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首页> 外文期刊>Neurosurgical focus >Potential of predictive computer models for preoperative patient selection to enhance overall quality-adjusted life years gained at 2-year follow-up: a simulation in 234 patients with adult spinal deformity
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Potential of predictive computer models for preoperative patient selection to enhance overall quality-adjusted life years gained at 2-year follow-up: a simulation in 234 patients with adult spinal deformity

机译：预测计算机模型在术前患者选择中的潜力，以提高在2年随访中获得的总体质量调整生命年：在234例成人脊柱畸形患者中进行的模拟

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OBJECTIVE Patients with adult spinal deformity (ASD) experience significant quality of life improvements after surgery. Treatment, however, is expensive and complication rates are high. Predictive analytics has the potential to use many variables to make accurate predictions in large data sets. A validated minimum clinically important difference (MCID) model has the potential to assist in patient selection, thereby improving outcomes and, potentially, cost-effectiveness. METHODS The present study was a retrospective analysis of a multiinstitutional database of patients with ASD. Inclusion criteria were as follows: age ≥ 18 years, radiographic evidence of ASD, 2-year follow-up, and preoperative Oswestry Disability Index (ODI) > 15. Forty-six variables were used for model training: demographic data, radiographic parameters, surgical variables, and results on the health-related quality of life questionnaire. Patients were grouped as reaching a 2-year ODI MCID (+MCID) or not (?MCID). An ensemble of 5 different bootstrapped decision trees was constructed using the C5.0 algorithm. Internal validation was performed via 70:30 data split for training/testing. Model accuracy and area under the curve (AUC) were calculated. The mean quality-adjusted life years (QALYs) and QALYs gained at 2 years were calculated and discounted at 3.5% per year. The QALYs were compared between patients in the +MCID and –MCID groups. RESULTS A total of 234 patients met inclusion criteria (+MCID 129, ?MCID 105). Sixty-nine patients (29.5%) were included for model testing. Predicted versus actual results were 50 versus 40 for +MCID and 19 versus 29 for ?MCID (i.e., 10 patients were misclassified). Model accuracy was 85.5%, with 0.96 AUC. Predicted results showed that patients in the +MCID group had significantly greater 2-year mean QALYs (p = 0.0057) and QALYs gained (p = 0.0002). CONCLUSIONS A successful model with 85.5% accuracy and 0.96 AUC was constructed to predict which patients would reach ODI MCID. The patients in the +MCID group had significantly higher mean 2-year QALYs and QALYs gained. This study provides proof of concept for using predictive modeling techniques to optimize patient selection in complex spine surgery.
机译：目的患有成人脊柱畸形（ASD）的患者手术后生活质量显着改善。然而，治疗昂贵且并发症发生率高。预测分析可能会使用许多变量来对大型数据集进行准确的预测。经过验证的最小临床重要差异（MCID）模型有可能协助患者选择，从而改善结果并可能改善成本效益。方法本研究是对ASD患者的多机构数据库的回顾性分析。纳入标准如下：年龄≥18岁，ASD的影像学证据，2年的随访以及术前Oswestry残疾指数（ODI）>15。模型训练使用了46个变量：人口统计学数据，影像学参数，手术变量，以及有关健康相关生活质量问卷的结果。患者分为达到或未达到2年ODI MCID（+ MCID）（？MCID）。使用C5.0算法构建了5个不同的自举决策树的集合。内部验证是通过70:30数据拆分进行的，用于培训/测试。计算模型精度和曲线下面积（AUC）。计算了平均质量调整生命年（QALYs）和在2年时获得的QALYs，并以每年3.5％的价格折现。比较了+ MCID组和–MCID组的患者的QALY。结果共有234位患者符合入选标准（+ MCID 129，？MCID 105）。包括69名患者（29.5％）用于模型测试。 + MCID的预测结果与实际结果分别为50和40，ΔMCID的预测结果与实际结果分别为19和29（即10位患者被错误分类）。模型的准确度为85.5％，AUC为0.96。预测结果表明，+ MCID组的患者2年平均QALYs（p = 0.0057）和获得的QALYs（p = 0.0002）明显更高。结论构建了一个成功模型，其准确度为85.5％，AUC为0.96，可以预测哪些患者会达到ODI MCID。 + MCID组的患者平均2年QALYs和获得的QALYs明显更高。这项研究提供了在复杂脊柱手术中使用预测建模技术优化患者选择的概念证明。

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