Retrospective Cohort Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. May 21, 2022; 28(19): 2123-2136
Published online May 21, 2022. doi: 10.3748/wjg.v28.i19.2123
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
Abstract
BACKGROUND

Acute respiratory distress syndrome (ARDS) is a major cause of death in patients with severe acute pancreatitis (SAP). Although a series of prediction models have been developed for early identification of such patients, the majority are complicated or lack validation. A simpler and more credible model is required for clinical practice.

AIM

To develop and validate a predictive model for SAP related ARDS.

METHODS

Patients diagnosed with AP from four hospitals located at different regions of China were retrospectively grouped into derivation and validation cohorts. Statistically significant variables were identified using the least absolute shrinkage and selection operator regression method. Predictive models with nomograms were further built using multiple logistic regression analysis with these picked predictors. The discriminatory power of new models was compared with some common models. The performance of calibration ability and clinical utility of the predictive models were evaluated.

RESULTS

Out of 597 patients with AP, 139 were diagnosed with SAP (80 in derivation cohort and 59 in validation cohort) and 99 with ARDS (62 in derivation cohort and 37 in validation cohort). Four identical variables were identified as independent risk factors for both SAP and ARDS: heart rate [odds ratio (OR) = 1.05; 95%CI: 1.04-1.07; P < 0.001; OR = 1.05, 95%CI: 1.03-1.07, P < 0.001], respiratory rate (OR = 1.08, 95%CI: 1.0-1.17, P = 0.047; OR = 1.10, 95%CI: 1.02-1.19, P = 0.014), serum calcium concentration (OR = 0.26, 95%CI: 0.09-0.73, P = 0.011; OR = 0.17, 95%CI: 0.06-0.48, P = 0.001) and blood urea nitrogen (OR = 1.15, 95%CI: 1.09-1.23, P < 0.001; OR = 1.12, 95%CI: 1.05-1.19, P < 0.001). The area under receiver operating characteristic curve was 0.879 (95%CI: 0.830-0.928) and 0.898 (95%CI: 0.848-0.949) for SAP prediction in derivation and validation cohorts, respectively. This value was 0.892 (95%CI: 0.843-0.941) and 0.833 (95%CI: 0.754-0.912) for ARDS prediction, respectively. The discriminatory power of our models was improved compared with that of other widely used models and the calibration ability and clinical utility of the prediction models performed adequately.

CONCLUSION

The present study constructed and validated a simple and accurate predictive model for SAP-related ARDS in patients with AP.

Keywords: Acute pancreatitis, Acute respiratory distress syndrome, Nomogram, Calibration, Early identification, Predictive model

Core Tip: Severe acute pancreatitis (SAP)-related acute respiratory distress syndrome (ARDS) affect the mortality of patients with AP. Early identification of patients at high risk for SAP and ARDS can aid clinicians to adopt interventions to stop disease progression. However, current predictive models are either too complicated due to various parameters or unreliable due to lack of validation. This study developed new models to predict SAP and ARDS using only four routine clinical items within 24 h of admission. New models were externally validated and performed as well as or with a higher efficiency than other models.