The Risk Assessment and Prediction Tool (RAPT) for Discharge Planning in a Posterior Lumbar Fusion Population

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Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Glauser, G; Piazza, M; Berger, I; Osiemo, B; McClintock, SD; Winter, E; Chen, HI; Ali, ZS; Malhotra, NR

Published In

Published By

Electronic International Standard Serial Number (EISSN)

  • 1524-4040

International Standard Serial Number (ISSN)

  • 0148-396X

Digital Object Identifier (DOI)

  • 10.1093/neuros/nyz419

Language

  • en