The Effect of Beta-Blocker Therapy on Heart Failure with Preserved Ejection Fraction

Abstract

Heart rate variability (HRV) measures calculated from the beat-to-beat interval in the Electrocardiogram (ECG) recordings are promising markers of cardiovascular disease. Sympathetic activation and parasympathetic withdrawal, which cause an aberrant autonomic regulation, are symptoms of heart failure. Beta-blockers (BB) are prescribed for heart failure patients with low ejection fraction because they prevent overstimulation of the sympathetic nervous system. Uncertainty exists regarding beta-blocker therapy's impact on heart failure with preserved ejection fraction (HFpEF). This study looked examined how BB treatment affected heart rate variability (HRV) characteristics as a predictor of the likelihood of an abnormal cardiac event. 73 patients with HFpEF > 55% whose 24-hour ECGs were collected. There were 56 patients in the BB group and 17 patients in the group without BB. HRV can be quantitatively assessed using HRV measurements, although the analysis may be tainted by the existence of artifacts. HRV can be quantitatively assessed using HRV measurements, although the analysis may be tainted by the existence of artifacts. The ECG signal contains two different types of noise: physiological and technical abnormalities. A novel pre-processing approach for removing noise for HRV analysis using a hybrid two-step method targeting each type of noise separately is described. For 6% of additional ectopic beats and 6dB Gaussian noise, the two-step pre-processing tool's performance displayed a strong correlation coefficient of 0.846 and RMSE value of 7.69 x 10-5. The outcome demonstrates that at times associated with high cardiac risk, AVNN, RMSSD, HF power, VLF power, Sample entropy, and the novel fragmentation metrics were significantly different between the two groups. Depending on the characteristic being studied, BB therapy enhances HRV measurements in the HFpEF group, indicating a general lowered risk of a cardiac event and a potentially positive effect of BBs, particularly in the morning when there is a sympathetic surge. The potential of HRV measurements for phenotyping HFpEF using unsupervised machine learning methods was also demonstrated in an exploratory study.

Date of Award	Dec 2022
Original language	American English
Supervisor	Herbert Jelinek (Supervisor)