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

MedStar author(s):
Citation: Journal of the American College of Cardiology. 63(7):636-46, 2014 Feb 25.PMID: 24239664Institution: MedStar Heart & Vascular InstituteForm of publication: Journal ArticleMedline article type(s): Journal Article | Meta-Analysis | Research Support, Non-U.S. Gov't | ReviewSubject headings: *Aorta/ph [Physiology] | *Cardiovascular Diseases/di [Diagnosis] | *Cardiovascular Diseases/pp [Physiopathology] | *Pulse Wave Analysis/mt [Methods] | Cardiovascular Diseases/ep [Epidemiology] | Humans | Observational Study as Topic | Predictive Value of Tests | Prospective StudiesLocal holdings: Available online from MWHC library: 1995 - present, Available in print through MWHC library:1999-2007ISSN:
  • 0735-1097
Name of journal: Journal of the American College of CardiologyAbstract: 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.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. Copyright 2014 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.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.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.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 20140821 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.All authors: Anderson SG, Ben-Shlomo Y, Benjamin EJ, Boustred C, Boutouyrie P, Cameron J, Chen CH, Cockcroft JR, Cruickshank JK, Hwang SJ, Lakatta EG, Laurent S, Maldonado J, May M, McEniery CM, Mitchell GF, Najjar SS, Newman AB, Ohishi M, Pannier B, Pereira T, Shokawa T, Spears M, Sutton-Tyrell K, Vasan RS, Verbeke F, Wang KL, Webb DJ, Wilkinson IB, Willum Hansen T, Zoungas SDigital Object Identifier: Date added to catalog: 2014-08-21
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Item type Current library Collection Call number Status Date due Barcode
Journal Article MedStar Authors Catalog Article Available 24239664

Available online from MWHC library: 1995 - present, Available in print through MWHC library:1999-2007

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.

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. Copyright 2014 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

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.

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.

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 20140821 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.

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