Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada.
Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada.
Neurosurgery. 2021 Feb 16;88(3):584-591. doi: 10.1093/neuros/nyaa477.
Surgical decompression for degenerative cervical myelopathy (DCM) is one of the mainstays of treatment, with generally positive outcomes. However, some patients who undergo surgery for DCM continue to show functional decline.
To use machine learning (ML) algorithms to determine predictors of worsening functional status after surgical intervention for DCM.
This is a retrospective analysis of prospectively collected data. A total of 757 patients enrolled in 2 prospective AO Spine clinical studies, who underwent surgical decompression for DCM, were analyzed. The modified Japanese Orthopedic Association (mJOA) score, a marker of functional status, was obtained before and 1 yr postsurgery. The primary outcome measure was the dichotomized change in mJOA at 1 yr according to whether it was negative (worse functional status) or non-negative. After applying an 80:20 training-testing split of the dataset, we trained, optimized, and tested multiple ML algorithms to evaluate algorithm performance and determine predictors of worse mJOA at 1 yr.
The highest-performing ML algorithm was a polynomial support vector machine. This model showed good calibration and discrimination on the testing data, with an area under the receiver operating characteristic curve of 0.834 (accuracy: 74.3%, sensitivity: 88.2%, specificity: 72.4%). Important predictors of functional decline at 1 yr included initial mJOA, male gender, duration of myelopathy, and the presence of comorbidities.
The reasons for worse mJOA are frequently multifactorial (eg, adjacent segment degeneration, tandem lumbar stenosis, ongoing neuroinflammatory processes in the cord). This study successfully used ML to predict worse functional status after surgery for DCM and to determine associated predictors.
手术减压是治疗退行性颈椎病（DCM）的主要方法之一，一般效果良好。但部分 DCM 患者术后功能仍会出现下降。
利用机器学习（ML）算法确定 DCM 手术治疗后功能状态恶化的预测因素。
这是一项前瞻性收集数据的回顾性分析。共分析了 757 例在 2 项前瞻性 AO 脊柱临床研究中接受 DCM 手术减压的患者。采用改良日本骨科协会（mJOA）评分作为功能状态的标志物，在术前和术后 1 年进行评估。主要结局指标为 mJOA 在 1 年时的变化是否为阴性（功能状态恶化）或非阴性。对数据集应用 80:20 的训练-测试分割后，我们训练、优化和测试了多种 ML 算法，以评估算法性能并确定 1 年时 mJOA 恶化的预测因素。
表现最好的 ML 算法是多项式支持向量机。该模型在测试数据上具有良好的校准和区分度，受试者工作特征曲线下面积为 0.834（准确率：74.3%，敏感度：88.2%，特异度：72.4%）。1 年时功能下降的重要预测因素包括初始 mJOA、男性、颈椎病病程和合并症。
mJOA 恶化的原因通常是多因素的（例如，相邻节段退变、腰椎串联狭窄、脊髓持续神经炎症过程）。本研究成功地利用 ML 预测了 DCM 手术后功能状态的恶化，并确定了相关的预测因素。
