Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China.
J Dig Dis. 2024 Apr;25(4):238-247. doi: 10.1111/1751-2980.13271. Epub 2024 May 23.
As a serious complication of moderately severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP), infected pancreatic necrosis (IPN) can lead to a prolonged course of interventional therapy. Most predictive models designed to identify such patients are complex or lack validation. The aim of this study was to develop a predictive model for the early detection of IPN in MSAP and SAP.
A total of 594 patients with MSAP or SAP were included in the study. To reduce dimensionality, least absolute shrinkage and selection operator regression analysis was used to screen potential predictive variables, a nomogram was then constructed using logistic regression analysis. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the discrimination, accuracy, and clinical efficacy of the model. External data were also obtained to further validate the constructed model.
There were 476, 118, and 82 patients in the training, internal validation, and external validation cohorts, respectively. Platelet count, hematocrit, albumin/globulin, severity of acute pancreatitis, and modified computed tomography severity index score were independent factors for predicting IPN in MSAP and SAP. The area under the ROC curves were 0.923, 0.940, and 0.817, respectively, in the three groups. There was a good consistency between the actual probabilities and the predicted probabilities. DCA revealed excellent clinical utility.
The constructed nomogram is a simple and feasible model that has good clinical predictive value and efficacy in clinical decision-making for IPN in MSAP and SAP.
感染性胰腺坏死（IPN）是中度重症急性胰腺炎（MSAP）和重症急性胰腺炎（SAP）的严重并发症，可导致介入治疗过程延长。大多数旨在识别此类患者的预测模型较为复杂或缺乏验证。本研究旨在建立一种预测 MSAP 和 SAP 中 IPN 的早期检测模型。
共纳入 594 例 MSAP 或 SAP 患者。为了降低维度，采用最小绝对收缩和选择算子回归分析筛选潜在的预测变量，然后使用逻辑回归分析构建列线图。使用接收者操作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估模型的区分度、准确性和临床疗效。还获取外部数据进一步验证构建的模型。
训练组、内部验证组和外部验证组分别有 476、118 和 82 例患者。血小板计数、血细胞比容、白蛋白/球蛋白、急性胰腺炎严重程度和改良 CT 严重指数评分是 MSAP 和 SAP 预测 IPN 的独立因素。三组 ROC 曲线下面积分别为 0.923、0.940 和 0.817。实际概率与预测概率之间具有良好的一致性。DCA 显示出优异的临床实用性。
所构建的列线图是一种简单可行的模型，在 MSAP 和 SAP 中 IPN 的临床决策中具有良好的临床预测价值和疗效。
