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

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. Several studies have shown that aPWV may be a useful risk factor for predicting CVD, but they have been underpowered to exami...

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Vydáno v:Journal of the American College of Cardiology Ročník 63; číslo 7; s. 636
Hlavní autoři: 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 G, 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
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 25.02.2014
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ISSN:1558-3597, 1558-3597
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Abstract 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. 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. 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. 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. 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.
AbstractList 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.OBJECTIVESThe goal of this study was to determine whether aortic pulse wave velocity (aPWV) improves prediction of cardiovascular disease (CVD) events beyond conventional risk factors.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.BACKGROUNDSeveral 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.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.METHODSWe 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.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.RESULTSOf 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.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.CONCLUSIONSConsideration 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.
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. 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. 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. 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. 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.
Author Vasan, Ramachandran S
Hwang, Shih-Jen
Anderson, Simon G
Cameron, James
Lakatta, Edward G
Cockcroft, John R
Webb, David J
Verbeke, Francis
May, Margaret
Laurent, Stephane
Ohishi, Mitsuru
Maldonado, João
Boutouyrie, Pierre
Boustred, Chris
Cruickshank, J Kennedy
Shokawa, Tomoki
Chen, Chen-Huan
Zoungas, Sophia
Pereira, Telmo
Newman, Anne B
Sutton-Tyrell, Kim
Benjamin, Emelia J
Willum Hansen, Tine
Najjar, Samer S
Pannier, Bruno
Wang, Kang-Ling
Spears, Melissa
Mitchell, Gary F
Wilkinson, Ian B
Ben-Shlomo, Yoav
McEniery, Carmel M
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  surname: Ben-Shlomo
  fullname: Ben-Shlomo, Yoav
  email: y.ben-shlomo@bristol.ac.uk
  organization: School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom. Electronic address: y.ben-shlomo@bristol.ac.uk
– sequence: 2
  givenname: Melissa
  surname: Spears
  fullname: Spears, Melissa
  organization: School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
– sequence: 3
  givenname: Chris
  surname: Boustred
  fullname: Boustred, Chris
  organization: School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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  givenname: Margaret
  surname: May
  fullname: May, Margaret
  organization: School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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  givenname: Simon G
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  organization: Institute of Cardiovascular Sciences, University of Manchester, United Kingdom
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  organization: National Heart Lung and Blood Institute and Boston University's Framingham Heart Study, Cardiology Section, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
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  organization: INSERM U 970, Paris-Descartes University, Hopital Europeen Georges Pompidou, Assistance Publique Hopitaux de Paris, Paris, France
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  givenname: James
  surname: Cameron
  fullname: Cameron, James
  organization: Monash Cardiovascular Research Centre, MonashHEART and Monash University Department of Medicine (MMC), Melbourne, Australia
– sequence: 9
  givenname: Chen-Huan
  surname: Chen
  fullname: Chen, Chen-Huan
  organization: School of Medicine, National Yang-Ming University, Taipei, Taiwan
– sequence: 10
  givenname: J Kennedy
  surname: Cruickshank
  fullname: Cruickshank, J Kennedy
  organization: King's College & King's Health Partners, St. Thomas' & Guy's Hospital, London, United Kingdom
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  givenname: Shih-Jen
  surname: Hwang
  fullname: Hwang, Shih-Jen
  organization: Branch of Population Sciences, Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, Maryland
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  givenname: Edward G
  surname: Lakatta
  fullname: Lakatta, Edward G
  organization: Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
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  givenname: Stephane
  surname: Laurent
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  organization: INSERM U 970, Paris-Descartes University, Hopital Europeen Georges Pompidou, Assistance Publique Hopitaux de Paris, Paris, France
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  givenname: João
  surname: Maldonado
  fullname: Maldonado, João
  organization: Instituto de Investigação e Formação Cardiovascular, Penacova, Portugal
– sequence: 15
  givenname: Gary F
  surname: Mitchell
  fullname: Mitchell, Gary F
  organization: Cardiovascular Engineering, Inc., Norwood, Massachusetts
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  givenname: Samer S
  surname: Najjar
  fullname: Najjar, Samer S
  organization: Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, Maryland; MedStar Heart Research Institute, Washington, DC
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  givenname: Anne B
  surname: Newman
  fullname: Newman, Anne B
  organization: Center for Aging and Population Health, Pittsburgh, Pennsylvania
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  givenname: Mitsuru
  surname: Ohishi
  fullname: Ohishi, Mitsuru
  organization: Department of Geriatric Medicine, Osaka University, Osaka, Japan
– sequence: 19
  givenname: Bruno
  surname: Pannier
  fullname: Pannier, Bruno
  organization: Centre d'Investigations Preventives et Cliniques, Paris, France
– sequence: 20
  givenname: Telmo
  surname: Pereira
  fullname: Pereira, Telmo
  organization: Escola Superior de Tecnologia da Saúde de Coimbra, Coimbra, Portugal
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  givenname: Ramachandran S
  surname: Vasan
  fullname: Vasan, Ramachandran S
  organization: National Heart Lung and Blood Institute and Boston University's Framingham Heart Study, Department of Medicine, Boston University, Boston, Massachusetts
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  givenname: Tomoki
  surname: Shokawa
  fullname: Shokawa, Tomoki
  organization: Department of Molecular and Internal Medicine, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan
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  givenname: Kim
  surname: Sutton-Tyrell
  fullname: Sutton-Tyrell, Kim
  organization: Center for Aging and Population Health, Pittsburgh, Pennsylvania
– sequence: 24
  givenname: Francis
  surname: Verbeke
  fullname: Verbeke, Francis
  organization: Department of Nephrology, Ghent University Hospital, Ghent, Belgium
– sequence: 25
  givenname: Kang-Ling
  surname: Wang
  fullname: Wang, Kang-Ling
  organization: School of Medicine, National Yang-Ming University, Taipei, Taiwan
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  givenname: David J
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  organization: University/BHF Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
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  givenname: Tine
  surname: Willum Hansen
  fullname: Willum Hansen, Tine
  organization: Research Center for Prevention and Health, Glostrup Hospital, Glostrup and Steno Diabetes Center, Glostrup, Denmark
– sequence: 28
  givenname: Sophia
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  organization: School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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  fullname: McEniery, Carmel M
  organization: Clinical Pharmacology Unit, University of Cambridge, Cambridge, United Kingdom
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  givenname: John R
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  organization: Wales Heart Research Institute, Cardiff, United Kingdom
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  surname: Wilkinson
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  organization: Clinical Pharmacology Unit, University of Cambridge, Cambridge, United Kingdom
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Keywords meta-analysis
cardiovascular disease
prognostic factor
pulse wave velocity
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References 24239659 - J Am Coll Cardiol. 2014 Feb 25;63(7):647-649. doi: 10.1016/j.jacc.2013.10.040.
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Snippet The goal of this study was to determine whether aortic pulse wave velocity (aPWV) improves prediction of cardiovascular disease (CVD) events beyond...
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StartPage 636
SubjectTerms Aorta - physiology
Cardiovascular Diseases - diagnosis
Cardiovascular Diseases - epidemiology
Cardiovascular Diseases - physiopathology
Humans
Observational Studies as Topic
Predictive Value of Tests
Prospective Studies
Pulse Wave Analysis - methods
Title Aortic pulse wave velocity improves cardiovascular event prediction: an individual participant meta-analysis of prospective observational data from 17,635 subjects
URI https://www.ncbi.nlm.nih.gov/pubmed/24239664
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