Blood pressure variability: cardiovascular risk integrator or independent risk factor?
| Autor: | Camille Ly, F Szabo de Edelenyi, Serge Hercberg, Pilar Galan, Jacques Blacher, Michel E. Safar |
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| Přispěvatelé: | Université Paris Descartes - Paris 5 (UPD5), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM), French Ministry of Research R02010JJ, Ministry of Health (DGS), Sodexo, Candia, Unilever, Danone, Roche Laboratory, Merck EPROVA GS, Pierre Fabre Laboratory |
| Rok vydání: | 2014 |
| Předmět: |
Male
medicine.medical_specialty [SDV]Life Sciences [q-bio] Population Blood Pressure 030204 cardiovascular system & hematology Logistic regression 03 medical and health sciences 0302 clinical medicine Predictive Value of Tests Risk Factors Diabetes mellitus Internal medicine Internal Medicine medicine Humans 030212 general & internal medicine Risk factor education Aged education.field_of_study business.industry Regression analysis Middle Aged medicine.disease EPISODIC HYPERTENSION 3. Good health Blood pressure Cardiovascular Diseases Predictive value of tests Cardiology Regression Analysis Female France business Risk assessment |
| Zdroj: | Journal of Human Hypertension Journal of Human Hypertension, Nature Publishing Group, 2015, 29 (2), pp.122-126. ⟨10.1038/jhh.2014.44⟩ |
| ISSN: | 1476-5527 0950-9240 |
| DOI: | 10.1038/jhh.2014.44 |
| Popis: | International audience; Blood pressure (BP) variability is associated with several cardiovascular (CV) risk factors. Is BP variability measurement of any additive value, in terms of CV risk assessment strategies? To answer this question, we analyzed data from the SU.FOL.OM3 secondary prevention trial that included 2501 patients with background of CV disease history (coronary or cerebrovascular disease). BP was measured every year allowing calculation of variability of BP, expressed as s.d. and coefficient of variability (s.d./mean systolic BP) in 2157 patients. We found that systolic BP variability was associated with several CV risk factors: principally hypertension, age, and diabetes. Furthermore, all antihypertensives were positively associated with variability. Logistic regression analysis revealed that three factors were independent predictors of major CV event: coefficient of variability of systolic BP (OR = 1.23 per s.d., 95% CI: 1.04-1.46, P = 0.016), current smoking (OR = 1.94, 95% CI: 1.03-3.66, P = 0.039), and inclusion for cerebrovascular disease (OR = 1.92, 95% CI: 1.29-2.87, P = 0.001). Finally, when comparing logistic regression models characteristics without, and then with, inclusion of BP variability, there was a modest but statistically significant improvement (P = 0.04). In conclusion, age, BP and diabetes were the major determinants of BP variability. Furthermore, BP variability has an independent prognostic value in the prediction of major CV events; but improvement in the prediction model was quite modest. This last finding is more in favor of BP variability acting as an integrator of CV risk than acting as a robust independent CV risk factor in this high-risk population. |
| Databáze: | OpenAIRE |
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