Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Street Taiping No.25, Region Jiangyang, Luzhou, 646099, Sichuan, China.
Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China.
Dig Dis Sci. 2024 Jun;69(6):2235-2246. doi: 10.1007/s10620-024-08415-8. Epub 2024 Apr 11.
BACKGROUND: Acute pancreatitis is easily confused with abdominal pain symptoms, and it could lead to serious complications for pregnant women and fetus, the mortality was as high as 3.3% and 11.6-18.7%, respectively. However, there is still lack of sensitive laboratory markers for early diagnosis of APIP and authoritative guidelines to guide treatment. OBJECTIVE: The purpose of this study was to explore the risk factors of acute pancreatitis in pregnancy, establish, and evaluate the dynamic prediction model of risk factors in acute pancreatitis in pregnancy patients. STUDY DESIGN: Clinical data of APIP patients and non-pregnant acute pancreases patients who underwent regular antenatal check-ups during the same period were collected. The dataset after propensity matching was randomly divided into training set and verification set at a ratio of 7:3. The model was constructed using Logistic regression, least absolute shrinkage and selection operator regression, R language and other methods. The training set model was used to construct the diagnostic nomogram model and the validation set was used to validate the model. Finally, the accuracy and clinical practicability of the model were evaluated. RESULTS: A total of 111 APIP were included. In all APIP patients, hyperlipidemic pancreatitis was the most important reason. The levels of serum amylase, creatinine, albumin, triglyceride, high-density lipoprotein cholesterol, and apolipoprotein A1 were significantly different between the two groups. The propensity matching method was used to match pregnant pancreatitis patients and pregnant non-pancreatic patients 1:1 according to age and gestational age, and the matching tolerance was 0.02. The multivariate logistic regression analysis of training set showed that diabetes, triglyceride, Body Mass Index, white blood cell, and C-reactive protein were identified and entered the dynamic nomogram. The area under the ROC curve of the training set was 0.942 and in validation set was 0.842. The calibration curve showed good predictive in training set, and the calibration performance in the validation set was acceptable. The calibration curve showed the consistency between the nomogram model and the actual probability. CONCLUSION: The dynamic nomogram model we constructed to predict the risk factors of acute pancreatitis in pregnancy has high accuracy, discrimination, and clinical practicability.
背景：急性胰腺炎容易与腹痛症状混淆，可导致孕妇和胎儿出现严重并发症，死亡率分别高达 3.3%和 11.6-18.7%。但是，目前对于早发型妊娠急性胰腺炎（APIP）仍然缺乏敏感的实验室标志物，也缺乏权威的指南来指导治疗。
目的：本研究旨在探讨妊娠合并急性胰腺炎的危险因素，建立并评估妊娠合并急性胰腺炎患者危险因素的动态预测模型。
研究设计：收集同期常规产前检查的 APIP 患者和非妊娠急性胰腺炎患者的临床资料。经过倾向评分匹配后，数据集被随机分为训练集和验证集，比例为 7:3。使用 Logistic 回归、最小绝对收缩和选择算子回归、R 语言等方法构建模型。使用训练集模型构建诊断列线图模型，并使用验证集验证模型。最后，评估模型的准确性和临床实用性。
结果：共纳入 111 例 APIP。在所有 APIP 患者中，高脂血症性胰腺炎是最重要的原因。两组患者的血清淀粉酶、肌酐、白蛋白、三酰甘油、高密度脂蛋白胆固醇和载脂蛋白 A1 水平均有显著差异。采用倾向评分匹配法，根据年龄和孕周，1:1 匹配妊娠胰腺炎患者和妊娠非胰腺炎患者，匹配容忍度为 0.02。训练集的多因素 logistic 回归分析显示，糖尿病、三酰甘油、体重指数、白细胞和 C 反应蛋白被识别并纳入动态列线图。训练集的 ROC 曲线下面积为 0.942，验证集为 0.842。校准曲线显示训练集具有良好的预测能力，验证集的校准性能可接受。校准曲线显示列线图模型与实际概率之间具有一致性。
结论：我们构建的预测妊娠合并急性胰腺炎危险因素的动态列线图模型具有较高的准确性、区分度和临床实用性。
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