The Risk Assessment and Prediction Tool (RAPT) for Discharge Planning in a Posterior Lumbar Fusion Population. Issue 2 (10th October 2019) - Glauser, Gregory; Piazza, Matthew; Berger, Ian; Osiemo, Benjamin; McClintock, Scott D; Winter, Eric; Chen, H Isaac; Ali, Zarina S; Malhotra, Neil R
Abstract: BACKGROUND: As the use of bundled care payment models has become widespread in neurosurgery, there is a distinct need for improved preoperative predictive tools to identify patients who will not benefit from prolonged hospitalization, thus facilitating earlier discharge to rehabilitation or nursing facilities. OBJECTIVE: To validate the use of Risk Assessment and Prediction Tool (RAPT) in patients undergoing posterior lumbar fusion for predicting discharge disposition. METHODS: Patients undergoing elective posterior lumbar fusion from June 2016 to February 2017 were prospectively enrolled. RAPT scores and discharge outcomes were recorded for patients aged 50 yr or more (n = 432). Logistic regression analysis was used to assess the ability of RAPT score to predict discharge disposition. Multivariate regression was performed in a backwards stepwise logistic fashion to create a binomial model. RESULTS: Escalating RAPT score predicts disposition to home ( P < .0001). Every unit increase in RAPT score increases the chance of home disposition by 55.8% and 38.6% than rehab and skilled nursing facility, respectively. Further, RAPT score was significant in predicting length of stay ( P = .0239), total surgical cost ( P = .0007), and 30-d readmission ( P < .0001). Amongst RAPT score subcomponents, walk, gait, and postoperative care availability were all predictive of disposition location ( P < .0001) for both models. In a generalized multiple logistic regression model, the 3 top predictive factors for disposition were the RAPT score, length of stay, and age ( P < .0001, P < .0001 and P = .0001, respectively). CONCLUSION: Preoperative RAPT score is a highly predictive tool in lumbar fusion patients for discharge disposition. Graphical Abstract: