scholarly journals Can Ambulatory Blood Pressure Variability Contribute to Individual Cardiovascular Risk Stratification?

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Annamária Magdás ◽  
László Szilágyi ◽  
Alexandru Incze
Keyword(s):  
Blood Pressure ◽  
Risk Factor ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factor ◽  
Blood Pressure Variability ◽  
Antihypertensive Treatment ◽  
Threshold Value ◽  
High Variability ◽  
Normal Range ◽  
Average Real Variability

Objective. The aim of this study is to define the normal range for average real variability (ARV) and to establish whether it can be considered as an additional cardiovascular risk factor.Methods. In this observational study, 110 treated hypertensive patients were included and admitted for antihypertensive treatment adjustment. Circadian blood pressure was recorded with validated devices. Blood pressure variability (BPV) was assessed according to the ARV definition. Based on their variability, patients were classified into low, medium, and high variability groups using the fuzzyc-means algorithm. To assess cardiovascular risk, blood samples were collected. Characteristics of the groups were compared by ANOVA tests.Results. Low variability was defined as ARV below 9.8 mmHg (32 patients), medium as 9.8–12.8 mmHg (48 patients), and high variability above 12.8 mmHg (30 patients). Mean systolic blood pressure was 131.2 ± 16.7, 135.0 ± 12.1, and 141.5 ± 11.4 mmHg in the low, medium, and high variability groups, respectively (p=0.0113). Glomerular filtration rate was 78.6 ± 29.3, 74.8 ± 26.4, and62.7±23.2 mL/min/1.73 m2in the low, medium, and high variability groups, respectively (p=0.0261).Conclusion. Increased values of average real variability represent an additional cardiovascular risk factor. Therefore, reducing BP variability might be as important as achieving optimal BP levels, but there is need for further studies to define a widely acceptable threshold value.

scholarly journals Home Blood Pressure Variability as Cardiovascular Risk Factor in the Population of Ohasama

Hypertension ◽  
2013 ◽  
Vol 61 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Kei Asayama ◽  
Masahiro Kikuya ◽  
Rudolph Schutte ◽  
Lutgarde Thijs ◽  
Miki Hosaka ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Risk Factor ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factor ◽  
Blood Pressure Variability ◽  
Home Blood Pressure

A Comparative Study of Short-Term Blood Pressure Variability in Hemodialysis Patients with and without Intradialytic Hypertension

2018 ◽  
Vol 48 (4) ◽  
pp. 295-305 ◽  
Author(s):  
Athanasios Bikos ◽  
Elena Angeloudi ◽  
Evangelos Memmos ◽  
Charalampos Loutradis ◽  
Antonios Karpetas ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Risk ◽  
Blood Pressure Variability ◽  
Hemodialysis Patients ◽  
Adverse Outcomes ◽  
Short Term ◽  
Ambulatory Bp Monitoring ◽  
Average Real Variability ◽  
Regular Hemodialysis ◽  
Sustained Increase

Background: Short-term blood pressure (BP) variability (BPV) is associated with increased cardiovascular risk in hemodialysis. Patients with intradialytic hypertension have high risk of adverse outcomes. Whether BPV is increased in these patients is not clear. The purpose of this study was to compare short-term BPV in patients with and without intradialytic hypertension. Methods: Forty-one patients with and 82 patients without intradialytic hypertension (intradialytic SBP rise ≥10 mm Hg to > 150 mm Hg) matched in a 1: 2 ratio for age, sex, and hemodialysis vintage were included. All subjects underwent 48-h ambulatory BP monitoring during a regular hemodialysis and the subsequent interdialytic interval. Brachial and aortic BPV were calculated with validated formulas and compared between the 2 groups during the 48-h and the 44-h periods and during the 2 daytime and nighttime periods respectively. Results: During 48-h or 44-h periods and daytime or nighttime, brachial SBP/DBP and aortic SBP/DBP were significantly higher in cases than in controls. All brachial SBP/DBP BPV indexes [SD, weighted SD (wSD), coefficient-of-variation (CV) and average-real-variability (ARV)] were not significantly different between groups during the 48- or 44-h periods (48-h: SBP-ARV 11.59 ± 3.05 vs. 11.70 ± 2.68, p = 0.844, DBP-ARV: 8.60 ± 1.90 vs. 8.90 ± 1.63, p = 0.357). Analysis stratified by day or night between days 1 and 2 revealed, in general, similar results. No significant differences in dipping pattern were observed between groups. Analysis of aortic BPV had similar findings. Conclusions: BPV is similar between those with and without intradialytic hypertension. However, those with intradialytic hypertension have a sustained increase in systolic and diastolic BP during the entire interdialytic interval.

Blood pressure variability, cardiovascular risk and antihypertensive treatment

1995 ◽  
Vol 13 (supplement4) ◽  
pp. S27-A34 ◽  
Author(s):  
Gianfranco Parati ◽  
Luisa Ulian ◽  
Cinzia Santucciu ◽  
Stefano Omboni ◽  
Giuseppe Mancia
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Risk ◽  
Blood Pressure Variability ◽  
Antihypertensive Treatment

scholarly journals Relation of blood pressure and body mass index during childhood to cardiovascular risk factor levels in young adults

2009 ◽  
Vol 27 (9) ◽  
pp. 1766-1774 ◽  
Author(s):  
Erin R Rademacher ◽  
David R Jacobs ◽  
Antoinette Moran ◽  
Julia Steinberger ◽  
Ronald J Prineas ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Body Mass Index ◽  
Young Adults ◽  
Risk Factor ◽  
Cardiovascular Risk ◽  
Body Mass ◽  
Cardiovascular Risk Factor

scholarly journals Linking big data for cardiovascular health surveillance – opportunities and challenges using the CANHEART cohort

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Anna Chu ◽  
Deirdre Hennessy ◽  
Sharon Johnston ◽  
Jacob Udell ◽  
Dennis Ko ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Big Data ◽  
Risk Factor ◽  
Cardiovascular Risk ◽  
Population Health ◽  
Cardiovascular Risk Factor ◽  
Cardiovascular Health ◽  
Population Based ◽  
Health Surveillance ◽  
Risk Factor Prevalence

IntroductionOur increasing ability to link large population-based health administrative datasets to create ‘big data’ cohorts offers unique opportunities to conduct health and health services surveillance at lower costs than traditional methods using surveys or primary data collection. However, comparability of findings from big data with traditional methods is unknown. Objectives and ApproachIn the CArdiovascular HEalth in Ambulatory Care Research Team (CANHEART) ‘big data’ initiative, we linked 19 population-based health databases to obtain baseline and 5-year follow-up health information on a cohort of 9.8 million adult residents of Ontario, Canada as of January 2008. We compared cardiovascular risk factor prevalence with results from 3500 participants in the 2007-09 Canadian Health Measures Survey (CHMS), a traditional population health surveillance survey. Additionally, we determined cardiovascular preventative care use and clinical event rates by sex and age. Planned linkages to new data sources will enable continued cohort surveillance of population health-related and care indicators. ResultsCholesterol and glucose levels determined from the CANHEART cohort were comparable to the CHMS, whereas blood pressure values and obesity rates were substantially higher. Overall, receipt of cardiovascular preventive care in the CANHEART cohort was high, with 85.7% of males and 91.8% of females having blood pressure assessments, and 67.8% of males and 79.4% of females having weight assessments. Cholesterol and diabetes screening rates among those recommended for screening were over 75%. Incidence of myocardial infarction, stroke or cardiovascular death was 51% higher among males than females (3.8 and 2.5 events per 1000 person-years, respectively). Challenges encountered in analyzing data included treatment of repeated and time-varying measures, selection of valid diagnostic and physician billing codes, changing coding practices and handling of missing and outlying data. Conclusion/ImplicationsComparability of cardiovascular risk factor prevalence using linked administrative data with survey methods varies by indicator. Selection biases amongst survey participants and different measurement methods could explain discrepancies. The added ability to examine health care indicators longitudinally and by subgroup supports use of linked population-based data to enhance health surveillance.

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