Department of Neurosurgery, University of California San Francisco, San Francisco, CA.
Department of Neurosurgery, University of Virginia Medical Center, Charlottesville, VA.
Spine (Phila Pa 1976). 2019 Aug 15;44(16):1144-1153. doi: 10.1097/BRS.0000000000003031.
Retrospective analysis of prospectively-collected, multicenter adult spinal deformity (ASD) databases.
To predict the likelihood of reaching minimum clinically important differences in patient-reported outcomes after ASD surgery.
ASD surgeries are costly procedures that do not always provide the desired benefit. In some series only 50% of patients achieve minimum clinically important differences in patient-reported outcomes (PROs). Predictive modeling may be useful in shared-decision making and surgical planning processes. The goal of this study was to model the probability of achieving minimum clinically important differences change in PROs at 1 and 2 years after surgery.
Two prospective observational ASD cohorts were queried. Patients with Scoliosis Research Society-22, Oswestry Disability Index , and Short Form-36 data at preoperative baseline and at 1 and 2 years after surgery were included. Seventy-five variables were used in the training of the models including demographics, baseline PROs, and modifiable surgical parameters. Eight predictive algorithms were trained at four-time horizons: preoperative or postoperative baseline to 1 year and preoperative or postoperative baseline to 2 years. External validation was accomplished via an 80%/20% random split. Five-fold cross validation within the training sample was performed. Precision was measured as the mean average error (MAE) and R values.
Five hundred seventy patients were included in the analysis. Models with the lowest MAE were selected; R values ranged from 20% to 45% and MAE ranged from 8% to 15% depending upon the predicted outcome. Patients with worse preoperative baseline PROs achieved the greatest mean improvements. Surgeon and site were not important components of the models, explaining little variance in the predicted 1- and 2-year PROs.
We present an accurate and consistent way of predicting the probability for achieving clinically relevant improvement after ASD surgery in the largest-to-date prospective operative multicenter cohort with 2-year follow-up. This study has significant clinical implications for shared decision making, surgical planning, and postoperative counseling.
回顾性分析前瞻性收集的多中心成人脊柱畸形（ASD）数据库。
预测 ASD 手术后患者报告结局（PRO）达到最小临床重要差异的可能性。
ASD 手术是昂贵的程序，并不总是提供预期的益处。在一些系列中，只有 50%的患者在 PRO 中达到最小临床重要差异。预测模型在共同决策和手术计划过程中可能是有用的。本研究的目的是建立模型，预测术后 1 年和 2 年 PRO 达到最小临床重要差异变化的概率。
对两个前瞻性 ASD 队列进行了查询。纳入术前基线和术后 1 年和 2 年具有 Scoliosis Research Society-22、Oswestry 残疾指数和 Short Form-36 数据的患者。在模型训练中使用了 75 个变量，包括人口统计学、基线 PRO 和可修改的手术参数。在四个时间点（术前或术后基线到 1 年和术前或术后基线到 2 年）训练了 8 种预测算法。通过 80%/20%的随机分割进行外部验证。在训练样本中进行了 5 倍交叉验证。精度以平均平均误差（MAE）和 R 值衡量。
570 名患者纳入分析。选择了 MAE 最低的模型；R 值范围从 20%到 45%，MAE 范围从 8%到 15%，取决于预测的结果。术前基线 PRO 较差的患者取得了最大的平均改善。外科医生和地点不是模型的重要组成部分，对预测的 1 年和 2 年 PRO 方差解释很少。
我们提出了一种准确一致的方法，用于预测迄今为止最大的前瞻性多中心手术队列中 ASD 手术后达到临床相关改善的可能性，该队列具有 2 年随访。这项研究对共同决策、手术计划和术后咨询具有重要的临床意义。
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