Atrial Fibrillation and Hypertension: “Quo Vadis”

Hypertension is one of the most well-established risk factors for atrial fibrillation. Long-standing untreated hypertension leads to structural remodeling and electrophysiologic alterations causing an atrial myopathy that forms a vulnerable substrate for the development and maintenance of atrial fibrillation. Hypertension-induced hemodynamic, inflammatory, hormonal, and autonomic changes all appear to be important contributing factors. Furthermore, hypertension is also associated with several atrial fibrillation-related comorbidities. As such, hypertension may represent an important target for therapy in atrial fibrillation. Clinicians should be aware of pitfalls of the blood pressure measurement in atrial fibrillation. While the auscultatory method is preferred, the use of automated devices appears to be an acceptable method in the ambulatory setting. There are pathophysiologic bases and emerging clinical evidence suggesting the benefit of renin-angiotensin system inhibition in risk reduction of atrial fibrillation development particularly in patients with left ventricular hypertrophy or left ventricular dysfunction. A better understanding of hypertension’s pathophysiologic link to atrial fibrillation may lead to the development of novel therapies for the primary prevention of atrial fibrillation. Finally, future studies are needed to address optimal blood pressure goal to minimize the risk of atrial fibrillation-related complications.

Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography

Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing.

Direct Determination rather than Oscillometric Estimation of Systolic Blood Pressure in Patients with Severe Chronic Kidney Disease

<b><i>Introduction:</i></b> Although arterial hypertension is a major concern in patients with chronic kidney disease (CKD), obtaining accurate systolic blood pressure (SBP) measurement is challenging in this population for whom automatic oscillometric devices may yield erroneous results. <b><i>Methods:</i></b> This cross-sectional study was conducted in 89 patients with stages 4, 5, and 5D CKD, for whom we compared SBP values obtained by the recently described systolic foot-to-apex time interval (SFATI) technique which provides direct SBP determination, the standard technique (Korotkoff sounds), and oscillometry. We investigated the effects of age, sex, diabetes, CKD stage, and pulse pressure to explain measurement errors defined as biases or misclassification relative to the SBP thresholds of 110–130-mm Hg. <b><i>Results:</i></b> All 3 techniques showed satisfactory reproducibility for SBP measurement (CCC &#x3e; 0.84 and &#x3e;0.91, respectively, in dialyzed and nondialyzed patients). The mean ± SD from SBP as determined via Korotkoff sounds was 1.7 ± 4.6 mm Hg for SFATI (CCC = 0.98) and 5.9 ± 9.3 mm Hg for oscillometry (CCC = 0.88). Referring to the 110–130-mm Hg SBP range outside which treatment prescription or adaptation is recommended for CKD patients, SFATI underestimated SBP in 3 patients and overestimated it in 1, whereas oscillometry underestimated SBP in 12 patients and overestimated it in 3. Higher pulse pressure was the main explanatory factor for measurement and classification errors. <b><i>Discussion/Conclusion:</i></b> SFATI provides accurate SBP measurements in patients with severe CKD and paves the way for the standardization of automated noninvasive blood pressure measurement devices. Before prescribing or adjusting antihypertensive therapy, physicians should be aware of the risk of misclassification when using oscillometry.