Department of Orthopaedic Surgery (D.E.G., S.P.R., D.E.A., M.P.B., and T.M.S.), Department of Anesthesiology (T.J.H.), and Performance Services (C.B.H.), Duke University Medical Center, Durham, North Carolina.
J Bone Joint Surg Am. 2019 Mar 20;101(6):547-556. doi: 10.2106/JBJS.18.00843.
A reliable prediction tool for 90-day adverse events not only would provide patients with valuable estimates of their individual risk perioperatively, but would also give health-care systems a method to enable them to anticipate and potentially mitigate postoperative complications. Predictive accuracy, however, has been challenging to achieve. We hypothesized that a broad range of patient and procedure characteristics could adequately predict 90-day readmission after total joint arthroplasty (TJA).
The electronic medical records on 10,155 primary unilateral total hip (4,585, 45%) and knee (5,570, 55%) arthroplasties performed at a single institution from June 2013 to January 2018 were retrospectively reviewed. In addition to 90-day readmission status, >50 candidate predictor variables were extracted from these records with use of structured query language (SQL). These variables included a wide variety of preoperative demographic/social factors, intraoperative metrics, postoperative laboratory results, and the 30 standardized Elixhauser comorbidity variables. The patient cohort was randomly divided into derivation (80%) and validation (20%) cohorts, and backward stepwise elimination identified important factors for subsequent inclusion in a multivariable logistic regression model.
Overall, subsequent 90-day readmission was recorded for 503 cases (5.0%), and parameter selection identified 17 variables for inclusion in a multivariable logistic regression model on the basis of their predictive ability. These included 5 preoperative parameters (American Society of Anesthesiologists [ASA] score, age, operatively treated joint, insurance type, and smoking status), duration of surgery, 2 postoperative laboratory results (hemoglobin and blood-urea-nitrogen [BUN] level), and 9 Elixhauser comorbidities. The regression model demonstrated adequate predictive discrimination for 90-day readmission after TJA (area under the curve [AUC]: 0.7047) and was incorporated into static and dynamic nomograms for interactive visualization of patient risk in a clinical or administrative setting.
A novel risk calculator incorporating a broad range of patient factors adequately predicts the likelihood of 90-day readmission following TJA. Identifying at-risk patients will allow providers to anticipate adverse outcomes and modulate postoperative care accordingly prior to discharge.
Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.
可靠的 90 天不良事件预测工具不仅可以为患者提供其个体围手术期风险的有价值的估计，还可以为医疗保健系统提供一种方法，使他们能够预测并可能减轻术后并发症。然而，预测准确性一直是一个挑战。我们假设广泛的患者和手术特点可以充分预测全关节置换术后 90 天的再入院。
回顾性分析了 2013 年 6 月至 2018 年 1 月在一家机构进行的 10155 例单侧初次全髋关节（4585 例，45%）和全膝关节（5570 例，55%）置换术的电子病历。除了 90 天再入院情况外，还使用结构化查询语言（SQL）从这些记录中提取了 50 多个候选预测变量。这些变量包括各种术前人口统计学/社会因素、术中指标、术后实验室结果以及 30 个标准化的 Elixhauser 合并症变量。患者队列随机分为推导（80%）和验证（20%）队列，逐步向后消除法确定了后续纳入多变量逻辑回归模型的重要因素。
总体而言，503 例（5.0%）患者记录了随后的 90 天再入院，参数选择确定了 17 个变量，用于基于其预测能力纳入多变量逻辑回归模型。这些变量包括 5 个术前参数（美国麻醉师协会[ASA]评分、年龄、手术关节、保险类型和吸烟状况）、手术持续时间、2 个术后实验室结果（血红蛋白和血尿素氮[BUN]水平）和 9 个 Elixhauser 合并症。该回归模型对 TJA 后 90 天再入院具有足够的预测区分能力（曲线下面积[AUC]：0.7047），并被纳入静态和动态列线图中，以便在临床或行政环境中直观地显示患者的风险。
一种新的风险计算器，纳入了广泛的患者因素，可以充分预测 TJA 后 90 天再入院的可能性。识别高危患者将使提供者能够在出院前预测不良后果并相应调整术后护理。
预后 IV 级。有关证据水平的完整描述，请参见作者说明。
