THA) (n=106), and revision THA (n=101). A stepwise multinomial logistic regression model was used to identify predictors of discharge to a skilled nursing facility (SNF), rehabilitation facility, or home, using SPSS version 11.5 statistical software (SPSS Inc., Chicago, IL).

Results: Patients were more likely to be discharged to either a SNF or rehabilitation facility if they underwent bilateral TKA (p< 0.001); were female (p< 0.001), have their heart disease monitored (p=0.003); or are older (p< 0.001). Patients are more likely to be discharged home if preoperatively they are capable of independent ambulation in the community (p=0.014). Patients discharged to either a SNF or rehabilitation facility were not significantly different except patients undergoing bilateral TKA were more likely to be discharged to a rehabilitation facility (p< 0.001).

Conclusion: We identified factors associated with discharge to a SNF, rehabilitation facility, or home following elective joint replacement surgery. With further validation, this model may be a useful tool for preoperatively predicting a patient’s discharge disposition, which is valuable to the hospital, clinicians, patients, and families in efficiently preparing for postoperative care.