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

Comput Math Methods Med. 2016:2016:7816830. doi: 10.1155/2016/7816830. Epub 2016 May 9.

Abstract

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 fuzzy c-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, and 62.7 ± 23.2 mL/min/1.73 m(2) in 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.

Publication types

  • Observational Study

MeSH terms

  • Aged
  • Algorithms
  • Analysis of Variance
  • Blood Pressure Determination*
  • Blood Pressure Monitoring, Ambulatory / methods*
  • Blood Pressure*
  • Circadian Rhythm
  • Computer Simulation
  • Female
  • Fuzzy Logic
  • Glomerular Filtration Rate
  • Humans
  • Hypertension / complications
  • Hypertension / physiopathology*
  • Male
  • Middle Aged
  • Models, Statistical
  • Multivariate Analysis
  • Regression Analysis
  • Risk Factors
  • Systole