Challenges in predictive modelling of chronic kidney disease: A narrative review

doi:10.5527/wjn.v13.i3.97214

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World Journal of Nephrology

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2220-6124

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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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

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Figure 1 Selection of appropriate regression model.

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Figure 2 Selection of appropriate regression model for chronic kidney disease. ACR: albumin-to-creatinine ratio; KDIGO: Kidney Disease Improving Global Outcomes; eGFR: Estimated glomerular filtration rate.

Citation: 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
URL: https://www.wjgnet.com/2220-6124/full/v13/i3/97214.htm
DOI: https://dx.doi.org/10.5527/wjn.v13.i3.97214