Pulse wave-driven machine learning for the non-invasive assessment of coronary artery calcification in patients with end-stage renal disease undergoing hemodialysis.

Abstract

Background: Coronary artery calcification (CAC) represents a major cardiovascular risk in patients with end-stage renal disease (ESRD) undergoing hemodialysis. Given that radial artery pulse waveforms can reflect vascular status, this study aimed to evaluate their utility in the non-invasive assessment of CAC severity.

Methods: 58 patients with ESRD undergoing hemodialysis were enrolled. CAC severity was assessed using low-dose computed tomography (LDCT) and classified into four groups based on Agatston scores: no calcification (0), mild (1-100), moderate (101-400), and severe (> 400). Radial artery pulse waveforms were recorded before, hourly during, and after hemodialysis. Key features were extracted based on morphological differences among groups. Statistical inter-group comparisons and intra-group trend analyses were performed. A gradient boosting decision tree (GBDT) model was trained to classify CAC severity using waveform features.

Results: Clear morphological differences were observed among the four CAC groups. The non-calcified group showed a distinct main wave followed by identifiable tidal waves. With increasing CAC severity, the tidal waves became progressively attenuated and less distinguishable, resulting in a smoother overall waveform, particularly in the severe calcification group. Pulse waveform features exhibited significant variation across CAC groups and over the hemodialysis process, including parameters related to waveform morphology, descending limb, complexity and distribution, mean value, and full-process stereoscopic pulse wave features. The GBDT model demonstrated robust and consistent performance, with an average accuracy of 84.1% and a macro-AUC of 0.962 in fivefold cross-validation, and comparable results (83.9% accuracy, 0.961 macro-AUC) on the independent test set. Notably, the model performed particularly well in identifying Severe Calcification cases.

Conclusions: Radial artery pulse wave analysis may serve as a non-invasive adjunct for assessing CAC severity in patients with ESRD undergoing hemodialysis.