Abstract
AbstractIn considering the impact of blood-pressure variability on health outcomes, two methodological challenges arise: The presence of multiple timescales of variability that may act independently and interactively, and the fairly large stochastic uncertainty that is inevitable in estimates of individual variability based on modest numbers of observations. Here we present an application of Bayesian hierarchical modeling to the problem of estimating the effect of blood pressure (BP) variability on all-cause and cardiovascular mortality with two timescales – short-term variation among multiple measures at one visit, and medium-term variation between the measures at two visits several months apart. We use data from the Third National Health and Nutrition Examination Survey linked with up to 27 years of mortality follow-up. We find that medium-term systolic BP variability had a very significant predictive value for CV and all-cause mortality, around one-third as large as the well-established impact of mean systolic BP. Medium-term diastolic variability had an additional, though smaller, predictive effect. Short-term variability, on the other hand, had little or no measurable predictive value. The medium-term variability effect persisted when controlling for Framingham risk score.
Publisher
Cold Spring Harbor Laboratory
Reference34 articles.
1. Society of Actuaries. Blood Pressure Study. 1939.
2. Society of Actuaries. Blood Pressure: Report of the Joint Committee on Mortality of the Association of Life Insurance Medical Directors and the Actuarial Society of America.
3. A multivariate analysis of the risk of coronary heart disease in Framingham
4. Probability of Middle-Aged Men Developing Coronary Heart Disease in Five Years
5. Effects of Treatment on Morbidity in Hypertension