| ISSN |
2220-3141(online) |
| 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/ |
| Copyright |
© The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Critical Care Medicine |
| Manuscript Type |
Retrospective Cohort Study |
| Article Title |
Predicting acute kidney injury in septic shock patients using inflammatory indices in the intensive care unit
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Jackson Rajendran, Song-Peng Ang, Maria Jose Lorenzo-Capps, Carlos Valladares, Eunseuk Lee, Veera Jayasree Latha Bommu, George Altarcha, Svitlana Pominov, Bryan Gregory, Jia Ee Chia and Jose Iglesias |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Jose Iglesias, FASN, Department of Internal Medicine, Hackensack Meridian School of Medicine, 123 Metro Blvd, Nutley, NJ 07110, United States. jiglesias23@gmail.com |
| Key Words |
Acute kidney injury; Septic shock; Inflammatory indices; eICU Collaborative Research Database; Principal component analysis; Machine learning; Neutrophil-to-lymphocyte ratio; Systemic immune-inflammation index; Aggregate index of systemic inflammation; Monocyte-to-lymphocyte ratio |
| Core Tip |
Composite inflammatory markers are elevated in patients who develop acute kidney injury. However, due to heterogeneity of septic shock, multicollinearity and nonlinear relationships, these markers alone offer limited incremental predictive value. Neural network models further expounded the contribution of both clinical factors and the combined inflammatory/metabolic dimension to accurate acute kidney injury prediction, capturing complex interactions and non-linear relationships not evident in traditional regression models. Implementation of supervised and unsupervised machine learning together may offer further insights. |
| Publish Date |
2026-02-28 07:48 |
| Citation |
Rajendran J, Ang SP, Lorenzo-Capps MJ, Valladares C, Lee E, Bommu VJL, Altarcha G, Pominov S, Gregory B, Chia JE, Iglesias J. Predicting acute kidney injury in septic shock patients using inflammatory indices in the intensive care unit. World J Crit Care Med 2026; 15(1): 114318 |
| URL |
https://www.wjgnet.com/2220-3141/full/v15/i1/114318.htm |
| DOI |
https://dx.doi.org/10.5492/wjccm.v15.i1.114318 |
