Machine Learning Approach in Predicting Clinically Significant Improvements After Surgery in Patients with Cervical Ossification of the Posterior Longitudinal Ligament
Machine Learning Approach in Predicting Clinically Significant Improvements After Surgery in Patients with Cervical Ossification of the Posterior Longitudinal Ligament
复制标题
DOI：
10.1097/brs.0000000000004125
复制
发表时间：
2021-12-15
期刊：
影响因子：
3
通讯作者：
Okawa, Atsushi
中科院分区：
文献类型：
--
作者：
Maki, Satoshi;Furuya, Takeo;Okawa, Atsushi
研究设计.前瞻性收集数据的回顾性分析。Objective.本研究旨在使用机器学习（ML）创建颈椎后纵韧带骨化症（OPLL）患者手术结局的预后模型。背景数据总结。确定手术结果有助于外科医生为患者提供预后信息并管理他们的期望。ML是一种数学模型，可以从大样本数据中发现模式，并做出优于传统统计方法的预测。方法.在478例患者中，分别有397例和370例患者在1年和2年时有完整的随访信息，并被纳入分析。最小临床重要差异（MCID）定义为获得的日本骨科协会（乔亚）评分≥ 2.5分，之后创建ML模型，预测术后1年和2年是否可以实现MCID。患者背景、临床症状和影像学检查结果作为变量进行分析。使用LightGBM、XGBoost、随机森林和逻辑回归创建ML模型，然后计算准确度和受试者工作特征曲线下面积（AUC）。结果术前平均乔亚评分为10.3，术后1年为13.4，术后2年为13.5。XGBoost在预测1年MCID时显示出最高的AUC（0.72）和高准确度（67.8），而随机森林在预测2年MCID时具有最高的AUC（0.75）和准确度（69.6）。在纳入的特征中，术前乔亚总评分、症状持续时间、体重、乔亚的下肢感觉功能分项评分和年龄在大多数ML模型中被确定为具有最大意义。结论构建OPLL患者手术结局的预测ML模型是可行的，表明ML在脊柱手术预测模型中的潜在应用。
Study Design. A retrospective analysis of prospectively collected data. Objective. This study aimed to create a prognostic model for surgical outcomes in patients with cervical ossification of the posterior longitudinal ligament (OPLL) using machine learning (ML). Summary of Background Data. Determining surgical outcomes helps surgeons provide prognostic information to patients and manage their expectations. ML is a mathematical model that finds patterns from a large sample of data and makes predictions outperforming traditional statistical methods. Methods. Of 478 patients, 397 and 370 patients had complete follow-up information at 1 and 2 years, respectively, and were included in the analysis. A minimal clinically important difference (MCID) was defined as an acquired Japanese Orthopedic Association (JOA) score of >= 2.5 points, after which a ML model that predicts whether MCID can be achieved 1 and 2 years after surgery was created. Patient background, clinical symptoms, and imaging findings were used as variables for analysis. The ML model was created using LightGBM, XGBoost, random forest, and logistic regression, after which the accuracy and area under the receiver-operating characteristic curve (AUC) were calculated. Results. The mean JOA score was 10.3 preoperatively, 13.4 at 1 year after surgery, and 13.5 at 2 years after surgery. XGBoost showed the highest AUC (0.72) and high accuracy (67.8) for predicting MCID at 1 year, whereas random forest had the highest AUC (0.75) and accuracy (69.6) for predicting MCID at 2 years. Among the included features, total preoperative JOA score, duration of symptoms, body weight, sensory function of the lower extremity sub-score of the JOA, and age were identified as having the most significance in most of ML models. Conclusion. Constructing a prognostic ML model for surgical outcomes in patients with OPLL is feasible, suggesting the potential application of ML for predictive models of spinal surgery.