Computers in biology and medicine · 2025
Leveraging pulse wave signal properties for coronary artery calcification screening in CKD patients.
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Bialonczyk Urszula, Pstras Leszek, Debowska Malgorzata, Dai Lu, Qureshi Abdul Rashid, Soderberg Magnus, Brismar Torkel B, Ripsweden Jonaz, Lindholm Bengt, Stenvinkel Peter, Poleszczuk Jan
Аннотация
BACKGROUND AND AIMS: Chronic kidney disease (CKD) patients are particularly susceptible to coronary atherosclerosis, which can be assessed using computed tomography (CT)-based coronary artery calcium (CAC) score. However, such a costly examination might not always be required and cost-effective. This study investigates a novel screening approach utilizing pulse wave analysis combined with machine learning models to identify CKD patients at high risk for coronary atherosclerosis.
METHODS: We analyzed retrospective data from 124 CKD stage 5 patients who underwent kidney transplantation. Pulse wave signals were collected using SphygmoCor system (AtCor Medical, Sydney, Australia), and CAC scores were determined via CT scans. Machine learning models were developed using either pulse wave features or traditional risk factors (TRF) to detect high CAC scores (≥100 Agatston units).
RESULTS: The pulse wave-based model outperformed TRF-based model in identifying high CAC scores, particularly among younger patients. Specifically, the pulse wave-based classifier showed superior balanced accuracy in all analyzed age groups and superior sensitivity in patients under 60 years old, especially in those under 50 years old. The overall balanced accuracy of the pulse wave-based model exceeded 80 %, suggesting its potential as a reliable screening tool for detecting high risk of coronary atherosclerosis in CKD patients.
CONCLUSIONS: Pulse wave analysis combined with machine learning offers a promising, non-invasive method for preliminary CAC screening in CKD patients. This approach could enhance early risk identification and improve clinical management, although further research is needed to validate and refine this method in larger, more diverse populations.
METHODS: We analyzed retrospective data from 124 CKD stage 5 patients who underwent kidney transplantation. Pulse wave signals were collected using SphygmoCor system (AtCor Medical, Sydney, Australia), and CAC scores were determined via CT scans. Machine learning models were developed using either pulse wave features or traditional risk factors (TRF) to detect high CAC scores (≥100 Agatston units).
RESULTS: The pulse wave-based model outperformed TRF-based model in identifying high CAC scores, particularly among younger patients. Specifically, the pulse wave-based classifier showed superior balanced accuracy in all analyzed age groups and superior sensitivity in patients under 60 years old, especially in those under 50 years old. The overall balanced accuracy of the pulse wave-based model exceeded 80 %, suggesting its potential as a reliable screening tool for detecting high risk of coronary atherosclerosis in CKD patients.
CONCLUSIONS: Pulse wave analysis combined with machine learning offers a promising, non-invasive method for preliminary CAC screening in CKD patients. This approach could enhance early risk identification and improve clinical management, although further research is needed to validate and refine this method in larger, more diverse populations.