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©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Jun 27, 2020; 12(6): 298-311
Published online Jun 27, 2020. doi: 10.4254/wjh.v12.i6.298
Published online Jun 27, 2020. doi: 10.4254/wjh.v12.i6.298
LIV-4: A novel model for predicting transplant-free survival in critically ill cirrhotics
Christina C Lindenmeyer, Gianina Flocco, Neal A Mehta, William D Carey, Carlos Romero-Marrero, Department of Gastroenterology, Hepatology and Nutrition, Cleveland Clinic, Cleveland, OH 44195, United States
Vedha Sanghi, Ahyoung J Kim, Fadi Niyazi, Department of Internal Medicine, Cleveland Clinic, Cleveland, OH 44195, United States
Rocio Lopez, Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH 44106, United States
Aanchal Kapoor, Eduardo Mireles-Cabodevila, Department of Critical Care Medicine, Cleveland Clinic, Cleveland, OH 44195, United States
Author contributions: Lindenmeyer CC designed the study and drafted the article; Lindenmeyer CC, Flocco G, Sanghi V, Kim AJ, Niyazi F and Mehta NA acquired data; Lindenmeyer CC, Lopez R, Kapoor A, Carey WD, Mireles-Cabodevila E and Romero-Marrero C analyzed and interpreted data; all of the authors made critical revision for important intellectual content and final approval.
Institutional review board statement: The study was reviewed and approved by the Cleveland Clinic Foundation Institutional Review Board.
Informed consent statement: The Cleveland Clinic Foundation Institutional Review Board waives the written consent for all retrospective medical record reviews done for research purposes (as the one done for this study) if the investigators can ensure there are adequate protections to maintain the data in a secure manner with access limited to the study team and if sharing or releasing identifiable data to any outside person or entity will not occur. For this reason, no Informed Consent Form was used for this study.
Conflict-of-interest statement: There are no conflicts of interest associated with any of the senior authors or other coauthors who contributed their efforts to this manuscript. All the authors have no conflict of interest related to the manuscript.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
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: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Christina C Lindenmeyer, MD, Staff Physician, Assistant Professor of Medicine, Department of Gastroenterology, Hepatology and Nutrition, Cleveland Clinic, 9500 Euclid Avenue, Mail Code A51, Cleveland, OH 44195, United States. lindenc@ccf.org
Received: January 26, 2020
Peer-review started: January 26, 2020
First decision: March 15, 2020
Revised: May 15, 2020
Accepted: May 19, 2020
Article in press: May 19, 2020
Published online: June 27, 2020
Peer-review started: January 26, 2020
First decision: March 15, 2020
Revised: May 15, 2020
Accepted: May 19, 2020
Article in press: May 19, 2020
Published online: June 27, 2020
Core Tip
Core tip: Critically ill patients with cirrhosis have higher mortality rates in the intensive care unit (ICU) than patients without chronic liver disease. None of the currently available prognostic models have been widely accepted for use in cirrhotic patients in the ICU, perhaps due to complexity of calculation. We believe survival modeling can guide therapeutic decision-making and contribute to cost-effective healthcare resource utilization. We describe the development of a novel model to predict in-hospital mortality in critically ill patients with cirrhosis. Our validated model for predicting mortality on admission to the ICU performs better than previously published liver and ICU-specific scores.