Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
McKenna EpiLog Fellowship in Population Health, University of Pennsylvania, Philadelphia, Pennsylvania.
Neurosurgery. 2020 Feb 1;86(2):E140-E146. doi: 10.1093/neuros/nyz419.
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.
To validate the use of Risk Assessment and Prediction Tool (RAPT) in patients undergoing posterior lumbar fusion for predicting discharge disposition.
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.
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).
Preoperative RAPT score is a highly predictive tool in lumbar fusion patients for discharge disposition.
随着捆绑式护理支付模式在神经外科中的广泛应用，人们迫切需要改进术前预测工具，以识别出那些不会因住院时间延长而受益的患者，从而促进他们更早地出院到康复或护理机构。
验证风险评估和预测工具（RAPT）在接受后路腰椎融合术的患者中预测出院去向的作用。
前瞻性纳入 2016 年 6 月至 2017 年 2 月期间接受择期后路腰椎融合术的患者。记录 RAPT 评分和出院结局，纳入年龄 50 岁及以上的患者（n=432）。采用逻辑回归分析评估 RAPT 评分预测出院去向的能力。采用向后逐步逻辑回归的方法进行多变量回归，建立二项式模型。
RAPT 评分升高预示着出院去向为家庭（P<0.0001）。RAPT 评分每增加 1 个单位，家庭出院的可能性分别增加 55.8%和 38.6%，而康复和熟练护理机构的可能性则分别增加 55.8%和 38.6%。此外，RAPT 评分对住院时间（P=0.0239）、总手术费用（P=0.0007）和 30 天再入院（P<0.0001）均有显著预测作用。在 RAPT 评分的亚组中，行走、步态和术后护理可用性都是两种模型中预测出院去向的因素（P<0.0001）。在广义多重逻辑回归模型中，决定出院去向的 3 个最重要的预测因素是 RAPT 评分、住院时间和年龄（P<0.0001、P<0.0001 和 P=0.0001）。
术前 RAPT 评分是腰椎融合术患者出院去向的一个高度预测工具。
