新医学2026，Vol.57Issue(4)：412-421,10.DOI:10.12464/j.issn.0253-9802.2025-0481
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
A machine learning-based study of serological biomarkers for predicting intestinal necrosis in patients with adhesive small bowel obstruction
摘要
Abstract
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.关键词
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Key words
Machine learning/Homocysteine/Adhesive small bowel obstruction/Intestinal necrosis/Serum biomarkers/Extra trees/SHAP analysis引用本文复制引用
刘入铭,朱优龙,冯嘉伟..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[J].新医学,2026,57(4):412-421,10.基金项目
江苏省医学引进新技术(2024-021-R1) （2024-021-R1）
徐州市科技计划项目(KC23173) （KC23173）
