THE DYNAMICS OF BLOOD PRESSURE VARIABILITY ASSESSED THROUGH AMBULATORIAL BLOOD PRESSURE MONITORING – 24 HOUR

Objective: Blood pressure exhibits dynamic behavior; however, there are still divergences in the literature regarding the characterization of its variability. In general, dynamic systems can be classified into three main domains: linear, chaotic, and stochastic, according to their underlying properties. Objective: To evaluate time series obtained through 24-hour ambulatory blood pressure monitoring (ABPM) using nonlinear variables to characterize the behavior of these series, hypothesizing that blood pressure variability exhibits chaotic behavior. Design and method: Methods: This observational study analyzed 70 time series from 24-hour ambulatory blood pressure monitoring (ABPM) in outpatients undergoing cardiological evaluation. The analysis focused on sequential variability in systolic blood pressure values in their original and randomized forms. Calculations included the Lyapunov exponent, Hurst exponent, and Correlation Time (TAU). Equivalent values, when compared to the original series, would indicate that it exhibits stochastic behavior. The detection of statistically significant differences in the comparison would indicate that the original series exhibits chaotic behavior. The possibility of linear behavior is automatically excluded due to already established knowledge. Statistical Analysis: Parametric data were presented as mean ± standard deviation. Comparisons between parametric variables were performed using a two-tailed paired t-test. An alpha error of 5% was assumed, with p-values less than or equal to 0.05 considered significant. Results: Results: Significant differences were found in the comparison between the original series and their respective randomized series. The Lyapunov exponent of systolic blood pressure variability in the original series was significantly different from that of the randomized series (mean difference = -0.037171 ± 0.112697; p = 0.0074), indicating that the analyzed time series does not exhibit random behavior and, therefore, belongs to a deterministic nonlinear system, i.e., a chaotic system. The Hurst exponent corroborates the findings of the Lyapunov exponent, as does the Correlation Time, highlighting the differences between the original and randomized time series. For the Hurst exponent, the mean difference was 0.080202 ± 0.098542 (p < 0.0001), and for the Correlation Time, it was 0.238497 ± 0.249643 (p < 0.0001). Conclusions: Conclusion: Blood pressure variability exhibits nonlinear dynamic behavior, that is, chaotic.