Song Hong, Yang Juan, Li Jiao, Deng Cui, Zhang SiMin, Zheng Sheng
Department of Gerontology, Taiyuan Central Hospital, Taiyuan City, Shanxi Province, China.
Department of Gastroenterology, Affiliated Hospital of Yunnan University, Kunming City, Yunnan Province, China.
Clinics (Sao Paulo). 2025 Apr 23;80:100644. doi: 10.1016/j.clinsp.2025.100644. eCollection 2025.
This research aimed to determine the feasibility and accuracy of CLR and clinical features to formulate a prediction model for Peptic Ulcer (PU)-induced Upper Gastrointestinal Bleeding (UGIB).
The clinical data of 146 PU patients were prospectively collected, and patients were divided into the UGIB group (n = 48) and the non-UGIB group (n = 98). The factors affecting UGIB were analyzed using multifactorial logistic regression and collinearity analysis. The prediction model of UGIB was constructed, the predictive value of which was analyzed using the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), while the accuracy was analyzed using the calibration curve and Hosmer Lemeshow goodness-of-fit tests, and the application value was assessed using decision curve analysis (DCA).
Statistical significance was observed between the two groups regarding HP infection, ulcer diameter, ulcer stage, use of nonsteroidal anti-inflammatory drugs, Neutrophil, LYM, NEUT/LYM Ratio (NLR), CRP, and CLR. HP infection, ulcer stage, use of NSAIDs, NLR, and CLR were independent risk factors for UGIB, and PCT was a non-independent risk factor. The AUC for this model was 0.921. The calibration curve of the model matched the actual curve. The model achieved a better fitting effect in predicting UGIB (χ = 8.5069, df = 8, p = 0.3856) and had a better clinical application value.
A predictive model for PU-induced UGIB, based on CLR and clinical features, can assist in developing clinical treatment plans to prevent UGIB.
本研究旨在确定C反应蛋白（CLR）和临床特征用于制定消化性溃疡（PU）所致上消化道出血（UGIB）预测模型的可行性和准确性。
前瞻性收集146例PU患者的临床资料，将患者分为UGIB组（n = 48）和非UGIB组（n = 98）。采用多因素logistic回归和共线性分析来分析影响UGIB的因素。构建UGIB的预测模型，使用受试者工作特征曲线（ROC）和曲线下面积（AUC）分析其预测价值，同时使用校准曲线和Hosmer Lemeshow拟合优度检验分析准确性，并使用决策曲线分析（DCA）评估应用价值。
两组在幽门螺杆菌（HP）感染、溃疡直径、溃疡分期、非甾体抗炎药的使用、中性粒细胞、淋巴细胞、中性粒细胞/淋巴细胞比值（NLR）、C反应蛋白（CRP）和CLR方面存在统计学差异。HP感染、溃疡分期、非甾体抗炎药的使用、NLR和CLR是UGIB的独立危险因素，而降钙素原（PCT）是一个非独立危险因素。该模型的AUC为0.921。模型的校准曲线与实际曲线匹配。该模型在预测UGIB方面具有较好的拟合效果（χ = 8.5069，自由度 = 8，p = 0.3856），并且具有较好的临床应用价值。
基于CLR和临床特征的PU所致UGIB预测模型可有助于制定临床治疗方案以预防UGIB。
