Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Department of Anesthesiology, Xi'an Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Med Sci Monit. 2024 Sep 26;30:e945310. doi: 10.12659/MSM.945310.
BACKGROUND The objective of this study was to develop and validate machine learning (ML) algorithms to predict the 30-day and 6-month risk of deteriorating functional status following surgical treatment for thoracic spinal stenosis (TSS). We aimed to provide surgeons with tools to identify patients with TSS who have a higher risk of postoperative functional decline. MATERIAL AND METHODS The records of 327 patients with TSS who completed both follow-up visits were analyzed. Our primary endpoint was the dichotomized change in the perioperative Japanese Orthopedic Association (JOA) score, categorized based on whether it deteriorated or not. The models were developed using Naïve Bays, LightGBM, XGBoost, logistic regression, and random forest classification models. The model performance was assessed by accuracy and the c-statistic. ML algorithms were trained, optimized, and tested. RESULTS The best-performing algorithms for predicting functional decline at 30 days and 6 months after TSS surgery were XGBoost (accuracy=88.17%, c-statistic=0.83) and Naïve Bays (accuracy=86.03%, c-statistic=0.80). Both algorithms presented good calibration and discrimination in our testing data. We identified several significant predictors, including poor quality of intraoperative SSEP/MEP baseline, poor quality of preoperative SSEP, duration of symptoms, operated level, and motor dysfunction of the lower extremity. CONCLUSIONS The best-performing algorithms for predicting functional decline at 30 days and 6 months after TSS surgery were XGBoost (accuracy=88.17%, c-statistic=0.83) and Naïve Bays (accuracy=86.03%, c-statistic=0.80). Both algorithms presented good calibration and discrimination in our testing data. We identified several significant predictors, including poor quality of intraoperative SSEP/MEP baseline, poor quality of preoperative SSEP, duration of symptoms, operated level, and motor dysfunction of the lower extremity.
本研究旨在开发和验证机器学习（ML）算法，以预测胸椎管狭窄症（TSS）患者手术后 30 天和 6 个月功能状态恶化的风险。我们的目标是为外科医生提供工具，以识别 TSS 患者术后功能下降风险较高的患者。
分析了 327 例完成两次随访的 TSS 患者的记录。我们的主要终点是根据术后日本矫形协会（JOA）评分是否恶化而分为二分类的变化。模型采用朴素贝叶斯、LightGBM、XGBoost、逻辑回归和随机森林分类模型进行开发。通过准确性和 c 统计量评估模型性能。对 ML 算法进行了训练、优化和测试。
预测 TSS 手术后 30 天和 6 个月功能下降的最佳算法是 XGBoost（准确性=88.17%，c 统计量=0.83）和朴素贝叶斯（准确性=86.03%，c 统计量=0.80）。这两种算法在我们的测试数据中均表现出良好的校准和区分能力。我们确定了几个重要的预测指标，包括术中 SSEP/MEP 基线质量差、术前 SSEP 质量差、症状持续时间、手术水平和下肢运动功能障碍。
预测 TSS 手术后 30 天和 6 个月功能下降的最佳算法是 XGBoost（准确性=88.17%，c 统计量=0.83）和朴素贝叶斯（准确性=86.03%，c 统计量=0.80）。这两种算法在我们的测试数据中均表现出良好的校准和区分能力。我们确定了几个重要的预测指标，包括术中 SSEP/MEP 基线质量差、术前 SSEP 质量差、症状持续时间、手术水平和下肢运动功能障碍。
