Bialonczyk Urszula, Debowska Malgorzata, Dai Lu, Qureshi Abdul Rashid, Bobrowski Leon, Soderberg Magnus, Lindholm Bengt, Stenvinkel Peter, Lukaszuk Tomasz, Poleszczuk Jan
Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland.
Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden.
Sci Rep. 2025 May 20;15(1):17453. doi: 10.1038/s41598-025-02457-2.
Machine learning algorithms that integrate multiple biomarkers are increasingly used in disease detection, yet economic considerations are often overlooked. Medial vascular calcification (mVC), a pathology associated with elevated cardiovascular risk in chronic kidney disease (CKD), requires cost-effective diagnostic approaches. This pilot study evaluated the cost-effectiveness of machine learning models for mVC detection using traditional risk markers and circulating biomarkers in 152 CKD patients undergoing living donor kidney transplantation. Patients were classified as having no/minimal (n = 93) or moderate/extensive (n = 59) mVC. Five classification frameworks with automatic variable selection identified predictors of mVC. Age and copeptin were selected by all algorithms, while diabetes, male sex, choline, and osteoprotegerin were chosen by four methods. The number of features selected ranged from 5 to 21. Although accuracy differences among classifiers were limited to 3%, models using more features nearly tripled the procedure's cost. By incorporating the incremental cost-effectiveness ratio, the study highlighted significant disparities in performance versus cost among classifiers. The present findings suggest that machine learning has the potential to complement imaging techniques for mVC detection and uncover novel biomarkers. However, modest performance improvements may not justify higher costs, underscoring the importance of considering cost-effectiveness when selecting classification models.
整合多种生物标志物的机器学习算法在疾病检测中的应用日益广泛，但经济因素却常常被忽视。血管中层钙化（mVC）是一种与慢性肾脏病（CKD）中心血管风险升高相关的病理状态，需要具有成本效益的诊断方法。这项前瞻性研究评估了在152例接受活体供肾移植的CKD患者中，使用传统风险标志物和循环生物标志物的机器学习模型检测mVC的成本效益。患者被分为无/轻度（n = 93）或中度/重度（n = 59）mVC。五个具有自动变量选择功能的分类框架确定了mVC的预测因素。所有算法均选择了年龄和 copeptin，而四种方法选择了糖尿病、男性、胆碱和骨保护素。所选特征数量从5到21不等。尽管分类器之间的准确率差异限制在3%以内，但使用更多特征的模型使该过程的成本几乎增加了两倍。通过纳入增量成本效益比，该研究突出了分类器在性能与成本方面的显著差异。目前的研究结果表明，机器学习有潜力补充用于mVC检测的成像技术并发现新的生物标志物。然而，适度的性能提升可能无法证明更高的成本是合理的，这凸显了在选择分类模型时考虑成本效益的重要性。
