Aortic pulse wave velocity improves cardiovascular event prediction

An individual participant meta-analysis of prospective observational data from 17,635 subjects

Yoav Ben-Shlomo, Melissa Spears, Chris Boustred, Margaret May, Simon G. Anderson, Emelia J. Benjamin, Pierre Boutouyrie, James Cameron, Chen Huan Chen, J. Kennedy Cruickshank, Shih Jen Hwang, Edward Lakatta, Stephane Laurent, João Maldonado, Gary F. Mitchell, Samer S. Najjar, Anne B. Newman, Mitsuru Ohishi, Bruno Pannier, Telmo Pereira & 11 others Ramachandran S. Vasan, Tomoki Shokawa, Kim Sutton-Tyrell, Francis Verbeke, Kang Ling Wang, David J. Webb, Tine Willum Hansen, Sophia Zoungas, Carmel M. McEniery, John R. Cockcroft, Ian B. Wilkinson

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)636-646
Number of pages11
JournalJournal of the American College of Cardiology
Volume63
Issue number7
DOIs
StatePublished - Feb 25 2014
Externally publishedYes

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Pulse Wave Analysis
Meta-Analysis
Cardiovascular Diseases
Confidence Intervals
Risk Management
Proportional Hazards Models
Coronary Disease
Stroke

Keywords

  • cardiovascular disease
  • meta-analysis
  • prognostic factor
  • pulse wave velocity

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Cite this

Aortic pulse wave velocity improves cardiovascular event prediction : An individual participant meta-analysis of prospective observational data from 17,635 subjects. / Ben-Shlomo, Yoav; Spears, Melissa; Boustred, Chris; May, Margaret; Anderson, Simon G.; Benjamin, Emelia J.; Boutouyrie, Pierre; Cameron, James; Chen, Chen Huan; Cruickshank, J. Kennedy; Hwang, Shih Jen; Lakatta, Edward; Laurent, Stephane; Maldonado, João; Mitchell, Gary F.; Najjar, Samer S.; Newman, Anne B.; Ohishi, Mitsuru; Pannier, Bruno; Pereira, Telmo; Vasan, Ramachandran S.; Shokawa, Tomoki; Sutton-Tyrell, Kim; Verbeke, Francis; Wang, Kang Ling; Webb, David J.; Willum Hansen, Tine; Zoungas, Sophia; McEniery, Carmel M.; Cockcroft, John R.; Wilkinson, Ian B.

In: Journal of the American College of Cardiology, Vol. 63, No. 7, 25.02.2014, p. 636-646.

Research output: Contribution to journalArticle

Ben-Shlomo, Y, Spears, M, Boustred, C, May, M, Anderson, SG, Benjamin, EJ, Boutouyrie, P, Cameron, J, Chen, CH, Cruickshank, JK, Hwang, SJ, Lakatta, E, Laurent, S, Maldonado, J, Mitchell, GF, Najjar, SS, Newman, AB, Ohishi, M, Pannier, B, Pereira, T, Vasan, RS, Shokawa, T, Sutton-Tyrell, K, Verbeke, F, Wang, KL, Webb, DJ, Willum Hansen, T, Zoungas, S, McEniery, CM, Cockcroft, JR & Wilkinson, IB 2014, 'Aortic pulse wave velocity improves cardiovascular event prediction: An individual participant meta-analysis of prospective observational data from 17,635 subjects', Journal of the American College of Cardiology, vol. 63, no. 7, pp. 636-646. https://doi.org/10.1016/j.jacc.2013.09.063
Ben-Shlomo, Yoav ; Spears, Melissa ; Boustred, Chris ; May, Margaret ; Anderson, Simon G. ; Benjamin, Emelia J. ; Boutouyrie, Pierre ; Cameron, James ; Chen, Chen Huan ; Cruickshank, J. Kennedy ; Hwang, Shih Jen ; Lakatta, Edward ; Laurent, Stephane ; Maldonado, João ; Mitchell, Gary F. ; Najjar, Samer S. ; Newman, Anne B. ; Ohishi, Mitsuru ; Pannier, Bruno ; Pereira, Telmo ; Vasan, Ramachandran S. ; Shokawa, Tomoki ; Sutton-Tyrell, Kim ; Verbeke, Francis ; Wang, Kang Ling ; Webb, David J. ; Willum Hansen, Tine ; Zoungas, Sophia ; McEniery, Carmel M. ; Cockcroft, John R. ; Wilkinson, Ian B. / Aortic pulse wave velocity improves cardiovascular event prediction : An individual participant meta-analysis of prospective observational data from 17,635 subjects. In: Journal of the American College of Cardiology. 2014 ; Vol. 63, No. 7. pp. 636-646.
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abstract = "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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TY - JOUR

T1 - Aortic pulse wave velocity improves cardiovascular event prediction

T2 - An individual participant meta-analysis of prospective observational data from 17,635 subjects

AU - Ben-Shlomo, Yoav

AU - Spears, Melissa

AU - Boustred, Chris

AU - May, Margaret

AU - Anderson, Simon G.

AU - Benjamin, Emelia J.

AU - Boutouyrie, Pierre

AU - Cameron, James

AU - Chen, Chen Huan

AU - Cruickshank, J. Kennedy

AU - Hwang, Shih Jen

AU - Lakatta, Edward

AU - Laurent, Stephane

AU - Maldonado, João

AU - Mitchell, Gary F.

AU - Najjar, Samer S.

AU - Newman, Anne B.

AU - Ohishi, Mitsuru

AU - Pannier, Bruno

AU - Pereira, Telmo

AU - Vasan, Ramachandran S.

AU - Shokawa, Tomoki

AU - Sutton-Tyrell, Kim

AU - Verbeke, Francis

AU - Wang, Kang Ling

AU - Webb, David J.

AU - Willum Hansen, Tine

AU - Zoungas, Sophia

AU - McEniery, Carmel M.

AU - Cockcroft, John R.

AU - Wilkinson, Ian B.

PY - 2014/2/25

Y1 - 2014/2/25

N2 - 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.

AB - 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.

KW - cardiovascular disease

KW - meta-analysis

KW - prognostic factor

KW - pulse wave velocity

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