Analysis of Risk Factors and Establishment of Prediction Model for Turbidity Toxicity Accumulation Syndrome in Patients with Chronic Atrophic Gastritis
- VernacularTitle:慢性萎缩性胃炎患者浊毒内蕴证风险因素分析及预测模型的建立
- Author:
Yican WANG
1
;
Chenggong ZHAO
1
;
Pengli DU
1
;
Jie WANG
1
;
Yuxi GUO
1
;
Haiyan BAI
1
;
Yongli HUO
1
;
Xiaomeng LANG
1
;
Zheng ZHI
1
;
Bolin LI
1
;
Jianping LIU
1
;
Yanru CAI
1
;
Jianming JIANG
1
;
Qian YANG
1
Author Information
- Publication Type:Journal Article
- Keywords: chronic atrophic gastritis; turbidity toxin accumulation syndrome; risk factor; prediction model
- From: Chinese Journal of Experimental Traditional Medical Formulae 2026;32(10):288-295
- CountryChina
- Language:Chinese
- Abstract: ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.
