Chan Zeng1, Mark W Melberg2, Heather M Tavel1, Suzanne E Argosino3, Denise A Kiepe3, Ella E Lyons1, Morgan A Ford1, Claudia A Steiner4. 1. Institute for Health Research, Kaiser Permanente Colorado, Aurora, CO. 2. Orthopedics Department, Kaiser Permanente Colorado, Denver, CO; Colorado Permanente Medical Group, Denver, CO. 3. Orthopedics Department, Kaiser Permanente Colorado, Denver, CO. 4. Institute for Health Research, Kaiser Permanente Colorado, Aurora, CO; Colorado Permanente Medical Group, Denver, CO.
Abstract
BACKGROUND: Demand for joint replacement is increasing, with many patients receiving postsurgical physical therapy (PT) in non-inpatient settings. Clinicians need a reliable tool to guide decisions about the appropriate PT setting for patients discharged home after surgery. We developed and validated a model to predict PT location for patients in our health system discharged home after total knee arthroplasty. METHODS: We analyzed data for patients who completed a preoperative total knee risk assessment in 2017 (model development cohort) or during the first 6 months of 2018 (model validation cohort). The initial total knee risk assessment, to guide rehabilitation disposition, included 28 variables in mobility, social, and environment domains, and on patient demographics and comorbidities. Multivariable logistic regression was used to identify factors that best predict discharge to home health service (HHS) vs home with outpatient PT. Model performance was assessed by standard criteria. RESULTS: The development cohort included 259 patients (19%) discharged to HHS and 1129 patients (81%) discharged to home with outpatient PT. The validation cohort included 609 patients, with 91 (15%) discharged to HHS. The final model included age, gender, motivation for outpatient PT, and reliable transportation. Patients without motivation for outpatient PT had the highest probability of discharge to HHS, followed by those without reliable transportation. Model performance was excellent in the development and validation cohort, with c-statistics of 0.91 and 0.86, respectively. CONCLUSION: We developed and validated a predictive model for total knee arthroplasty PT discharge location. This model includes 4 variables with accurate prediction to guide patient-clinician preoperative decision making.
BACKGROUND: Demand for joint replacement is increasing, with many patients receiving postsurgical physical therapy (PT) in non-inpatient settings. Clinicians need a reliable tool to guide decisions about the appropriate PT setting for patients discharged home after surgery. We developed and validated a model to predict PT location for patients in our health system discharged home after total knee arthroplasty. METHODS: We analyzed data for patients who completed a preoperative total knee risk assessment in 2017 (model development cohort) or during the first 6 months of 2018 (model validation cohort). The initial total knee risk assessment, to guide rehabilitation disposition, included 28 variables in mobility, social, and environment domains, and on patient demographics and comorbidities. Multivariable logistic regression was used to identify factors that best predict discharge to home health service (HHS) vs home with outpatient PT. Model performance was assessed by standard criteria. RESULTS: The development cohort included 259 patients (19%) discharged to HHS and 1129 patients (81%) discharged to home with outpatient PT. The validation cohort included 609 patients, with 91 (15%) discharged to HHS. The final model included age, gender, motivation for outpatient PT, and reliable transportation. Patients without motivation for outpatient PT had the highest probability of discharge to HHS, followed by those without reliable transportation. Model performance was excellent in the development and validation cohort, with c-statistics of 0.91 and 0.86, respectively. CONCLUSION: We developed and validated a predictive model for total knee arthroplasty PT discharge location. This model includes 4 variables with accurate prediction to guide patient-clinician preoperative decision making.