Division of Pediatric Surgery, Department of Surgery, University of California San Diego, Rady Children's Hospital San Diego, CA.
Division of Pediatric Surgery, Children's Hospital Los Angeles, CA; Department of Surgery, Keck School of Medicine of the University of Southern California, Los Angeles, CA.
Surgery. 2023 Oct;174(4):934-939. doi: 10.1016/j.surg.2023.07.008. Epub 2023 Aug 12.
The purpose of this study was to accurately predict pediatric choledocholithiasis with clinical data using a computational machine learning algorithm.
A multicenter retrospective cohort study was performed on children <18 years of age who underwent cholecystectomy between 2016 to 2019 at 10 pediatric institutions. Demographic data, clinical findings, laboratory, and ultrasound results were evaluated by bivariate analyses. An Extra-Trees machine learning algorithm using k-fold cross-validation was used to determine predictive factors for choledocholithiasis. Model performance was assessed using the area under the receiver operating characteristic curve on a validation dataset.
A cohort of 1,597 patients was included, with an average age of 13.9 ± 3.2 years. Choledocholithiasis was confirmed in 301 patients (18.8%). Obesity was the most common comorbidity in all patients. Choledocholithiasis was associated with the finding of a common bile duct stone on ultrasound, increased common bile duct diameter, and higher serum concentrations of aspartate aminotransferase, alanine transaminase, lipase, and direct and peak total bilirubin. Nine features (age, body mass index, common bile duct stone on ultrasound, common bile duct diameter, aspartate aminotransferase, alanine transaminase, lipase, direct bilirubin, and peak total bilirubin) were clinically important and included in the machine learning algorithm. Our 9-feature model deployed on new patients was found to be highly predictive for choledocholithiasis, with an area under the receiver operating characteristic score of 0.935.
This multicenter study uses machine learning for pediatric choledocholithiasis. Nine clinical factors were highly predictive of choledocholithiasis, and a machine learning model trained using medical and laboratory data was able to identify children at the highest risk for choledocholithiasis.
本研究旨在使用计算机器学习算法，通过临床数据准确预测小儿胆总管结石。
对 2016 年至 2019 年期间在 10 家儿科机构接受胆囊切除术的<18 岁儿童进行了一项多中心回顾性队列研究。通过双变量分析评估了人口统计学数据、临床发现、实验室和超声结果。使用 k 折交叉验证的 Extra-Trees 机器学习算法确定胆总管结石的预测因素。在验证数据集上使用受试者工作特征曲线下面积评估模型性能。
纳入了 1597 例患者，平均年龄为 13.9±3.2 岁。301 例（18.8%）患者确诊为胆总管结石。所有患者中最常见的合并症是肥胖。胆总管结石与超声发现胆总管结石、胆总管直径增大、血清天门冬氨酸转氨酶、丙氨酸转氨酶、脂肪酶、直接和峰值总胆红素浓度升高有关。9 个特征（年龄、体重指数、超声发现胆总管结石、胆总管直径、天门冬氨酸转氨酶、丙氨酸转氨酶、脂肪酶、直接胆红素和峰值总胆红素）具有临床重要性，包含在机器学习算法中。在新患者中部署的 9 个特征模型被发现对胆总管结石具有高度预测性，受试者工作特征评分下面积为 0.935。
这项多中心研究使用机器学习来预测小儿胆总管结石。9 个临床因素对胆总管结石有高度预测性，使用医学和实验室数据训练的机器学习模型能够识别出患胆总管结石风险最高的儿童。
