基于机器学习的血清学标志物预测粘连性小肠梗阻患者肠坏死的研究OA
A machine learning-based study of serological biomarkers for predicting intestinal necrosis in patients with adhesive small bowel obstruction
目的 探讨基于机器学习的血清学标志物预测粘连性小肠梗阻(ASBO)手术患者不可逆性透壁性肠坏死(ITIN)的价值.方法 前瞻性纳入 2023年 2月至 2025年 2月徐州市中心医院 133例接受手术治疗的 ASBO 患者,根据术中探查和病理结果分为坏死组(68例)和非坏死组(65例).检测血清血同型半胱氨酸(HCY)、内毒素、降钙素原(PCT)、C 反应蛋白(CRP)、白细胞介素-6(IL-6)、IL-1β、IL-5、中性粒细胞明胶酶相关脂质运载蛋白(NGAL)、乳酸脱氢酶、维生素 B12、叶酸以及年龄、性别、体质量指数等 14项指标.构建 20个机器学习模型,数据集按 8:2随机分为训练集(n=106)和测试集(n=27).在测试集上通过 ROC、DCA、校准曲线等评估模型性能,并进行 SHAP特征重要性分析.结果 坏死组 HCY、内毒素、PCT、CRP 水平均高于非坏死组(均 P＜0.05).极度随机树(Extra Trees)模型性能最优,AUC 为 0.977(95%CI 0.955～0.999),灵敏度 92.6%(95%CI 83.9%～96.8%),特异度 95.4%(95%CI 87.3%～98.4%).SHAP分析显示 HCY 为最重要的预测因子(平均|SHAP值|=0.119 6),其次为内毒素(0.100 8)和 CRP(0.055 7).决策曲线分析表明,在阈值概率 0.2～0.8范围内,Extra Trees模型净获益显著高于"全部治疗"或"全部不治疗"策略.校准曲线显示良好一致性(Brier Score=0.098).结论 基于机器学习的多标志物模型可准确预测 ASBO 手术患者肠坏死风险,Extra Trees模型表现最佳.HCY 是最重要预测因子,为临床术前风险评估提供客观依据.未来需进一步开发适用于保守治疗ASBO患者的综合预测模型.
Objective To explore the value of machine learning-based serological markers in predicting irreversible transmural intestinal necrosis(ITIN)in surgical patients with adhesive small bowel obstruction(ASBO).Methods A total of 133 ASBO patients who underwent surgical treatment at Xuzhou Central Hospital from February 2023 to February 2025 were prospectively enrolled.According to intraoperative exploration and pathological results,patients were divided into necrosis group(n=68)and non-necrosis group(n=65).Fourteen indicators were assessed,including serum homocysteine(HCY),endotoxin,procalcitonin(PCT),C-reactive protein(CRP),interleukin-6(IL-6),IL-1β,IL-5,neutrophil gelatinase-associated lipocalin(NGAL),lactate dehydrogenase(LDH),vitamin B12(VB12),folate,and age,gender,and body mass index(BMI).Twenty machine learning models were constructed.The dataset was randomly divided into a training set(n=106)and a test set(n=27)at an 8:2 ratio.Model performance was evaluated on the test set using ROC curves,decision curve analysis(DCA),calibration curves,and SHAP feature importance analysis was performed.Results Levels of HCY,endotoxin,PCT,and CRP were higher in the necrosis group than in the non-necrosis group(all P＜0.05).The Extra Trees model demonstrated optimal performance with an AUC of 0.977(95%CI:0.955-0.999),sensitivity of 92.6%(95%CI:83.9%-96.8%),and specificity of 95.4%(95%CI:87.3%-98.4%).SHAP analysis identified HCY as the most important predictor(mean|SHAP value|=0.119 6),followed by endotoxin(0.100 8)and CRP(0.055 7).Decision curve analysis showed that within a threshold probability range of 0.2-0.8,the net benefit of the Extra Trees model was significantly higher than that of the"treat-all"or"treat-none"strategy.The calibration curve demonstrated good agreement(Brier Score=0.098).Conclusions A machine learning-based multi-biomarker models can accurately predict the risk of intestinal necrosis in surgical ASBO patients,with the Extra Trees model showing the best performance.HCY is the most important predictor,providing an objective basis for preoperative clinical risk assessment.Future development of a comprehensive prediction model applicable to conservatively treated ASBO patients is needed.
刘入铭;朱优龙;冯嘉伟
东南大学附属徐州市中心医院胃肠外科,江苏 徐州 221009东南大学附属徐州市中心医院胃肠外科,江苏 徐州 221009常州市第一人民医院甲状腺外科,江苏 常州 213000
机器学习同型半胱氨酸粘连性小肠梗阻肠坏死血清学标志物极度随机树SHAP分析
Machine learningHomocysteineAdhesive small bowel obstructionIntestinal necrosisSerum biomarkersExtra treesSHAP analysis
《新医学》 2026 (4)
412-421,10
江苏省医学引进新技术(2024-021-R1)徐州市科技计划项目(KC23173)
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