Department of Emergency, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
Graduate College of Wannan Medical College, Wuhu, Anhui, China.
Medicine (Baltimore). 2023 Nov 24;102(47):e36102. doi: 10.1097/MD.0000000000036102.
Pancreatic pseudocyst (PPC) increases the risk of a poor prognosis in in patients with acute pancreatitis (AP). Currently, an efficient tool is not available for predicting the risk of PPC in patients with AP. Therefore, this research aimed to explore the risk factors associated with PPC secondary to AP and to develop a model based on clinical information for predicting PPC secondary to AP. This study included 400 patients with acute pancreatitis and pancreatic pseudocyst secondary to acute pancreatitis admitted to the emergency department and gastroenterology department of The First Affiliated Hospital of the University of Science and Technology of China from January 2019 to June 2022. Participants were divided into no PPCs (321 cases) and PPCs (79 cases). Independent factors of PPC secondary to AP were analyzed using univariate and multivariate logistic regression. The nomogram model was constructed based on multivariate logistic regression analyses, which included all risk factors, and evaluated using R. We enrolled 400 eligible patients and allocated 280 and 120 to the training and test sets, respectively. Clinical features, including severe pancreatitis history [odds ratio (OR) = 4.757; 95% confidence interval (CI): 1.758-12.871], diabetes mellitus (OR = 6.919; 95% CI: 2.084-22.967), history of biliary surgery (OR = 9.232; 95% CI: 3.022-28.203), hemoglobin (OR = 0.974; 95% CI: 0.955-0.994), albumin (OR = 0.888; 95% CI: 0.825-0.957), and body mass index (OR = 0.851; 95% CI: 0.753-0.962), were significantly associated with the incidence of PPC after AP in the training sets. Additionally, the individualized nomogram demonstrated good discrimination in the training and validation samples with good calibration, The area under the curve and 95% CI of the nomogram were 0.883 (0.839-0.927) in the training dataset and 0.839 (0.752-0.925) in the validation set. We developed a nomogram model of PPC secondary to AP using R Studio. This model has a good predictive value for PPC in patients with AP and can help improve clinical decision-making.
胰腺假性囊肿（PPC）会增加急性胰腺炎（AP）患者预后不良的风险。目前，尚无有效的工具可用于预测 AP 患者 PPC 的风险。因此，本研究旨在探讨与 AP 继发 PPC 相关的危险因素，并基于临床信息建立预测 AP 继发 PPC 的模型。
这项研究纳入了 2019 年 1 月至 2022 年 6 月期间在中国科学技术大学第一附属医院急诊部和消化内科收治的 400 例急性胰腺炎和急性胰腺炎继发胰腺假性囊肿患者。将患者分为无 PPC 组（321 例）和 PPC 组（79 例）。采用单因素和多因素 logistic 回归分析 AP 继发 PPC 的独立因素。基于多因素 logistic 回归分析，构建了包含所有危险因素的列线图模型，并在 R 中进行了评估。
我们纳入了 400 名符合条件的患者，将 280 名和 120 名患者分别分配到训练集和测试集中。临床特征，包括重症胰腺炎病史[比值比（OR）=4.757；95%置信区间（CI）：1.758-12.871]、糖尿病（OR=6.919；95%CI：2.084-22.967）、胆道手术史（OR=9.232；95%CI：3.022-28.203）、血红蛋白（OR=0.974；95%CI：0.955-0.994）、白蛋白（OR=0.888；95%CI：0.825-0.957）和体重指数（OR=0.851；95%CI：0.753-0.962）与 AP 后 PPC 的发生率显著相关。此外，个体化列线图在训练和验证样本中具有良好的区分度和校准度，在训练数据集中曲线下面积和 95%CI 为 0.883（0.839-0.927），在验证集中为 0.839（0.752-0.925）。
我们使用 R Studio 开发了一个用于预测 AP 后 PPC 的列线图模型。该模型对 AP 患者 PPC 具有良好的预测价值，有助于提高临床决策水平。
