Pei Bei, Wen Ziang, Yang Qi, Wang Jieyu, Cao Qinglin, Dai Longfei, Li Xuejun
The Graduated School, Anhui University of Traditional Chinese Medicine, Hefei, China.
The Graduated School, Anhui Medical University, Hefei, China.
Front Med (Lausanne). 2022 May 18;9:912331. doi: 10.3389/fmed.2022.912331. eCollection 2022.
To investigate the risk factors and construct a prediction model of chronic atrophic gastritis (CAG) patients with intestinal metaplasia or dysplasia.
The clinical data of 450 patients with CAG who were diagnosed and treated in the Department of Gastroenterology of the Second Affiliated Hospital of Anhui University of Traditional Chinese Medicine from June 2016 to February 2022 were collected. Single and multiple factors logistic regression analysis were used to explore the risk factors of intestinal metaplasia or dysplasia in patients of training cohort. Then, we constructed a model to predict the onset of intestinal metaplasia or dysplasia based on the data of training cohort, following which we tested the model in an external validation cohort of 193 patients from a local university teaching hospital. The ROC curve, calibration curve, and decision curve analysis were used to evaluate the accuracy of the prediction model.
(, HP) infection, pepsinogen I, gastrin-17, and the number of lesions were found to be independent rick factors of the model. The liner prediction model showed excellent predictive value in both training cohort and validation cohort.
HP infection, pepsinogen I, gastrin-17, and the number of lesions are independent risk factors for intestinal metaplasia or dysplasia in patients with CAG. The prediction model constructed based on these factors has a high accuracy and excellent calibration, which can provide a great basis for condition assessment and individualized treatment of the patients.
探讨慢性萎缩性胃炎(CAG)伴肠化生或异型增生患者的危险因素并构建预测模型。
收集2016年6月至2022年2月在安徽中医药大学第二附属医院胃肠科诊治的450例CAG患者的临床资料。采用单因素和多因素logistic回归分析探索训练队列患者肠化生或异型增生的危险因素。然后,基于训练队列的数据构建预测肠化生或异型增生发病的模型,随后在当地某大学教学医院193例患者的外部验证队列中对该模型进行测试。采用ROC曲线、校准曲线和决策曲线分析评估预测模型的准确性。
发现幽门螺杆菌(HP)感染、胃蛋白酶原I、胃泌素-17和病变数量是该模型的独立危险因素。线性预测模型在训练队列和验证队列中均显示出良好的预测价值。
HP感染、胃蛋白酶原I、胃泌素-17和病变数量是CAG患者肠化生或异型增生的独立危险因素。基于这些因素构建的预测模型具有较高的准确性和良好的校准性,可为患者的病情评估和个体化治疗提供重要依据。