Santipas Borriwat, Suvithayasiri Siravich, Trathitephun Warayos, Wilartratsami Sirichai, Luksanapruksa Panya
Department of Orthopedic Surgery, Faculty of Medicine Siriraj Hospital, Mahidol University.
Department of Orthopedics, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok, Thailand, Bangkok, Thailand.
Clin Spine Surg. 2025 Mar 1;38(2):E81-E88. doi: 10.1097/BSD.0000000000001659. Epub 2024 Aug 29.
This is a retrospective cohort study utilizing machine learning to predict postoperative complications in cervical spine metastases surgery.
The main objective is to develop a machine learning model that accurately predicts complications following cervical spine metastases surgery.
Cervical spine metastases surgery can enhance quality of life but carries a risk of complications influenced by various factors. Existing scoring systems may not include all predictive factors. Machine learning offers the potential for a more accurate predictive model by analyzing a broader range of variables.
Data from January 2012 to December 2020 were retrospectively collected from medical databases. Predictive models were developed using Gradient Boosting, Logistic Regression, and Decision Tree Classifier algorithms. Variables included patient demographics, disease characteristics, and laboratory investigations. SMOTE was used to balance the dataset, and the models were assessed using AUC, F1-score, precision, recall, and SHAP values.
The study included 72 patients, with a 29.17% postoperative complication rate. The Gradient Boosting model had the best performance with an AUC of 0.94, indicating excellent predictive capability. Albumin level, platelet count, and tumor histology were identified as top predictors of complications.
The Gradient Boosting machine learning model showed superior performance in predicting postoperative complications in cervical spine metastases surgery. With continuous data updating and model training, machine learning can become a vital tool in clinical decision-making, potentially improving patient outcomes.
Level III.
这是一项回顾性队列研究，利用机器学习预测颈椎转移瘤手术的术后并发症。
主要目的是开发一种机器学习模型，准确预测颈椎转移瘤手术后的并发症。
颈椎转移瘤手术可提高生活质量，但存在受多种因素影响的并发症风险。现有的评分系统可能未涵盖所有预测因素。机器学习通过分析更广泛的变量，为建立更准确的预测模型提供了潜力。
从医疗数据库中回顾性收集2012年1月至2020年12月的数据。使用梯度提升、逻辑回归和决策树分类器算法开发预测模型。变量包括患者人口统计学特征、疾病特征和实验室检查结果。采用SMOTE平衡数据集，并使用AUC、F1分数、精确率、召回率和SHAP值评估模型。
该研究纳入72例患者，术后并发症发生率为29.17%。梯度提升模型表现最佳，AUC为0.94，表明具有出色的预测能力。白蛋白水平、血小板计数和肿瘤组织学被确定为并发症的主要预测因素。
梯度提升机器学习模型在预测颈椎转移瘤手术术后并发症方面表现出卓越性能。随着数据的持续更新和模型训练，机器学习可成为临床决策的重要工具，有可能改善患者预后。
三级。
