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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年6月5日

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  • Muhammad Khubaib Iftikhar

摘要

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.