疑难病杂志2026，Vol.25Issue(9)：1055-1061,7.DOI:10.3969/j.issn.1671-6450.2026.09.006
基于血清标志物动态变化的幽门螺杆菌阴性慢性胃炎疾病进展预测模型构建
Construction of a predictive model for the progression of Helicobacter pylori negative chronic gastritis based on dy-namic changes in serum biomarkers
摘要
Abstract
Objective To construct a predictive model for disease progression in patients with Helicobacter pylori(Hp)-negative chronic gastritis based on dynamic changes in serum biomarkers,so as to provide evidence for early identifica-tion of high-risk patients and individualized intervention strategies.Methods A total of 128 patients with Hp-negative chronic gastritis admitted to the Department of Gastroenterology,Inner Mongolia People's Hospital from January 2021 to De-cember 2024 were enrolled.According to disease progression status,the patients were divided into a disease-stable group and a disease-progression group.Serum levels of pepsinogen I(PGⅠ),pepsinogen II(PGⅡ),gastrin-17(G-17),and interleukin-6(IL-6)were measured at enrollment(T0)and at 3,6,and 12 months of follow-up(T1-T3).The absolute changes and 12-month change rates of these indicators were calculated.Lasso regression was used to screen core predictors of disease progres-sion in Hp-negative chronic gastritis,and multivariate logistic regression was performed to identify independent influencing factors and construct the predictive model.Receiver operating characteristic(ROC)curves and calibration curves were used to evaluate model performance.Results Compared with the disease-stable group,the disease-progression group had a higher proportion of patients aged≥60 years and a longer disease course(χ2/P=7.686/0.006,t/P=5.243/<0.001).At T1-T3,serum PGⅠ levels were significantly lower,while PGⅡ,G-17,and IL-6 levels were significantly higher in the disease-progression group than in the disease-stable group(F/P=21.853/<0.001,43.573/<0.001,34.068/<0.001,and 42.497/<0.001).The ΔPGⅠ,ΔPGⅡ,ΔG-17,ΔIL-6 values and their corresponding change rates were all significantly higher in the disease-progression group than in the disease-stable group(t/P=3.109/0.004,5.077/<0.001,6.179/<0.001,5.455/<0.001,3.132/0.003,5.538/<0.001,5.437/<0.001,and 6.445/<0.001).Multivariate logistic regression analysis showed that the change rates of PGⅠ,PGⅡ,G-17,and IL-6 were independent risk factors for disease progression in Hp-negative chronic gastritis[OR(95%CI)=1.213(1.031-1.426),1.128(1.026-1.240),1.074(1.005-1.148),and 1.313(1.133-1.521),respectively].ROC curve analysis showed that the area under the curve of the model was 0.934(95%CI:0.871-0.997),with a sensitivity of 87.50%and a spe-cificity of 95.83%.The predicted probabilities were highly consistent with the actual outcomes.During 12 months of follow-up,the disease progression rate among the 128 patients was 25.00%(32/128).Conclusion The predictive model constructed based on the dynamic change rates of serum PGⅠ,PGⅡ,G-17,and IL-6 can predict the risk of disease progression in pa-tients with Hp-negative chronic gastritis.关键词
慢性胃炎/幽门螺杆菌阴性/血清标志物/动态变化/预测模型/列线图Key words
Chronic gastritis/Helicobacter pylori negative/Serum biomarkers/Dynamic changes/Predictive model/Nomogram分类
医药卫生引用本文复制引用
张春凤,张苗,刘峥璐..基于血清标志物动态变化的幽门螺杆菌阴性慢性胃炎疾病进展预测模型构建[J].疑难病杂志,2026,25(9):1055-1061,7.基金项目
内蒙古自治区科技计划项目(2022YFSH0014) Inner Mongolia Autonomous Region Science and Technology Program Project(2022YFSH0014) （2022YFSH0014）
