Department of Gastroenterological Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China.
Department of Pancreatic Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China.
World J Gastroenterol. 2022 Apr 21;28(15):1588-1600. doi: 10.3748/wjg.v28.i15.1588.
The severity of acute pancreatitis in pregnancy (APIP) is correlated with higher risks of maternal and fetal death.
To develop a nomogram that could predict moderately severe and severe acute pancreatitis in pregnancy (MSIP).
Patients with APIP admitted to West China Hospital between January 2012 and December 2018 were included in this study. They were divided into mild acute pancreatitis in pregnancy (MAIP) and MSIP. Characteristic parameters and laboratory results were collected. The training set and test set were randomly divided at a ratio of 7:3. Least absolute shrinkage and selection operator regression was used to select potential prognostic factors. A nomogram was developed by logistic regression. A random forest model was used to validate the stability of the prediction factors. Receiver operating characteristic curves and calibration curves were used to evaluate the model's predictive performance.
A total of 190 patients were included in this study. A total of 134 patients (70.5%) and 56 patients (29.5%) were classified as having MAIP and MSIP, respectively. Four independent predictors (lactate dehydrogenase, triglyceride, cholesterol, and albumin levels) were identified for MSIP. A nomogram prediction model based on these factors was established. The model had areas under the curve of 0.865 and 0.853 in the training and validation sets, respectively. The calibration curves showed that the nomogram has a good consistency.
A nomogram including lactate dehydrogenase, triglyceride, cholesterol, and albumin levels as independent predictors was built with good performance for MSIP prediction.
妊娠合并急性胰腺炎（APIP）的严重程度与母婴死亡风险增加相关。
建立预测妊娠中重度急性胰腺炎（MSIP）的列线图。
纳入 2012 年 1 月至 2018 年 12 月在华西医院住院的 APIP 患者，分为妊娠轻症急性胰腺炎（MAIP）和 MSIP。收集特征参数和实验室结果。采用 7:3 的比例随机分为训练集和验证集。采用最小绝对收缩和选择算子回归选择潜在的预后因素。采用 logistic 回归建立列线图。随机森林模型验证预测因素的稳定性。采用受试者工作特征曲线和校准曲线评估模型的预测性能。
本研究共纳入 190 例患者。134 例（70.5%）和 56 例（29.5%）患者分别归类为 MAIP 和 MSIP。MSIP 的 4 个独立预测因子（乳酸脱氢酶、甘油三酯、胆固醇和白蛋白水平）被确定。建立了基于这些因素的列线图预测模型。该模型在训练集和验证集中的曲线下面积分别为 0.865 和 0.853。校准曲线表明该列线图具有良好的一致性。
建立了一个包含乳酸脱氢酶、甘油三酯、胆固醇和白蛋白水平作为独立预测因子的列线图，用于预测 MSIP 具有良好的性能。
