Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
BMC Urol. 2024 Jul 8;24(1):140. doi: 10.1186/s12894-024-01529-1.
The objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL).
We retrospectively reviewed the clinical data of 337 patients who received PCNL between May 2020 and June 2022. In our study, 80% of the data were used as the training set, and the remaining data were used as the testing set. Separate prediction models based on the six machine learning algorithms were created using the training set. The predictive performance of each machine learning model was determined by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity using the testing set. We used coefficients to interpret the contribution of each variable to the predictive performance.
Among the six machine learning algorithms, the support vector machine (SVM) delivered the best performance with accuracy of 0.868, AUC of 0.942 (95% CI 0.890-0.994) in the testing set. Further analysis using the SVM model showed that prealbumin contributed the most to the prediction of the outcome, followed by preoperative urine culture, systemic immune-inflammation (SII), neutrophil to lymphocyte ratio (NLR), staghorn stones, fibrinogen, operation time, preoperative urine white blood cell (WBC), preoperative urea nitrogen, hydronephrosis, stone burden, sex and preoperative lymphocyte count.
Machine learning-based prediction models can accurately predict the possibility of SIRS after PCNL in advance by learning patient clinical data, and should be used to guide surgeons in clinical decision-making.
本研究旨在开发和评估机器学习模型预测经皮肾镜碎石取石术（PCNL）后全身炎症反应综合征（SIRS）发生可能性的性能。
我们回顾性分析了 2020 年 5 月至 2022 年 6 月期间接受 PCNL 的 337 例患者的临床资料。在本研究中，80%的数据被用作训练集，其余数据被用作测试集。使用训练集为每个机器学习算法创建了基于预测模型。使用测试集通过接收者操作特征曲线（AUC）下的面积、准确性、敏感性和特异性来确定每个机器学习模型的预测性能。我们使用系数来解释每个变量对预测性能的贡献。
在六种机器学习算法中，支持向量机（SVM）在测试集的准确性为 0.868、AUC 为 0.942（95%CI 0.890-0.994），表现最佳。使用 SVM 模型进行的进一步分析表明，白蛋白前体对预测结果的贡献最大，其次是术前尿液培养、全身免疫炎症（SII）、中性粒细胞与淋巴细胞比值（NLR）、鹿角结石、纤维蛋白原、手术时间、术前尿液白细胞（WBC）、术前尿素氮、肾积水、结石负荷、性别和术前淋巴细胞计数。
基于机器学习的预测模型可以通过学习患者的临床数据提前准确预测 PCNL 后 SIRS 的发生可能性，应用于指导外科医生的临床决策。
