Aortic Pulse Wave Velocity Improves Cardiovascular Event Prediction
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Objectives The goal of this study was to determine whether aortic pulse wave velocity (aPWV) improves prediction of cardiovascular disease (CVD) events beyond conventional risk factors.Background Several studies have shown that aPWV may be a useful risk factor for predicting CVD, but they have been underpowered to examine whether this is true for different subgroups.Methods We undertook a systematic review and obtained individual participant data from 16 studies. Study-specific associations of aPWV with CVD outcomes were determined using Cox proportional hazard models and random effect models to estimate pooled effects.Results Of 17,635 participants, a total of 1,785 (10%) had a CVD event. The pooled age- and sex-adjusted hazard ratios (HRs) per 1-SD change in loge aPWV were 1.35 (95% confidence interval [CI]: 1.22 to 1.50; p < 0.001) for coronary heart disease, 1.54 (95% CI: 1.34 to 1.78; p < 0.001) for stroke, and 1.45 (95% CI: 1.30 to 1.61; p < 0.001) for CVD. Associations stratified according to sex, diabetes, and hypertension were similar but decreased with age (1.89, 1.77, 1.36, and 1.23 for age ≤50, 51 to 60, 61 to 70, and >70 years, respectively; pinteraction <0.001). After adjusting for conventional risk factors, aPWV remained a predictor of coronary heart disease (HR: 1.23 [95% CI: 1.11 to 1.35]; p < 0.001), stroke (HR: 1.28 [95% CI: 1.16 to 1.42]; p < 0.001), and CVD events (HR: 1.30 [95% CI: 1.18 to 1.43]; p < 0.001). Reclassification indices showed that the addition of aPWV improved risk prediction (13% for 10-year CVD risk for intermediate risk) for some subgroups.Conclusions Consideration of aPWV improves model fit and reclassifies risk for future CVD events in models that include standard risk factors. aPWV may enable better identification of high-risk populations that might benefit from more aggressive CVD risk factor management.
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High relative wall thickness indicates concentric cardiac remodeling, and elevated pulse wave velocity reflects organ damage and predicts future cardiovascular events and all-cause mortality better than conventional risk factors.
“As high RWT indicates concentric cardiac remodeling and elevated PWV is reflective of organ damage and predicts future cardiovascular events and all-cause mortality better than conventional cardiovascular disease risk factors45–47”
From An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging (2021) · cites this paper as [46]
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Self-citation
- Authors
- Ben-Shlomo, Y., Spears, M., Boustred, C., May, M., Anderson, S. G., Benjamin, E. J., Boutouyrie, P., Cameron, J., Chen, C.-H., Cruickshank, J. K., Hwang, S.-J., Lakatta, E. G., Laurent, S., Maldonado, J., Mitchell, G. F., Najjar, S. S., Newman, A. B., Ohishi, M., Pannier, B., Pereira, T., Vasan, R. S., Shokawa, T., Sutton-Tyrell, K., Verbeke, F., Wang, K.-L., Webb, D. J., Willum Hansen, T., Zoungas, S., McEniery, C. M., Cockcroft, J. R., & Wilkinson, I. B.
- Affiliations
- University of Bristol (GB)
- University of Manchester (GB)
- Boston University (US)
- Inserm (FR)
- Monash Medical Centre (AU)
- National Yang Ming University (TW)
- Guy's Hospital (GB)
- National Heart, Lung, and Blood Institute (US)
- National Institutes of Health (US)
- University of Pittsburgh (US)
- The University of Osaka (JP)
- Escola Superior de Tecnologia da Saúde de Coimbra (PT)
- Hiroshima University (JP)
- Ghent University Hospital (BE)
- The Queen's Medical Research Institute (GB)
- Monash University (AU)
- University of Cambridge (GB)
- ORCID
- May, Margaret
- Anderson, Simon G.
- Benjamin, Emelia J.
- Boutouyrie, Pierre
- Cameron, James
- Cruickshank, J. Kennedy
- Hwang, Shih-Jen
- Lakatta, Edward G.
- Maldonado, João
- Mitchell, Gary F.
- Najjar, Samer S.
- Newman, Anne B.
- Pereira, Telmo
- Vasan, Ramachandran S.
- Verbeke, Francis
- Webb, David J.
- Zoungas, Sophia
- McEniery, Carmel M.
- Wilkinson, Ian B.
- Venue
- Journal of the American College of Cardiology
- Publisher
- Elsevier BV
- Published
- 2014-02
- Volume
- 63
- Issue
- 7
- Pages
- 636-646
- ISSN
- 0735-1097 (ISSN-L)
- PMC
- PMC4401072
- OpenAlex
- W2143691602
- Cited by
- 1,835
- References
- 60
- Citations by year
- 2026: 105 · 2025: 132 · 2024: 151 · 2023: 134 · 2022: 178 · 2021: 179 · 2020: 169 · 2019: 162 · 2018: 144 · 2017: 145 · 2016: 128 · 2015: 133 · 2014: 73 · 2013: 1
- FWCI
- 75.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of the American College of Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- PubMed Central (acceptedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Explore Bristol Research (submittedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Funders
- National Institutes of Health Research Methods Training Fellowship
- AtCor and Esaote
- National Institutes of Health.
- HeartWare Inc.
- British Heart Foundation
- Cambridge Biomedical Research Centre
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Disease Prevention
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Function and Risk Factors
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Disease and Adiposity
- Keywords
- aortic pulse wave velocity, cardiovascular risk prediction, coronary artery disease, stroke, cardiovascular events, risk reclassification, atherosclerosis, diabetes mellitus, hypertension, risk stratification, sex differences, subgroup analysis, cardiovascular risk factors, cardiovascular risk prediction models
- MeSH terms
- Aorta, Cardiovascular Diseases, Humans, Predictive Value of Tests, Prospective Studies, Pulse Wave Analysis, Observational Studies as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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