Publication Name	World Journal of Nephrology
Manuscript ID	97214
Country	India

Received	2024-05-26 10:06
Peer-Review Started	2024-05-26 10:06
To Make the First Decision
Return for Revision	2024-08-14 05:21
Revised	2024-08-27 08:42
Second Decision	2024-08-29 02:38
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-08-29 09:50
Articles in Press	2024-08-29 09:50
Publication Fee Transferred
Edit the Manuscript by Language Editor	2024-09-11 17:13
Typeset the Manuscript	2024-09-12 09:23
Publish the Manuscript Online	2024-09-19 08:21

ISSN	2220-6124 (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: http://creativecommons.org/licenses/by-nc/4.0/
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Category	Methodology
Manuscript Type	Minireviews
Article Title	Challenges in predictive modelling of chronic kidney disease: A narrative review
Manuscript Source	Invited Manuscript
All Author List	Sukhanshi Khandpur, Prabhaker Mishra, Shambhavi Mishra and Swasti Tiwari
ORCID	Author(s) ORCID Number Sukhanshi Khandpur http://orcid.org/0000-0002-5364-5975 Swasti Tiwari http://orcid.org/0000-0002-1701-2636
Funding Agency and Grant Number
Corresponding Author	Swasti Tiwari, FRCP, PhD, Professor, Department of Molecular Medicine & Biotechnology, Sanjay Gandhi Post Graduate Institute of Medical Science, PMSSY Building, 4th Floor, Raebareli Road, Lucknow 226014, India. tiwaris@sgpgi.ac.in
Key Words	Chronic kidney disease; Predictive modelling; Regression; Statistical modelling; Methodology
Core Tip	The burden of chronic kidney disease (CKD) is growing rapidly and there is an urgent need to prevent the growth of the disease burden by identifying the individuals at high risk for the development of CKD. A broad spectrum of statistical models exist that can predict the future onset of the disease. This narrative review discusses the practical applicability of various statistical models for CKD prediction.
Publish Date	2024-09-19 08:21
Citation	<p>Khandpur S, Mishra P, Mishra S, Tiwari S. Challenges in predictive modelling of chronic kidney disease: A narrative review. <i>World J Nephrol</i> 2024; 13(3): 97214</p>
URL	https://www.wjgnet.com/2220-6124/full/v13/i3/97214.htm
DOI	https://dx.doi.org/10.5527/wjn.v13.i3.97214

Full Article (PDF)	WJN-13-97214-with-cover.pdf
Manuscript File	97214_Auto_Edited_040849.docx
Answering Reviewers	97214-answering-reviewers.pdf
Audio Core Tip	97214-audio.mp3
Conflict-of-Interest Disclosure Form	97214-conflict-of-interest-statement.pdf
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Non-Native Speakers of English Editing Certificate	97214-non-native-speakers.pdf
Peer-review Report	97214-peer-reviews.pdf
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Scientific Editor Work List	97214-scientific-editor-work-list.pdf
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