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 |
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Yoav 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 – sequence: 4 givenname: Margaret surname: May fullname: May, Margaret organization: School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom – sequence: 5 givenname: Simon G surname: Anderson fullname: Anderson, Simon G organization: Institute of Cardiovascular Sciences, University of Manchester, United Kingdom – sequence: 6 givenname: Emelia J surname: Benjamin fullname: Benjamin, Emelia J 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 – sequence: 7 givenname: Pierre surname: Boutouyrie fullname: Boutouyrie, Pierre organization: INSERM U 970, Paris-Descartes University, Hopital Europeen Georges Pompidou, Assistance Publique Hopitaux de Paris, Paris, France – sequence: 8 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 – sequence: 11 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 – sequence: 12 givenname: Edward G surname: Lakatta fullname: Lakatta, Edward G organization: Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, Maryland – sequence: 13 givenname: Stephane surname: Laurent fullname: Laurent, Stephane organization: INSERM U 970, Paris-Descartes University, Hopital Europeen Georges Pompidou, Assistance Publique Hopitaux de Paris, Paris, France – sequence: 14 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 – sequence: 16 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 – sequence: 17 givenname: Anne B surname: Newman fullname: Newman, Anne B organization: Center for Aging and Population Health, Pittsburgh, Pennsylvania – sequence: 18 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 – sequence: 21 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 – sequence: 22 givenname: Tomoki surname: Shokawa fullname: Shokawa, Tomoki organization: Department of Molecular and Internal Medicine, Graduate School of Biomedical Sciences, Hiroshima University, Hiroshima, Japan – sequence: 23 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 – sequence: 26 givenname: David J surname: Webb fullname: Webb, David J organization: University/BHF Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom – sequence: 27 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 surname: Zoungas fullname: Zoungas, Sophia organization: School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia – sequence: 29 givenname: Carmel M surname: McEniery fullname: McEniery, Carmel M organization: Clinical Pharmacology Unit, University of Cambridge, Cambridge, United Kingdom – sequence: 30 givenname: John R surname: Cockcroft fullname: Cockcroft, John R organization: Wales Heart Research Institute, Cardiff, United Kingdom – sequence: 31 givenname: Ian B surname: Wilkinson fullname: Wilkinson, Ian B 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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| 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 |
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