Tang XW, Ren WS, Huang S, Zou K, Xu H, Shi XM, Zhang W, Shi L, Lü MH. Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis. World J Hepatol 2024; 16(4): 625-639 [PMID: 38689750 DOI: 10.4254/wjh.v16.i4.625]
Corresponding Author of This Article
Mu-Han Lü, MD, PhD, Chief Physician, Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, No. 25 Taiping Road, Jiangyang District, Luzhou 646099, Sichuan Province, China. lvmuhan@swmu.edu.cn
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/
World J Hepatol. Apr 27, 2024; 16(4): 625-639 Published online Apr 27, 2024. doi: 10.4254/wjh.v16.i4.625
Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis
Xiao-Wei Tang, Wen-Sen Ren, Shu Huang, Kang Zou, Huan Xu, Xiao-Min Shi, Wei Zhang, Lei Shi, Mu-Han Lü
Xiao-Wei Tang, Wen-Sen Ren, Kang Zou, Huan Xu, Xiao-Min Shi, Wei Zhang, Lei Shi, Mu-Han Lü, Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
Xiao-Wei Tang, Wen-Sen Ren, Kang Zou, Huan Xu, Xiao-Min Shi, Wei Zhang, Lei Shi, Mu-Han Lü, Nuclear Medicine and Molecular Imaging Key Laboratory, The Affiliated Hospital of Southwest Medical University, Luzhou 646099, Sichuan Province, China
Shu Huang, Department of Gastroenterology, Lianshui People’ Hospital of Kangda College Affiliated to Nanjing Medical University, Huaian 223499, Jiangsu Province, China
Co-first authors: Xiao-Wei Tang and Wen-Sen Ren.
Author contributions: Tang XW and Ren WS contributed equally to this work; Ren WS, Lü MH, Tang XW, and Huang S designed the research study; Ren WS, Zou K, Xu H and Shi XM collected the data; Ren WS, Zhang W and Shi L analyzed the data and constructed the nomogram model; Ren WS, Lü MH and Tang XW wrote the manuscript. All authors have read and approve the final manuscript.
Supported byNatural Science Foundation of Sichuan Province, No. 2022NSFSC1378.
Institutional review board statement: This study was reviewed and approved by the Institutional Review Committee of the Affiliated Hospital of Southwest Medical University (approval No. KY2023387).
Informed consent statement: This is an informed consent exemption statement. All data were downloaded from the Medical Information Mart for Intensive Care IV and the eICU collaborative research database. The two databases are publicly available. Before extracting data from the database, we completed the Collaborative Institutional Training Initiative Program course and were authorized to use the database.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
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: Mu-Han Lü, MD, PhD, Chief Physician, Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, No. 25 Taiping Road, Jiangyang District, Luzhou 646099, Sichuan Province, China. lvmuhan@swmu.edu.cn
Received: October 17, 2023 Peer-review started: October 17, 2023 First decision: January 2, 2024 Revised: February 23, 2024 Accepted: March 18, 2024 Article in press: March 18, 2024 Published online: April 27, 2024 Processing time: 189 Days and 16.6 Hours
Abstract
BACKGROUND
Liver cirrhosis patients admitted to intensive care unit (ICU) have a high mortality rate.
AIM
To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.
METHODS
We extracted demographic, etiological, vital sign, laboratory test, comorbidity, complication, treatment, and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and electronic ICU (eICU) collaborative research database (eICU-CRD). Predictor selection and model building were based on the MIMIC-IV dataset. The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors. The final predictors were included in the multivariate logistic regression model, which was used to construct a nomogram. Finally, we conducted external validation using the eICU-CRD. The area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were used to assess the efficacy of the models.
RESULTS
Risk factors, including the mean respiratory rate, mean systolic blood pressure, mean heart rate, white blood cells, international normalized ratio, total bilirubin, age, invasive ventilation, vasopressor use, maximum stage of acute kidney injury, and sequential organ failure assessment score, were included in the multivariate logistic regression. The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases, respectively. The calibration curve also confirmed the predictive ability of the model, while the decision curve confirmed its clinical value.
CONCLUSION
The nomogram has high accuracy in predicting in-hospital mortality. Improving the included predictors may help improve the prognosis of patients.
Core Tip: Liver cirrhosis patients admitted to the intensive care unit have a high mortality rate. In this study, we collected clinical data from patients with liver cirrhosis and constructed a nomogram predictive model that gained high accuracy in predicting in-hospital mortality. The accuracy was also confirmed by external validation, which suggests that the model can help us identify high-risk patients.
