Huang Caijun, He Chunyan, Meng Xiao, Wang Caihong, Li Jianyu, Wu Feiyu, Lu Ming, Chen Suping
Department of Radiology, Guiqian International Hospital, Guiyang, China.
CT Research Center, GE Healthcare, Changsha, China.
Medicine (Baltimore). 2025 Aug 29;104(35):e44229. doi: 10.1097/MD.0000000000044229.
This study aimed to construct a new model based on quantitative computed tomography (QCT) body composition and clinical features for early prediction of acute pancreatitis (AP) severity. The clinical features and body composition of patients with clinical first-onset AP between January 1, 2024, and May 30, 2024, were analyzed. Concurrently, 100 healthy physical examination patients were included to collect the clinical characteristics and QCT parameters. AP was divided into mild AP (MAP, n = 66), moderate severe AP (MSAP, n = 18), and severe AP (SAP, n = 21), according to the revised Atlanta classification (RAC), subsequently, the patients were divided into the SAP (n = 21) and non-severe AP (NSAP; N = 84) groups. Clinical features and body composition parameters were used to determine risk factors for SAP using univariate and multivariate logistic regression methods. Efficacy was assessed using calibration curves, receiver operating characteristic (ROC) curves, and a decision curve analysis (DCA). A total of 105 patients with AP and 100 healthy individuals undergoing physical examinations were included in this study. Except for subcutaneous adipose tissue (SAT), all other body parameters showed statistically significant differences between the 2 groups (P < .05). Univariate and multivariate logistic regression analyses revealed that alcoholic etiology, C-reactive protein (CRP), total adipose tissue (TAT), skeletal muscle area (SMA) were independent predictive factors for SAP, and a model was derived. For the training cohort, the nomogram predicted SAP with area under the curve (AUC) of 0.87 (95% CI: 0.78-0.95), sensitivity of 0.80 (95% CI: 0.69-0.92), and specificity of 0.80 (95% CI: 0.64-0.96). For the validation cohort, the AUC was 0.81 (95% CI: 0.65-0.96), sensitivity was 0.56 (95% CI: 0.33-0.79), and specificity was 0.79 (95% CI: 0.57-1.00), indicating that the model had high discriminative power. The Hosmer-Lemeshow test P-value was .628, indicating that the nomogram performed well in calibration. Finally, the DCA demonstrated the clinical applicability of the model. The present study demonstrated that alcoholic etiology, CRP level, TAT, and SMA are independent risk factors for predicting SAP. The developed nomogram has good discrimination, calibration, and clinical applicability.
本研究旨在构建一种基于定量计算机断层扫描（QCT）身体成分和临床特征的新模型，用于早期预测急性胰腺炎（AP）的严重程度。分析了2024年1月1日至2024年5月30日临床首发AP患者的临床特征和身体成分。同时，纳入100例健康体检患者以收集临床特征和QCT参数。根据修订的亚特兰大分类（RAC），将AP分为轻度AP（MAP，n = 66）、中度重症AP（MSAP，n = 18）和重症AP（SAP，n = 21），随后，将患者分为SAP组（n = 21）和非重症AP（NSAP；N = 84）组。采用单因素和多因素逻辑回归方法，使用临床特征和身体成分参数确定SAP的危险因素。使用校准曲线、受试者操作特征（ROC）曲线和决策曲线分析（DCA）评估效能。本研究共纳入105例AP患者和100例接受体检的健康个体。除皮下脂肪组织（SAT）外，两组间所有其他身体参数均有统计学显著差异（P <.05）。单因素和多因素逻辑回归分析显示，酒精性病因、C反应蛋白（CRP）、总脂肪组织（TAT）、骨骼肌面积（SMA）是SAP的独立预测因素，并得出一个模型。对于训练队列，列线图预测SAP的曲线下面积（AUC）为0.87（95%CI：0.78 - 0.95），灵敏度为0.80（95%CI：0.69 - 0.92），特异度为0.80（95%CI：0.64 - 0.96）。对于验证队列，AUC为0.81（95%CI：0.65 - 0.96），灵敏度为0.56（95%CI：0.33 - 0.79），特异度为0.79（95%CI：0.57 - 1.00），表明该模型具有较高的判别力。Hosmer - Lemeshow检验P值为.628，表明列线图在校准方面表现良好。最后，DCA证明了该模型的临床适用性。本研究表明，酒精性病因、CRP水平、TAT和SMA是预测SAP的独立危险因素。所开发的列线图具有良好的判别力、校准度和临床适用性。
