Pang Zixiang, Liang Jiawei, Chen Jiayi, Ou Yangqin, Wu Qinmian, Huang Shengsheng, Huang Shengbin, Chen Yuanming
Department Orthopedics Ward 3 (Spine and Osteopathy Surgery), Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Department of Spinal and Bone Disease Surgery, Sixth Affiliated Hospital of Guangxi Medical University, Yulin, Guangxi, China.
Front Med (Lausanne). 2025 Jul 30;12:1590248. doi: 10.3389/fmed.2025.1590248. eCollection 2025.
OBJECTIVES: Emerging systemic immune-inflammatory biomarkers demonstrate potential for predicting postoperative complications. This study develops machine learning models to assess the combined predictive value of Aggregate Index of Systemic Inflammation (AISI), Systemic Immune-Inflammation Index (SII), CRP-Albumin-Lymphocyte (CALLY) index and Subcutaneous Lumbar Spine Index (SLSI) for surgical site infection (SSI) following posterior lumbar spinal fusion. METHODS: This retrospective study analyzed 2,921 patients undergoing posterior lumbar spinal fusion at two tertiary hospitals in Guangxi (August 2017-January 2025). Data were partitioned into training (70%) and validation (30%) groups. Feature selection via univariate regression analysis identified predictive variables, followed by model development using ten machine learning algorithms: logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), XGBoost, neural network, K-nearest neighbors(KNN), AdaBoost, LightGBM, and CatBoost. Hyperparameters were optimized with 10-fold cross-validation. The top seven performing models (assessed by AUC, accuracy, sensitivity, specificity, precision, and F1 scores) were integrated into a dynamic nomogram. Internal validation employed ROC analysis and calibration curves, while Shapley Additive Explanations (SHAP) values interpreted feature importance in the optimal model. RESULTS: Among 2,921 screened patients, 1,272 met inclusion criteria. Consensus feature selection across the seven top-performing ML algorithms identified AISI, SII, CALLY and SLSI as independent predictors of SSI. The derived nomogram demonstrated exceptional discrimination (training groups AUC = 0.966; C-index = 0.993, 95% CI 0.984-0.995) and excellent calibration. Additionally, the SHAP method emphasized the significance of AISI, SII, CALLY and SLSI as independent predictors influencing the machine learning model's predictions. CONCLUSION: The AISI, SII, CALLY and SLSI emerged as independent predictors of SSI following posterior lumbar spinal fusion. Our machine learning-derived nomogram demonstrated high discriminative accuracy and clinical applicability through dynamic risk stratification. Leveraging the SHAP methodology enhances model interpretability, thereby empowering healthcare providers to proactively mitigate SSI occurrences and enhance overall patient outcomes.
目的：新兴的全身免疫炎症生物标志物显示出预测术后并发症的潜力。本研究开发机器学习模型，以评估全身炎症综合指数（AISI）、全身免疫炎症指数（SII）、CRP-白蛋白-淋巴细胞（CALLY）指数和腰椎皮下指数（SLSI）对腰椎后路融合术后手术部位感染（SSI）的联合预测价值。 方法：这项回顾性研究分析了广西两家三级医院2921例接受腰椎后路融合术的患者（2017年8月至2025年1月）。数据被分为训练组（70%）和验证组（30%）。通过单变量回归分析进行特征选择，确定预测变量，然后使用十种机器学习算法开发模型：逻辑回归（LR）、支持向量机（SVM）、随机森林（RF）、梯度提升机（GBM）、XGBoost、神经网络、K近邻（KNN）、AdaBoost、LightGBM和CatBoost。使用10折交叉验证对超参数进行优化。将表现最佳的前七个模型（通过AUC、准确率、敏感性、特异性、精确率和F1分数评估）整合到动态列线图中。内部验证采用ROC分析和校准曲线，而Shapley加性解释（SHAP）值解释了最佳模型中的特征重要性。 结果：在2921例筛查患者中,1272例符合纳入标准。在七个表现最佳的机器学习算法中进行的共识特征选择确定AISI、SII、CALLY和SLSI为SSI的独立预测因子。所推导的列线图显示出出色的辨别力（训练组AUC = 0.966；C指数 = 0.993，95%CI 0.984 - 0.995）和良好的校准。此外，SHAP方法强调了AISI、SII、CALLY和SLSI作为影响机器学习模型预测的独立预测因子的重要性。 结论：AISI、SII、CALLY和SLSI是腰椎后路融合术后SSI的独立预测因子。我们通过机器学习得出的列线图通过动态风险分层显示出高辨别准确性和临床适用性。利用SHAP方法增强了模型的可解释性，从而使医疗保健提供者能够主动减少SSI的发生并改善患者的总体预后。
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