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1.河北中医药大学,石家庄 050011
2.河北省中医院,石家庄 050011
3.河北省浊毒证重点实验室,石家庄 050011
Received:20 December 2024,
Revised:2025-01-06,
Accepted:17 January 2025,
Online First:06 February 2025,
Published:20 May 2026
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王艺灿,赵成功,杜朋丽等.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[J].中国实验方剂学杂志,2026,32(10):288-295.
WANG Yican,ZHAO Chenggong,DU Pengli,et al.Analysis of Risk Factors and Establishment of Prediction Model for Turbidity Toxicity Accumulation Syndrome in Patients with Chronic Atrophic Gastritis[J].Chinese Journal of Experimental Traditional Medical Formulae,2026,32(10):288-295.
王艺灿,赵成功,杜朋丽等.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[J].中国实验方剂学杂志,2026,32(10):288-295. DOI: 10.13422/j.cnki.syfjx.20250211.
WANG Yican,ZHAO Chenggong,DU Pengli,et al.Analysis of Risk Factors and Establishment of Prediction Model for Turbidity Toxicity Accumulation Syndrome in Patients with Chronic Atrophic Gastritis[J].Chinese Journal of Experimental Traditional Medical Formulae,2026,32(10):288-295. DOI: 10.13422/j.cnki.syfjx.20250211.
目的
2
探究慢性萎缩性胃炎(CAG)浊毒内蕴证的危险因素,并建立预测模型。
方法
2
收集河北省中医院2021年7月至2022年3月参与“香连化浊颗粒阻断慢性萎缩性胃炎炎癌转化的临床研究”的180例CAG患者的临床资料。倾向性评分匹配控制混杂因素后,将患者按7∶3的比例分为训练集(training set,dev)和验证集(validation set vad)。采用单因素Logistic回归分析、最小绝对收缩和选择算子(Lasso)回归算法研究训练集中CAG浊毒内蕴证发生的危险因素。随后,利用训练集数据建立模型(model 1se)来预测CAG浊毒内蕴证的危险因素。用受试者工作特征(ROC)曲线、霍斯默(H-L)检验、校准图(calibration plot)和决策曲线分析(DCA)等方法评估预测模型的准确性。
结果
2
“年龄、BMI、家庭肿瘤史、工作生活满意度、苔黄腻且脉滑、身体困重”是模型的独立危险因素。该预测模型在训练集和验证集中均显示出良好的预测价值。
结论
2
该研究构建的CAG浊毒内蕴证风险预测模型具有较高的区分性和良好的校准度,可为疾病诊断和患者的个体化治疗提供良好的临床依据。
Objective
2
This paper aims to explore the risk factors for chronic atrophic gastritis (CAG) with turbidity toxin accumulation syndrome and establish a prediction model.
Methods
2
Clinical 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).
Results
2
Age, 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.
Conclusion
2
The 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.
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