ArticleRenal failure2025
Development and external validation of a machine learning model for cardiac valve calcification early screening in dialysis patients: a multicenter study.
Article in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.Frontiers in medicine · 2025Pooled it
- Leveraging ICT Tools to Improve Kidney Health: A Comprehensive Review of Innovations in Nephrology.Healthcare (Basel, Switzerland) · 2026Review
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16 authors.
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Abstract
backgroundCardiac valve calcification (CVC) is common in dialysis patients and associated with increased cardiovascular risk. However, early screening has been limited by cost concerns. This study aimed to develop and validate a machine learning model to enhance early detection of CVC.
methodsData were collected at four centers between 2020 and 2023, including 852 dialysis patients in the development dataset and 661 in the external validation dataset. Predictive factors were selected using LASSO regression combined with univariate and multivariate analyses. Machine learning models including CatBoost, XGBoost, decision tree, support vector machine, random forest, and logistic regression were used to develop the CVC risk model. Model performance was evaluated in both validation sets. Risk thresholds were defined using the Youden index and validated in the external dataset.
resultsIn the development dataset, 32.9% of patients were diagnosed with CVC. Age, dialysis duration, alkaline phosphatase, apolipoprotein A1, and intact parathyroid hormone were selected to construct the CVC risk prediction model. CatBoost exhibited the best performance in the training dataset. The logistic regression model demonstrated the best predictive performance in both internal and external validation sets, with AUROCs of 0.806 (95% CI 0.750-0.863) and 0.757 (95% CI 0.720-0.793), respectively. Calibration curves and decision curves confirmed its predictive accuracy and clinical applicability. The logistic regression model was selected as the optimal model and achieved excellent risk stratification in CVC risk prediction.
conclusionThe predictive model effectively identifies CVC risk in dialysis patients and offers a robust tool for early detection and improved management.
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