Pre-Operative Predictors for Discharge to Post-Acute Care Facilities After Total Knee Arthroplasty
| Autor: | Morgan A. Ford, Suzanne E. Argosino, Heather M. Tavel, Claudia A. Steiner, Ella E. Lyons, Denise A. Kiepe, Ryan C. Koonce, Chan Zeng |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Joint replacement medicine.medical_treatment Arthroplasty Replacement Hip Total knee arthroplasty Neurological disorder Logistic regression Medicare Quality of life medicine Humans Orthopedics and Sports Medicine Arthroplasty Replacement Knee Aged Skilled Nursing Facilities Rehabilitation business.industry medicine.disease Patient Discharge United States Cohort Physical therapy Quality of Life business Risk assessment Subacute Care |
| Zdroj: | The Journal of arthroplasty. 37(1) |
| ISSN: | 1532-8406 |
| Popis: | Background Joint replacement surgery is in increasing demand and is the most common inpatient surgery for Medicare beneficiaries. The venue for post-operative rehabilitation, including early outpatient therapy after surgery, influences recovery and quality of life. As part of a comprehensive total joint program at Kaiser Permanente Colorado, we developed and validated a predictive model to anticipate and plan the disposition for rehabilitation of our patients after total knee arthroplasty (TKA). Methods We analyzed data for TKA patients who completed a pre-operative Total Knee Risk Assessment in 2017 (the model development cohort) or during the first 6 months of 2018 (the model validation cohort). The Total Knee Risk Assessment, which is used to guide disposition for rehabilitation, included questions in mobility, social, and environment domains. Multivariable logistic regression was used to predict discharge to post-acute care facilities (PACFs) (ie, skilled nursing facilities or acute rehabilitation centers). Results Data for a total of 1481 and 631 patients who underwent TKA were analyzed in the development and validation cohorts, respectively. Ninety-three patients (6.3%) in the development cohort and 22 patients (3.5%) in the validation cohort were discharged to PACFs. Eight risk factors for discharge to PACFs were included in the final multivariable model. Patients with a diagnosis of neurological disorder and with a mobility/balance issue had the greatest chance of discharge to PACFs. Conclusion This validated predictive model for discharge disposition following TKA may be used as a tool in shared decision-making and discharge planning for patients undergoing TKA. |
| Databáze: | OpenAIRE |
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