Khandpur S, Mishra P, Mishra S, Tiwari S. Challenges in predictive modelling of chronic kidney disease: A narrative review. World J Nephrol 2024; 13(3): 97214 [DOI: 10.5527/wjn.v13.i3.97214]
Corresponding Author of This Article
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
Research Domain of This Article
Methodology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
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 byCoord/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
Abstract
The exponential rise in the burden of chronic kidney disease (CKD) worldwide has put enormous pressure on the economy. Predictive modeling of CKD can ease this burden by predicting the future disease occurrence ahead of its onset. There are various regression methods for predictive modeling based on the distribution of the outcome variable. However, the accuracy of the predictive model depends on how well the model is developed by taking into account the goodness of fit, choice of covariates, handling of covariates measured on a continuous scale, handling of categorical covariates, and number of outcome events per predictor parameter or sample size. Optimal performance of a predictive model on an independent cohort is desired. However, there are several challenges in the predictive modeling of CKD. Disease-specific methodological challenges hinder the development of a predictive model that is cost-effective and universally applicable to predict CKD onset. In this review, we discuss the advantages and challenges of various regression models available for predictive modeling and highlight those best for future CKD prediction.
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.
