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Meta-Analysis
. 2014 Feb 25;63(7):636-646.
doi: 10.1016/j.jacc.2013.09.063. Epub 2013 Nov 13.

Aortic pulse wave velocity improves cardiovascular event prediction: an individual participant meta-analysis of prospective observational data from 17,635 subjects

Yoav Ben-Shlomo  1 , Melissa Spears  2 , Chris Boustred  2 , Margaret May  2 , Simon G Anderson  3 , Emelia J Benjamin  4 , Pierre Boutouyrie  5 , James Cameron  6 , Chen-Huan Chen  7 , J Kennedy Cruickshank  8 , Shih-Jen Hwang  9 , Edward G Lakatta  10 , Stephane Laurent  5 , João Maldonado  11 , Gary F Mitchell  12 , Samer S Najjar  13 , Anne B Newman  14 , Mitsuru Ohishi  15 , Bruno Pannier  16 , Telmo Pereira  17 , Ramachandran S Vasan  18 , Tomoki Shokawa  19 , Kim Sutton-Tyrell  14 , Francis Verbeke  20 , Kang-Ling Wang  7 , David J Webb  21 , Tine Willum Hansen  22 , Sophia Zoungas  23 , Carmel M McEniery  24 , John R Cockcroft  25 , Ian B Wilkinson  24
Affiliations
Meta-Analysis

Aortic pulse wave velocity improves cardiovascular event prediction: an individual participant meta-analysis of prospective observational data from 17,635 subjects

Yoav Ben-Shlomo et al. J Am Coll Cardiol. .

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.

Keywords: cardiovascular disease; meta-analysis; prognostic factor; pulse wave velocity.

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Figures

Figure 1
Figure 1. Flow diagram illustrating the process of study identification
Figure 2
Figure 2. Forest plot for aPWV and combined cardiovascular events adjusting for various risk factors
(a) Adjustment for age and sex Loge aPWV is shown. Size of box represents the study specific weight for the meta-analysis. BTC – Belgian Transplant Cohort, CORD – Calcification Outcome in Renal Disease, CaPS – Caerphilly Prospective Study. ES=effect size (b) Adjustment for age, sex and other cardiovascular risk factors. Loge aPWV is shown. Adjusted for age, sex, systolic blood pressure, total cholesterol, HDL-cholesterol, diabetes, and antihypertensive use. Data from BLSA were excluded as there were too few events. Size of box represents the study specific weight for the meta-analysis. BTC – Belgian Transplant Cohort, CORD – Calcification Outcome in Renal Disease, CaPS – Caerphilly Prospective Study, CVD – cardiovascular disease. ES=effect size
Figure 2
Figure 2. Forest plot for aPWV and combined cardiovascular events adjusting for various risk factors
(a) Adjustment for age and sex Loge aPWV is shown. Size of box represents the study specific weight for the meta-analysis. BTC – Belgian Transplant Cohort, CORD – Calcification Outcome in Renal Disease, CaPS – Caerphilly Prospective Study. ES=effect size (b) Adjustment for age, sex and other cardiovascular risk factors. Loge aPWV is shown. Adjusted for age, sex, systolic blood pressure, total cholesterol, HDL-cholesterol, diabetes, and antihypertensive use. Data from BLSA were excluded as there were too few events. Size of box represents the study specific weight for the meta-analysis. BTC – Belgian Transplant Cohort, CORD – Calcification Outcome in Renal Disease, CaPS – Caerphilly Prospective Study, CVD – cardiovascular disease. ES=effect size
Figure 3
Figure 3. Forest plot for a PWV with cardiovascular events according to pre-specified subgroups
Loge aPWV is shown. Data are adjusted for age and sex where applicable. Data from BLSA were excluded as there were too few events. ES=effect size
See this image and copyright information in PMC

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