Zhang Lanfang, Yang Lu, Meng Lijun, Zhang Haiyun, Zhu Yanli, Yang Fang, Qin Yongmei
Department of Gastroenterology, The First Affiliated Hospital of Henan Medical University, Xinxiang, China.
Front Med (Lausanne). 2025 Jul 29;12:1549901. doi: 10.3389/fmed.2025.1549901. eCollection 2025.
Gastritis, a global inflammatory disorder, progresses from symptomatic discomfort to potentially malignant changes. Existing staging systems (e.g., OLGA) focus on cancer risk but ignore modifiable factors like inflammation markers and infection. We developed a Nomogram model based on baseline data, inflammatory markers and infectious pathogens for predicting the prognosis of gastritis patients and validating it.
Retrospectively collect the clinical data of patients diagnosed with gastritis, including baseline characteristics, inflammatory markers, and pathogenic infection test results. Univariate and multivariate analyses were performed to identify independent risk factors associated with the prognosis of gastritis patients, based on which a Nomogram prediction model was constructed. The model's accuracy, calibration, and discriminative ability were internally validated using the concordance index (C-index), calibration curve, and the area under the receiver operating characteristic curve (AUC).
Among the 185 patients in the training set, 43 (23.24%) had poor treatment outcomes, while in the validation set of 79 patients, 18 (22.78%) exhibited poor treatment outcomes. No statistically significant differences were observed between the training and validation sets in terms of the incidence of poor treatment outcomes, baseline characteristics, or inflammatory and infectious markers parameters ( > 0.05). Univariate analysis revealed significant differences ( < 0.05) between the poor-outcome and favorable-outcome groups in dietary score, white blood cell count, neutrophil percentage, lymphocyte percentage, C-reactive protein (CRP) level, erythrocyte sedimentation rate (ESR), serum albumin level, and infection status. Multivariate logistic regression analysis identified dietary score, neutrophil proportion, CRP, ESR, serum albumin level, and infection as independent risk factors for poor endoscopic treatment outcome ( < 0.05). Subsequently, a nomogram prediction model was constructed. The model demonstrated good calibration and fit between predicted and actual outcomes in both the training and validation sets. ROC curve analysis showed that the nomogram model achieved AUC values of 0.808 in the training set and 0.800 in the validation set for predicting gastritis prognosis.
The Nomogram model constructed in this study based on baseline data, inflammation indicators and infectious pathogens can effectively predict the prognosis of patients with gastritis, which can provide a powerful reference for clinical individualized treatment decision-making.
胃炎是一种全球性炎症性疾病，可从有症状的不适发展为潜在的恶性病变。现有的分期系统（如OLGA）侧重于癌症风险，但忽略了炎症标志物和感染等可改变因素。我们基于基线数据、炎症标志物和感染病原体开发了一种列线图模型，用于预测胃炎患者的预后并进行验证。
回顾性收集诊断为胃炎患者的临床资料，包括基线特征、炎症标志物和病原体感染检测结果。进行单因素和多因素分析，以确定与胃炎患者预后相关的独立危险因素，并在此基础上构建列线图预测模型。使用一致性指数（C指数）、校准曲线和受试者工作特征曲线下面积（AUC）对模型的准确性、校准度和鉴别能力进行内部验证。
在训练集的185例患者中，43例（23.24%）治疗效果不佳，而在79例患者的验证集中，18例（22.78%）治疗效果不佳。在治疗效果不佳的发生率、基线特征或炎症和感染标志物参数方面，训练集和验证集之间未观察到统计学显著差异（>0.05）。单因素分析显示，治疗效果不佳组与良好组在饮食评分、白细胞计数、中性粒细胞百分比、淋巴细胞百分比、C反应蛋白（CRP）水平、红细胞沉降率（ESR）、血清白蛋白水平和感染状态方面存在显著差异（<0.05）。多因素逻辑回归分析确定饮食评分、中性粒细胞比例、CRP、ESR、血清白蛋白水平和感染是内镜治疗效果不佳的独立危险因素（<0.05）。随后，构建了列线图预测模型。该模型在训练集和验证集中均显示出预测结果与实际结果之间良好的校准度和拟合度。ROC曲线分析表明，列线图模型在训练集中预测胃炎预后的AUC值为0.808，在验证集中为0.800。
本研究基于基线数据、炎症指标和感染病原体构建的列线图模型能够有效预测胃炎患者的预后，可为临床个体化治疗决策提供有力参考。
