Evidence map›Paper›PMID 40883745›Full record

ArticleBiomedical engineering online2025

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

Yanxin Wang, Lin Yang, Ziwei Li, Xinyu Zhang, Hongyang Zhao, Man Ji, Dongmei Hao, Jie Yang, Chong Wang, Ying Li and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical engineering online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yanxin WangCollege of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, China.
Lin YangCollege of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, China. yanglin@bjut.edu.cn.
Ziwei LiDepartment of Asset Management, The Third People's Hospital of Yunnan Province, Yunnan, China.
Xinyu ZhangCollege of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, China.
Hongyang ZhaoCollege of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, China.
Man JiCollege of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, China.
Dongmei HaoCollege of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, China.
Jie YangDepartment of Nephrology and Department of Medical Engineering, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 102208, China.
Chong WangDepartment of Nephrology and Department of Medical Engineering, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 102208, China.
Ying LiDepartment of Nephrology and Department of Medical Engineering, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 102208, China.
Guangfei LiCollege of Chemistry and Life Science, Beijing University of Technology, Beijing, 100124, China.

Funding

National Key R&D Program of China 2019YFC0119700National Natural Science Foundation of China 12402350
6 · The paper itself

Abstract

backgroundCoronary 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.

methods58 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.

resultsClear 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.

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

Indexed as

Coronary Artery DiseaseCoronary VesselsKidney Failure, ChronicMachine LearningPulse Wave AnalysisRenal DialysisVascular CalcificationAgedFemaleHumansMaleMiddle AgedTomography, X-Ray ComputedCoronary artery calcificationEnd-stage renal diseaseGradient boosting decision treeMachine learningPulse wave

Identifiers

PMID40883745
PMCPMC12395768

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.