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©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
Development and external validation of models to predict acute respiratory distress syndrome related to severe acute pancreatitis
Yun-Long Li, Ding-Ding Zhang, Yang-Yang Xiong, Rui-Feng Wang, Xiao-Mao Gao, Hui Gong, Shi-Cheng Zheng, Dong Wu
Yun-Long Li, Yang-Yang Xiong, Dong Wu, Department of Gastroenterology, Peking Union Medical College Hospital, Beijing 100730, China
Ding-Ding Zhang, Medical Research Center, Peking Union Medical College Hospital, Beijing 100730, China
Ding-Ding Zhang, Dong Wu, Clinical Epidemiology Unit, International Clinical Epidemiology Network, Beijing 100730, China
Yang-Yang Xiong, Department of Gastroenterology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang Province, China
Rui-Feng Wang, Department of Gastroenterology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin 150001, Heilongjiang Province, China
Xiao-Mao Gao, Department of Gastroenterology, The Sixth Hospital of Beijing, Beijing 100191, China
Hui Gong, Shi-Cheng Zheng, Department of Gastroenterology, West China Longquan Hospital Sichuan University, Chengdu 610100, Sichuan Province, China
Author contributions: Li YL, Zhang DD, and Xiong YY contributed equally to this work; Li YL, Zhang DD, Xiong YY and Wu D designed the research study; Li YL, Xiong YY, Wang RF, Gao XM, Gong H, Zheng SC, and Wu D performed the study and collected the data; Li YL, Zhang DD, and Xiong YY analyzed the data and wrote the manuscript; All authors have read and approved the final manuscript.
Supported by the Chinese Natural Science Foundation, No. 32170788.
Institutional review board statement: This study was approved by the Ethics Committee of Peking Union Medical College Hospital (Approval No. S-K1772).
Conflict-of-interest statement: All authors have no conflict of interest to disclose.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE statement, and the manuscript was prepared and revised according to the STROBE statement.
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: Dong Wu, MD, Professor, Department of Gastroenterology, Peking Union Medical College Hospital, No. 1 Shuaifuyuan Wangfujing, Dongcheng District, Beijing 100730, China.
wudong061002@aliyun.com
Received: December 4, 2021
Peer-review started: December 4, 2021
First decision: January 27, 2022
Revised: February 9, 2022
Accepted: April 3, 2022
Article in press: April 3, 2022
Published online: May 21, 2022
Processing time: 164 Days and 5.6 Hours
ARTICLE HIGHLIGHTS
Research background
Acute respiratory distress syndrome (ARDS) is a major cause of death in patients with severe acute pancreatitis (SAP), but simple and credible predictive models are absent.
Research motivation
Use of models to predict and identify early patients with SAP and SAP-related ARDS, so that clinicians can manage these patients early to decrease mortality during admission.
Research objectives
To develop and verify new models to predict SAP and SAP-related ARDS.
Research methods
Clinical data from four centers were retrospectively collected. Items selected with least absolute shrinkage and selection operator regression method were involved in multiple logistic regression to develop the final model in development cohort. New models were than verified in validation cohort and assessed with C-index, calibration curve and decision-curve analysis.
Research results
New models could predict SAP and SAP-related ARDS with four easily available items, and performed well.
Research conclusions
We developed and verified simple and reliable models to predict SAP and SAP-related ARDS.
Research perspectives
To verify new models in a larger size of sample, and to investigate the questions raised by reviewer.
