When is an optimization not an optimization? Evaluation of clinical implications of information content (signal-to-noise ratio) in optimization of cardiac resynchronization therapy, and how to measure and maximize it

Impact of variability in the measured parameter is rarely considered in designing clinical protocols for optimization of atrioventricular (AV) or interventricular (VV) delay of cardiac resynchronization therapy (CRT). In this article, we approach this question quantitatively using mathematical simulation in which the true optimum is known and examine practical implications using some real measurements. We calculated the performance of any optimization process that selects the pacing setting which maximizes an underlying signal, such as flow or pressure, in the presence of overlying random variability (noise). If signal and noise are of equal size, for a 5-choice optimization (60, 100, 140, 180, 220 ms), replicate AV delay optima are rarely identical but rather scattered with a standard deviation of 45 ms. This scatter was overwhelmingly determined (ρ = −0.975, P < 0.001) by Information Content, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\frac{\text{Signal}}{{{\text{Signal}} + {\text{Noise}}}}}$$\end{document}, an expression of signal-to-noise ratio. Averaging multiple replicates improves information content. In real clinical data, at resting, heart rate information content is often only 0.2–0.3; elevated pacing rates can raise information content above 0.5. Low information content (e.g. <0.5) causes gross overestimation of optimization-induced increment in VTI, high false-positive appearance of change in optimum between visits and very wide confidence intervals of individual patient optimum. AV and VV optimization by selecting the setting showing maximum cardiac function can only be accurate if information content is high. Simple steps to reduce noise such as averaging multiple replicates, or to increase signal such as increasing heart rate, can improve information content, and therefore viability, of any optimization process

Chaotic Signatures of Heart Rate Variability and Its Power Spectrum in Health, Aging and Heart Failure

A paradox regarding the classic power spectral analysis of heart rate variability (HRV) is whether the characteristic high- (HF) and low-frequency (LF) spectral peaks represent stochastic or chaotic phenomena. Resolution of this fundamental issue is key to unraveling the mechanisms of HRV, which is critical to its proper use as a noninvasive marker for cardiac mortality risk assessment and stratification in congestive heart failure (CHF) and other cardiac dysfunctions. However, conventional techniques of nonlinear time series analysis generally lack sufficient sensitivity, specificity and robustness to discriminate chaos from random noise, much less quantify the chaos level. Here, we apply a ‘litmus test’ for heartbeat chaos based on a novel noise titration assay which affords a robust, specific, time-resolved and quantitative measure of the relative chaos level. Noise titration of running short-segment Holter tachograms from healthy subjects revealed circadian-dependent (or sleep/wake-dependent) heartbeat chaos that was linked to the HF component (respiratory sinus arrhythmia). The relative ‘HF chaos’ levels were similar in young and elderly subjects despite proportional age-related decreases in HF and LF power. In contrast, the near-regular heartbeat in CHF patients was primarily nonchaotic except punctuated by undetected ectopic beats and other abnormal beats, causing transient chaos. Such profound circadian-, age- and CHF-dependent changes in the chaotic and spectral characteristics of HRV were accompanied by little changes in approximate entropy, a measure of signal irregularity. The salient chaotic signatures of HRV in these subject groups reveal distinct autonomic, cardiac, respiratory and circadian/sleep-wake mechanisms that distinguish health and aging from CHF