Yang DJ, Lu HM, Liu Y, Li M, Hu WM, Zhou ZG. Development and validation of a prediction model for moderately severe and severe acute pancreatitis in pregnancy. World J Gastroenterol 2022; 28(15): 1588-1600 [PMID: 35582133 DOI: 10.3748/wjg.v28.i15.1588]
Corresponding Author of This Article
Zong-Guang Zhou, FACS, PhD, Chief Doctor, Department of Gastroenterological Surgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, China. zhou767@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Du-Jiang Yang, Yong Liu, Zong-Guang Zhou, Department of Gastroenterological Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Hui-Min Lu, Mao Li, Wei-Ming Hu, Department of Pancreatic Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Yang DJ and Zhou ZG conception and design; Lu HM, Liu Y, Li M, and WH collection data; Yang DJ, Lu HM, and Zhou ZG analysis data; Yang DJ write the manuscript; Hu WM and Zhou ZG revised the manuscript.
Supported bythe 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University No. ZYGD20006 and ZYJC18027.
Institutional review board statement: This study was reviewed and approved by the Institutional Ethics Committee of the West China Hospital.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: There are no conflicts of interest to report.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Zong-Guang Zhou, FACS, PhD, Chief Doctor, Department of Gastroenterological Surgery, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, China. zhou767@163.com
Received: November 20, 2021 Peer-review started: November 20, 2021 First decision: January 11, 2022 Revised: February 2, 2022 Accepted: March 6, 2022 Article in press: March 6, 2022 Published online: April 21, 2022 Processing time: 145 Days and 22.5 Hours
ARTICLE HIGHLIGHTS
Research background
The severity of acute pancreatitis in pregnancy is correlated with higher risks of maternal and fetal death.
Research motivation
There is a lack of a scoring model for predicting the moderately severe and severe acute pancreatitis in pregnancy (MSIP).
Research objectives
We aimed to develop a prediction model for moderately severe and severe acute pancreatitis in pregnancy.
Research methods
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 of prediction factors. Receiver operating characteristic curves and calibration curves were used to evaluate the model’s predictive performance.
Research results
A total of 190 patients were included in this study. Four predictors including lactate dehydrogenase, triglyceride, cholesterol, and albumin levels constitute the prediction model. 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 prediction model has a good consistency.
Research conclusions
An effective prediction model that can predict MSIP was constructed.
Research perspectives
Our model could help to predict moderately severe and severe acute pancreatitis in pregnancy. Usability of the model needs validation by other center data.
