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Accuracy of pulse wave velocity for screening coronary artery disease: a systematic review and meta-analysis

  • Carla-Geovanna Lever-Megina , Iván Cavero-Redondo EMAIL logo , Celia Álvarez-Bueno , Cristina Morales-Berenkova , Germán Cabeza-Arrebola and Alicia Saz-Lara
Published/Copyright: January 16, 2025

Abstract

Coronary artery disease (CAD) is the leading cause of cardiovascular events and showed high prevalence and healthcare costs in 2019. However, CAD screening for cardiovascular event prevention is invasive and expensive. This study aims to estimate the ability of a noninvasive method, pulse wave velocity (PWV), to detect the presence or absence of coronary artery disease in patients with suspected CAD. A systematic review and meta-analysis of the available evidence was conducted, comparing PWV with the gold standard diagnostic method, angiography. The literature search was systematically performed in the PubMed, Scopus and Web of Science databases from inception to August 2024. Study quality assessment was performed using the Diagnostic Accuracy Study Quality Assessment Tool (QUADAS-2). Publication bias was assessed using the method proposed by Deeks. Statistical analyses were performed with the STATA SE software, version 15. The eight included studies had a cross-sectional design, in which the presence of CAD was measured simultaneously by PWV and angiography. To assess the accuracy of the tests, the overall sensitivity and specificity were combined into a single value, the diagnostic odds ratio (dOR), which provided a value of 3.61, indicating a high probability of detecting CAD by PWV. The implementation of PWV as a screening technique in healthcare centers could bring great benefits to patients with suspected CAD and increase efficiency in the use of healthcare resources.


Corresponding author: Iván Cavero-Redondo, PhD, CarVasCare Research Group, Facultad de Enfermería de Cuenca, Universidad de Castilla-La Mancha, Cuenca, Spain. E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: C.G. Lever-Megina and I. Cavero-Redondo participated by carrying out the conception and design of the present study and performed the literature search; A. Saz-Lara and C. Álvarez-Bueno contributed further by reviewing the correct performance of the statistical analyses; C. Morales-Berenkova and G. Cabeza-Arrebola contributed by acquiring data and reviewing editorial errors. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: The original contributions presented in the study are included in the manuscript or in the supplemental material. Additional queries may be addressed to the corresponding author.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/dx-2024-0193).


Received: 2024-12-04
Accepted: 2024-12-26
Published Online: 2025-01-16

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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