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Review
. 2025 Nov;57(11):3919-3920.
doi: 10.1007/s11255-025-04597-w. Epub 2025 Jun 5.

Reappraising machine learning models for vascular calcification in CKD: methodological concerns and clinical gaps

Muhammad Khubaib Iftikhar  1
Affiliations
Review

Reappraising machine learning models for vascular calcification in CKD: methodological concerns and clinical gaps

Muhammad Khubaib Iftikhar. Int Urol Nephrol. 2025 Nov.

Abstract

Lin et al. (Int Urol Nephrol, 2025) contribute to the literature on abdominal aortic calcification (AAC) in chronic kidney disease (CKD) using interpretable machine learning. However, several limitations hinder its clinical applicability. The cross-sectional design restricts causal inference, while the lack of external validation limits generalizability. Critical confounders such as pharmacologic interventions and lifestyle factors are omitted, risking bias in the model. In addition, treating CKD as a binary variable oversimplifies its complexity. Despite the promising use of SHAP analysis, the study lacks clinical translation for actionable risk stratification and personalized treatment. Future research should address these gaps to enhance the model's clinical utility.

Keywords: Abdominal aortic calcification; CKD progression; Cardiovascular risk; Chronic kidney disease; Machine learning; Predictive models; SHAP analysis; Vascular calcification.

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Conflict of interest statement

Declarations. Conflict of interests: The authors declare no competing interests. Ethical approval: Not applicable.

Comment on

References

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