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.

Background-Normal coronary blood flow is principally determined by a backward-traveling decompression (suction) wave in diastole. Dyssynchronous chronic heart failure may attenuate suction, because regional relaxation and contraction overlap in timing. We hypothesized that biventricular pacing, by restoring left ventricular (LV) synchronization and improving LV relaxation, might increase this suction wave, improving coronary flow. Method and Results-Ten patients with chronic heart failure (9 males; age 65Ϯ12; ejection fraction 26Ϯ7%) with left bundle-branch block (LBBB; QRS duration 174Ϯ18 ms) were atriobiventricularly paced at 100 bpm. LV pressure was measured and wave intensity calculated from invasive coronary flow velocity and pressure, with native conduction (LBBB) and during biventricular pacing at atrioventricular (AV) delays of 40 ms, 120 ms, and separately preidentified hemodynamically optimal AV delay. In comparison with LBBB, biventricular pacing at separately preidentified hemodynamically optimal AV delay (BiV-Opt) enhanced coronary flow velocity time integral by 15% (7%-25%) (Pϭ0.007), LV dP/dt max by 15% (10%-21%) (Pϭ0.005), and neg dP/dt max by 17% (9%-22%) (Pϭ0.005). The cumulative intensity of the diastolic backward decompression (suction) wave increased by 26% (18%-54%) (Pϭ0.005). The majority of the increase in coronary flow velocity time integral occurred in diastole (69% [41%-84% ]; Pϭ0.047).The systolic compression waves also increased: forward by 36% (6%-49%) (Pϭ0.022) and backward by 38% (20%-55%) (Pϭ0.022). Biventricular pacing at AV delays of 120 ms generated a smaller LV dP/dt max (by 12% [5%-23% ], Pϭ0.013) and neg dP/dt max (by 15% [8%-40% ]; Pϭ0.009) increase than BiV-Opt, against LBBB as reference; BiV-Opt and biventricular pacing at AV delays of 120 ms were not significantly different in coronary flow velocity time integral or waves. Biventricular pacing at AV delays of 40 ms was no different from LBBB. Conclusions-When biventricular pacing improves LV contraction and relaxation, it increases coronary blood flow velocity, predominantly by increasing the dominant diastolic backward decompression (suction) wave. (Circulation. 2012;126:1334-1344.)

Aims Patients with cardiovascular disease appear particularly susceptible to severe COVID‐19 disease, but the impact of COVID‐19 infection on patients with heart failure (HF) is not known. This study aimed to quantify the impact of COVID‐19 infection on mortality in hospitalized patients known to have HF. Methods and results We undertook a retrospective analysis of all patients admitted with a pre‐existing diagnosis of HF between 1 March and 6 May 2020 to our unit. We assessed the impact of concomitant COVID‐19 infection on in‐hospital mortality, incidence of acute kidney injury, and myocardial injury. One hundred and thirty‐four HF patients were hospitalized, 40 (29.9%) with concomitant COVID‐19 infection. Those with COVID‐19 infection had a significantly increased in‐hospital mortality {50.0% vs. 10.6%; relative risk [RR] 4.70 [95% confidence interval (CI) 2.42–9.12], P < 0.001} and were more likely to develop acute kidney injury [45% vs. 24.5%; RR 1.84 (95% CI 1.12–3.01), P = 0.02], have evidence of myocardial injury [57.5% vs. 31.9%; RR 1.81 (95% CI 1.21–2.68), P < 0.01], and be treated for a superadded bacterial infection [55% vs. 32.5%; RR 1.67 (95% CI 1.12–2.49), P = 0.01]. Conclusions Patients with HF admitted to hospital with concomitant COVID‐19 infection have a very poor prognosis. This study highlights the need to regard patients with HF as a high‐risk group to be shielded to reduce the risks of COVID‐19 infection.

Reports of R(2) > 0.2 in response prediction arose exclusively from studies without formally documented enrollment and blinding. The HSSCS approach overestimates R(2) values, frequently breaching the mathematical ceiling on sustainably observable R(2), which is far below 1.0, and can easily be calculated by readers using formulas presented here. Community awareness of this low ceiling may help resist future claims. Reliable individualized response prediction, using methods originally designed for group-mean effects, may never be possible because it has 2 currently unavailable and perhaps impossible prerequisites: 1) excellent blinded test-retest reproducibility of dyssynchrony; and 2) response markers reproducible over time within nonintervened individuals. Dispassionate evaluation, and improvement, of test-retest reproducibility is required before any further claims of strong prediction. Prediction studies should be designed to resist bias.