摘要
目的探讨结直肠息肉切除术后病理升级的危险因素并构建预测模型。方法纳入在昆明市第一人民医院消化内科就诊的已行黏膜活检的结直肠息肉病人342例,根据病理差异分为病理升级组(112例)和病理一致组(230例)。分析病人术后病理升级的危险因素,构建预测模型并评价模型的预测效能。结果息肉分叶、黏膜充血、有蒂息肉、年龄、息肉直径、清蛋白、血小板与淋巴细胞比率、黏膜粗糙为结直肠息肉切除术后病理升级的独立危险因素(P<0.05)。依据危险因素构建的列线图预测模型的受试者工作特征曲线下面积为0.808(95%CI=0.754~0.856),灵敏度为62.5%,特异度为86.5%。校准曲线和临床决策曲线均表明预测模型具有较好的预测价值。结论本研究构建的结直肠息肉切除术后病理升级的预测模型预测效能较好,值得临床推广。
Objective To explore the risk factors for pathological upgrading after colorectal polypectomy and construct a predictive model.Methods We included 342 patients with colorectal polyps who had undergone a mucosal biopsy at the Department of Gastroenterology,the First People’s Hospital of Kunming City.According to postoperative pathological results,the patients were divided into pathological upgrade group(112 cases)and pathological consistency group(230 cases).The risk factors for postoperative pathological upgrading were analyzed,and a predictive model was constructed and evaluated.Results Lobulated polyps,mucosal congestion,pedunculated polyps,age,polyp diameter,albumin,platelet-to-lymphocyte ratio,and mucosal roughness were independent risk factors for pathological upgrading after colorectal polyp resection(P<0.05).For the nomogram prediction model constructed with the risk factors,the area under the receive operating characteristic curve was 0.808(95%CI=0.754-0.856),with a sensitivity of 62.5%and a specificity of 86.5%.Both the calibration curve and clinical decision curve showed that the prediction model had good prediction value.Conclusion The predictive model constructed in this study shows favorable efficacy for predicting pathological upgrading following colorectal polypectomy,which is worthy of clinical promotion.
作者
陈柬树
周冬玉
杨婷
宋亚峰
刘凤娇
张虹雨
CHEN Jianshu;ZHOU Dongyu;YANG Ting;SONG Yafeng;LIU Fengjiao;ZHANG Hongyu(Graduate School of Kunming Medical University,Kunming 650032,China)
出处
《青岛大学学报(医学版)》
2025年第2期280-284,共5页
Journal of Qingdao University(Medical Sciences)
基金
昆明市卫生科技人才培养项目“千工程”(2021-SW(后备)-17)。
关键词
肠息肉
内镜黏膜下剥离术
病理学
预测
列线图
intestinal polyps
endoscopic mucosal resection
pathology
forecasting
nomograms
