Department of Orthopedic Surgery, NYU Langone Health, New York, NY.
Department of Orthopaedic Surgery, Duke University Medical Center, Durham, North Carolina.
J Bone Joint Surg Am. 2022 Sep 7;104(17):1579-1585. doi: 10.2106/JBJS.21.00955. Epub 2022 Jul 20.
Cost excess in bundled payment models for total joint arthroplasty (TJA) is driven by discharge to rehabilitation or a skilled nursing facility (SNF). A recently published preoperative risk prediction tool showed very good internal accuracy in stratifying patients on the basis of likelihood of discharge to an SNF or rehabilitation. The purpose of the present study was to test the accuracy of this predictive tool through external validation with use of a large cohort from an outside institution.
A total of 20,294 primary unilateral total hip (48%) and knee (52%) arthroplasty cases at a tertiary health system were extracted from the institutional electronic medical record. Discharge location and the 9 preoperative variables required by the predictive model were collected. All cases were run through the model to generate risk scores for those patients, which were compared with the actual discharge locations to evaluate the cutoff originally proposed in the derivation paper. The proportion of correct classifications at this threshold was evaluated, as well as the sensitivity, specificity, positive and negative predictive values, number needed to screen, and area under the receiver operating characteristic curve (AUC), in order to determine the predictive accuracy of the model.
A total of 3,147 (15.5%) of the patients who underwent primary, unilateral total hip or knee arthroplasty were discharged to rehabilitation or an SNF. Despite considerable differences between the present and original model derivation cohorts, predicted scores demonstrated very good accuracy (AUC, 0.734; 95% confidence interval, 0.725 to 0.744). The threshold simultaneously maximizing sensitivity and specificity was 0.1745 (sensitivity, 0.672; specificity, 0.679), essentially identical to the proposed cutoff of the original paper (0.178). The proportion of correct classifications was 0.679. Positive and negative predictive values (0.277 and 0.919, respectively) were substantially better than those of random selection based only on event prevalence (0.155 and 0.845), and the number needed to screen was 3.6 (random selection, 6.4).
A previously published online predictive tool for discharge to rehabilitation or an SNF performed well under external validation, demonstrating a positive predictive value 79% higher and number needed to screen 56% lower than simple random selection. This tool consists of exclusively preoperative parameters that are easily collected. Based on a successful external validation, this tool merits consideration for clinical implementation because of its value for patient counseling, preoperative optimization, and discharge planning.
Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.
在全膝关节置换术 (TJA) 的捆绑支付模型中，成本超支是由康复或熟练护理机构 (SNF) 的出院导致的。最近发表的术前风险预测工具在基于 SNF 或康复出院的可能性对患者进行分层方面显示出非常好的内部准确性。本研究的目的是通过使用来自外部机构的大型队列进行外部验证来测试该预测工具的准确性。
从机构电子病历中提取了一家三级医疗机构的 20294 例初次单侧全髋关节 (48%) 和全膝关节 (52%) 置换术病例。收集了出院地点和预测模型所需的 9 个术前变量。对所有病例进行模型计算，为这些患者生成风险评分，并将其与实际出院地点进行比较，以评估原始推导论文中提出的截断值。评估了该阈值下的正确分类比例，以及敏感性、特异性、阳性和阴性预测值、需要筛查的数量以及接收器工作特征曲线 (AUC) 的面积，以确定模型的预测准确性。
初次单侧全髋关节或全膝关节置换术的患者中，有 3147 例 (15.5%) 出院至康复或 SNF。尽管当前和原始模型推导队列之间存在很大差异，但预测评分显示出非常高的准确性 (AUC，0.734；95%置信区间，0.725 至 0.744)。同时最大化敏感性和特异性的阈值为 0.1745(敏感性，0.672；特异性，0.679)，与原始论文提出的截断值(0.178)基本相同。正确分类的比例为 0.679。阳性和阴性预测值(分别为 0.277 和 0.919)明显优于仅基于事件发生率的随机选择(0.155 和 0.845)，需要筛查的数量为 3.6(随机选择，6.4)。
先前发表的用于康复或 SNF 出院的在线预测工具在外部验证中表现良好，阳性预测值高出 79%，需要筛查的数量减少 56%，优于简单的随机选择。该工具仅由术前参数组成，易于收集。基于成功的外部验证，该工具因其对患者咨询、术前优化和出院计划的价值，值得考虑临床实施。
预后 III 级。请参阅作者说明以获取完整的证据水平描述。
