Huang Xiaoli, Xu Jia, Hu Xiaogang, Yang Juntao, Liu Menggang
Department of Hepatobiliary and Pancreatic Surgery, The People's Hospital of Chongqing Liang Jiang New Area, Chongqing, China.
Department of Pharmacy, Chongqing Jiulongpo People's Hospital, Chongqing, China.
Front Med (Lausanne). 2025 Jul 2;12:1564742. doi: 10.3389/fmed.2025.1564742. eCollection 2025.
Acute pancreatitis (AP) is a common acute abdominal disease. The early identification of patients at risk of progression to severe AP (SAP) is crucial for developing effective therapeutic and nursing measures. Although many scoring systems exist for SAP risk assessment, none is widely accepted. Systemic inflammatory grade (SIG) is a novel systemic inflammation-based scoring system, but its relationship with AP, as well as the SAP risk prediction model involving SIG, has not been reported.
The demographic information, clinical data, and laboratory results of patients diagnosed with AP were collected. Baseline comparisons were made using the Wilcoxon rank-sum test, chi-square test and Fisher's exact test. Logistic regression analyses were used to identify independent predictors of SAP; these factors were then used to establish a nomogram model. The model's predictive efficacy and threshold values were evaluated using the receiver operating characteristic (ROC) curve and calibration curve. The decision curve analysis (DCA) and clinical impact curve (CIC) were used to further evaluate the benefit of the model.
Five hundred and ninety-two patients aged 18-92 years (median, 43 years) were included. In two stepwise regressions, SIG, C-reactive protein (CRP), prognostic nutritional index (PNI), and white blood cell (WBC) were all considered independent risk factors for SAP ( < 0.05). A nomogram prediction model was constructed using these four factors, with an area under the curve (AUC) of 0.940 (95% CI: 0.907-0.972, < 0.01). The AUC-ROC for 10-fold cross-validation was 0.942 ± 0.065. The results of the Hosmer and Lemeshow goodness of fit (GoF) test (-value = 0.596) and the Brier score (0.031, 95% CI 0.020-0.042), as well as the calibration curve, all demonstrated that the model exhibits good accuracy. DCA and CIC curves showed that the model provided good predictive value.
SIG, CRP, PNI, and WBC represent promising early prognostic markers for severe acute pancreatitis (SAP). A nomogram prediction model utilizing these markers offers effective early prediction for SAP.
急性胰腺炎（AP）是一种常见的急性腹部疾病。早期识别有进展为重症急性胰腺炎（SAP）风险的患者对于制定有效的治疗和护理措施至关重要。尽管存在许多用于SAP风险评估的评分系统，但没有一个被广泛接受。全身炎症分级（SIG）是一种基于全身炎症的新型评分系统，但其与AP的关系以及涉及SIG的SAP风险预测模型尚未见报道。
收集诊断为AP患者的人口统计学信息、临床数据和实验室结果。使用Wilcoxon秩和检验、卡方检验和Fisher精确检验进行基线比较。采用逻辑回归分析确定SAP的独立预测因素；然后使用这些因素建立列线图模型。使用受试者工作特征（ROC）曲线和校准曲线评估模型的预测效能和阈值。决策曲线分析（DCA）和临床影响曲线（CIC）用于进一步评估模型的益处。
纳入592例年龄在18 - 92岁（中位数43岁）的患者。在两次逐步回归中，SIG、C反应蛋白（CRP）、预后营养指数（PNI）和白细胞（WBC）均被视为SAP的独立危险因素（<0.05）。使用这四个因素构建了列线图预测模型，曲线下面积（AUC）为0.940（95%CI：0.907 - 0.972，<0.01）。10倍交叉验证的AUC-ROC为0.942±0.065。Hosmer和Lemeshow拟合优度（GoF）检验结果（P值 = 0.596）和Brier评分（0.031，95%CI 0.020 - 0.042）以及校准曲线均表明该模型具有良好的准确性。DCA和CIC曲线表明该模型具有良好的预测价值。
SIG、CRP、PNI和WBC是重症急性胰腺炎（SAP）有前景的早期预后标志物。利用这些标志物的列线图预测模型为SAP提供了有效的早期预测。
