A Preoperative Risk Prediction Tool for Discharge to a Skilled Nursing or Rehabilitation Facility After Total Joint Arthroplasty

Background: Discharge to rehabilitation or a skilled nursing facility (SNF) after total joint arthroplasty remains a primary driver of cost excess for bundled payments. An accurate preoperative risk prediction tool would help providers and health systems identify and modulate perioperative care for higher risk individuals and serve as a vital tool in preoperative clinic as part of shared decision-making regarding the risks/benefits of surgery. Methods: A total of 10,155 primary total knee (5,570, 55%) and hip (4,585, 45%) arthroplasties performed between June 2013 and January 2018 at a single institution were reviewed. The predictive ability of 45 variables for discharge location (SNF/rehab vs home) was tested, including preoperative sociodemo-graphic factors, intraoperative metrics, postoperative labs, as well as 30 Elixhauser comorbidities. Parameters surviving selection were included in a multivariable logistic regression model, which was calibrated using 20,000 bootstrapped samples. Results: A total of 1786 (17.6%) cases were discharged to a SNF/rehab, and a multivariable logistic regression model demonstrated excellent predictive accuracy (area under the receiver operator characteristic curve: 0.824) despite requiring only 9 preoperative variables: age, partner status, the American Society of Anesthesiologists score, body mass index, gender, neurologic disease, electrolyte disorder, paralysis, and pulmonary circulation disorder. Notably, this model was independent of surgery (knee vs hip). Internal validation showed no loss of accuracy (area under the receiver operator characteristic curve: 0.8216, mean squared error: 0.0004) after bias correction for overfitting, and the model was incorporated into a readily available, online prediction tool for easy clinical use. Conclusion: This convenient, interactive tool for estimating likelihood of discharge to a SNF/rehab achieves excellent accuracy using exclusively preoperative factors. These should form the basis for improved reimbursement legislation adjusting for patient risk, ensuring no disparities in access arise for vulnerable populations. (C) 2020 Elsevier Inc. All rights reserved.