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©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Prediction of mortality among patients with chronic kidney disease: A systematic review
Panupong Hansrivijit, Yi-Ju Chen, Kriti Lnu, Angkawipa Trongtorsak, Max M Puthenpura, Charat Thongprayoon, Tarun Bathini, Michael A Mao, Wisit Cheungpasitporn
Panupong Hansrivijit, Yi-Ju Chen, Kriti Lnu, Department of Internal Medicine, UPMC Pinnacle, Harrisburg, PA 17104, United States
Angkawipa Trongtorsak, Department of Internal Medicine, Amita Health Saint Francis Hospital, Evanston, IL 60202, United States
Max M Puthenpura, Department of Medicine, Drexel University College of Medicine, Philadelphia, PA 19129, United States
Charat Thongprayoon, Wisit Cheungpasitporn, Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, United States
Tarun Bathini, Department of Internal Medicine, University of Arizona, Tucson, AZ 85721, United States
Michael A Mao, Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL 32224, United States
Author contributions: Hansrivijit P contributed to acquisition of data, analysis, and interpretation of data, drafting the article, and final approval; Chen YJ, Lnu K and Trongtorsak A contributed to acquisition of data, analysis of data, and drafting the article; Puthenpura MM contributed to acquisition of data and drafting the article; Thongprayoon C and Bathini T contributed to acquisition of data and final approval; Mao MA and Cheungpasitporn W contributed to interpretation of data, revising the article, and final approval.
Conflict-of-interest statement: The authors have declared no potential conflicts of interest.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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: Wisit Cheungpasitporn, FACP, FASN, FAST, Associate Professor, Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, United States. wcheungpasitporn@gmail.com
Received: March 21, 2021
Peer-review started: March 21, 2021
First decision: May 6, 2021
Revised: May 11, 2021
Accepted: July 23, 2021
Article in press: July 23, 2021
Published online: July 25, 2021
ARTICLE HIGHLIGHTS
Research background
Chronic kidney disease (CKD) is a common medical condition that is increasing in prevalence. Understanding the accuracy of mortality risk factors in CKD patients could mitigate death.
Research motivation
Evidence has shown that several clinical factors are associated with mortality in CKD patients using regression analyses. However, the accuracy of these mortality predictive factors has not been clearly demonstrated.
Research objectives
To establish the accuracy of mortality predictive factors among CKD patients by utilizing the area under the receiver operating characteristic curve (AUC) analysis.
Research methods
Ovid MEDLINE, EMBASE, and the Cochrane Library were searched for eligible articles through January 2021. Only studies that reported their mortality predictive factors with AUC and 95% confidence interval were included. These factors were classified as acceptable, excellent, or outstanding based on their AUC.
Research results
Of 1759 citations, a total of 18 studies (n = 14579) were included in the systematic review. Eight hundred thirty two patients had non-dialysis CKD, and 13747 patients had dialysis-dependent CKD (2160 hemodialysis, 370 peritoneal dialysis, and 11217 undifferentiated modalities of dialysis). Of 24 predictive factors, none were considered outstanding for mortality prediction. A total of seven predictive factors (N-terminal pro-brain natriuretic peptide, brain natriuretic peptide, soluble urokinase plasminogen activator receptor, augmentation index, left atrial reservoir strain, C-reactive protein, and systolic pulmonary artery pressure) were identified as excellent. Seventeen predictive factors were in the acceptable range, which we classified into the following subgroups: predictors for the non-dialysis population, echocardiographic factors, comorbidities, and miscellaneous.
Research conclusions
This study determined several mortality risk factors for CKD patients that were deemed acceptable or excellent. Echocardiography is an important tool for mortality prognostication in CKD patients.
Research perspectives
The results of this study provide a preliminary perspective on the importance of identifying better prognostic factors for mortality in CKD patients. There is a lack of predictive risk factors with an AUC greater than 0.90. Currently identified mortality risk factors can be combined to create a risk calculator for CKD patients, which could be subsequently validated in future research.
