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World J Nephrol. Sep 25, 2024; 13(3): 97214
Published online Sep 25, 2024. doi: 10.5527/wjn.v13.i3.97214
Challenges in predictive modelling of chronic kidney disease: A narrative review
Sukhanshi Khandpur, Prabhaker Mishra, Shambhavi Mishra, Swasti Tiwari
Sukhanshi Khandpur, Swasti Tiwari, Department of Molecular Medicine & Biotechnology, Sanjay Gandhi Post Graduate Institute of Medical Science, Lucknow 226014, Uttar Pradesh, India
Prabhaker Mishra, Department of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
Shambhavi Mishra, Department of Statistics, University of Lucknow, Lucknow 226007, Uttar Pradesh, India
Author contributions: Tiwari S and Khandpur S conceptualized the review; Khandpur S designed the manuscript and wrote the initial draft; Tiwari S revised the manuscript; Mishra P and Mishra S supervised the development of the flowchart and table and gave inputs to improve the manuscript; All authors approved the manuscript.
Supported by Coord/7 (1)/CAREKD/2018/NCD-II, No. 5/4/7-12/13/NCD-II; and Senior Research Fellowship by the Indian Council of Medical Research, New Delhi, No. 3/1/2(6)/Nephro/2022-NCD-II.
Conflict-of-interest statement: All authors have no conflicts of interest to disclose.
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: 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, Uttar Pradesh, India. tiwaris@sgpgi.ac.in
Received: May 26, 2024
Revised: August 27, 2024
Accepted: August 29, 2024
Published online: September 25, 2024
Processing time: 115 Days and 22.4 Hours
Core Tip

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.