Objective: Optimization of cardiac resynchronization therapy using non-invasive haemodynamic parameters produces reliable optima when performed at high atrial paced heart rates. Here we investigate whether this is a result of increased heart rate or atrial pacing itself. Approach: Forty-three patients with cardiac resynchronization therapy underwent haemodynamic optimization of atrioventricular (AV) delay using non-invasive beat-to-beat systolic blood pressure in three states: rest (atrial-sensing, 66  ±  11 bpm), slow atrial pacing (73  ±  12 bpm), and fast atrial pacing (94  ±  10 bpm). A 20-patient subset underwent a fourth optimization, during exercise (80  ±  11 bpm). Main results: Intraclass correlation coefficient (ICC, quantifying information content mean  ±SE) was 0.20  ±  0.02 for resting sensed optimization, 0.45  ±  0.03 for slow atrial pacing (p   <  0.0001 versus rest-sensed), and 0.52  ±  0.03 for fast atrial pacing (p   =  0.12 versus slow-paced). 78% of the increase in ICC, from sinus rhythm to fast atrial pacing, is achieved by simply atrially pacing just above sinus rate. Atrial pacing increased signal (blood pressure difference between best and worst AV delay) from 6.5  ±  0.6 mmHg at rest to 13.3  ±  1.1 mmHg during slow atrial pacing (p   <  0.0001) and 17.2  ±  1.3 mmHg during fast atrial pacing (p   =  0.003 versus slow atrial pacing). Atrial pacing reduced noise (average SD of systolic blood pressure measurements) from 4.9  ±  0.4 mmHg at rest to 4.1  ±  0.3 mmHg during slow atrial pacing (p   =  0.28). At faster atrial pacing the noise was 4.6  ±  0.3 mmHg (p   =  0.69 versus slow-paced, p   =  0.90 versus rest-sensed). In the exercise subgroup ICC was 0.14  ±  0.02 (p   =  0.97 versus rest-sensed). Significance: Atrial pacing, rather than the increase in heart rate, contributes to ~80% of the observed information content improvement from sinus rhythm to fast atrial pacing. This is predominantly through increase in measured signal.

BackgroundControversy exists regarding how atrial activation mode and heart rate affect optimal atrioventricular (AV) delay in cardiac resynchronization therapy. We studied these questions using high-reproducibility hemodynamic and echocardiographic measurements. MethodsTwenty patients were hemodynamically optimized using noninvasive beat-to-beat blood pressure at rest (62 11beats/min), during exercise (80 6beats/min), and at three atrially paced rates: 5, 25, and 45beats/min above rest, denoted as A(paced,r+5), A(paced,r+25), and A(paced,r+45), respectively. Left atrial myocardial motion and transmitral flow were timed echocardiographically. ResultsDuring atrial sensing, raising heart rate shortened optimal AV delay by 25 6ms (P<0.001). During atrial pacing, raising heart rate from A(paced,r+5) to A(paced,r+25) shortened it by 16 6ms; A(paced,r+45) shortened it 17 +/- 6ms further (P<0.001). In comparison to atrial-sensed activation, atrial pacing lengthened optimal AV delay by 76 +/- 6ms (P<0.0001) at rest, and at approximate to 20beats/min faster, by 85 +/- 7ms (P<0.0001), 9 +/- 4ms more (P=0.017). Mechanically, atrial pacing delayed left atrial contraction by 63 +/- 5ms at rest and by 73 +/- 5ms (i.e., by 10 +/- 5ms more, P<0.05) at approximate to 20beats/min faster. Raising atrial rate by exercise advanced left atrial contraction by 7 +/- 2ms (P=0.001). Raising it by atrial pacing did not (P=0.2). ConclusionsHemodynamic optimal AV delay shortens with elevation of heart rate. It lengthens on switching from atrial-sensed to atrial-paced at the same rate, and echocardiography shows this sensed-paced difference in optima results from a sensed-paced difference in atrial electromechanical delay. The reason for the widening of the sensed-paced difference in AV optimum may be physiological stimuli (e.g., adrenergic drive) advancing left atrial contraction during exercise but not with fast atrial pacing.

Rationale: In patients with chronic heart failure, daytime oscillatory breathing at rest is associated with a high risk of mortality.Experimental evidence, including exaggerated ventilatory responses to CO 2 and prolonged circulation time, implicates the ventilatory control system and suggests feedback instability (loop gain .1) is responsible.However, daytime oscillatory patterns often appear remarkably irregular versus classic instability (Cheyne-Stokes respiration), suggesting our mechanistic understanding is limited.Objectives: We propose that daytime ventilatory oscillations generally result from a chemoreflex resonance, in which spontaneous biological variations in ventilatory drive repeatedly induce temporary and irregular ringing effects.Importantly, the ease with which spontaneous biological variations induce irregular oscillations (resonance "strength") rises profoundly as loop gain rises toward 1.We tested this hypothesis through a comparison of mathematical predictions against actual measurements in patients with heart failure and healthy control subjects.Methods: In 25 patients with chronic heart failure and 25 control subjects, we examined spontaneous oscillations in ventilation and separately quantified loop gain using dynamic inspired CO 2 stimulation.Measurements and Main Results: Resonance was detected in 24 of 25 patients with heart failure and 18 of 25 control subjects.With increased loop gain-consequent to increased chemosensitivity and delay-the strength of spontaneous oscillations increased precipitously as predicted (r = 0.88), yielding larger (r = 0.78) and more regular (interpeak interval SD, r = 20.68)oscillations (P , 0.001 for all, both groups combined).Conclusions: Our study elucidates the mechanism underlying daytime ventilatory oscillations in heart failure and provides a means to measure and interpret these oscillations to reveal the underlying chemoreflex hypersensitivity and reduced stability that foretells mortality in this population.

Left ventricular function can be evaluated by qualitative grading and by eyeball estimation of ejection fraction (EF). We sought to define the reproducibility of these techniques, and how they are affected by image quality, experience and accreditation. Twenty apical four-chamber echocardiographic cine loops (Online Resource 1–20) of varying image quality and left ventricular function were anonymized and presented to 35 operators. Operators were asked to provide (1) a one-phrase grading of global systolic function (2) an “eyeball” EF estimate and (3) an image quality rating on a 0–100 visual analogue scale. Each observer viewed every loop twice unknowingly, a total of 1400 viewings. When grading LV function into five categories, an operator’s chance of agreement with another operator was 50 % and with themself on blinded re-presentation was 68 %. Blinded eyeball LVEF re-estimates by the same operator had standard deviation (SD) of difference of 7.6 EF units, with the SD across operators averaging 8.3 EF units. Image quality, defined as the average of all operators’ assessments, correlated with EF estimate variability (r = −0.616, p < 0.01) and visual grading agreement (r = 0.58, p < 0.01). However, operators’ own single quality assessments were not a useful forewarning of their estimate being an outlier, partly because individual quality assessments had poor within-operator reproducibility (SD of difference 17.8). Reproducibility of visual grading of LV function and LVEF estimation is dependent on image quality, but individuals cannot themselves identify when poor image quality is disrupting their LV function estimate. Clinicians should not assume that patients changing in grade or in visually estimated EF have had a genuine clinical change.

Background Haemodynamic optimisation of atrioventricular delay has higher information content under fast atrial pacing. This study tests whether it is the presence of atrial pacing or the elevation of heart rate that is important. Methods 43 patients with CRT underwent haemodynamic optimisation of AV delay using non-invasive beat-to-beat systolic blood pressure in three states: rest (A-sensing, 66 ± 11 bpm), slow atrial pacing (73 ± 11 bpm), and fast atrial pacing (92 ± 11 bpm). A 20-patient subset underwent a fourth optimisation, during exercise (80 ± 11 bpm). Results Intraclass correlation coefficient (ICC, quantifying information content) was 0.30 ± 0.07 for resting sensed optimisation, 0.73 ± 0.04 for slow atrial pacing (p < 0.0001 versus rest-sensed), and 0.82 ± 0.02 for fast atrial pacing (p = 0.03 versus slow-paced). 83% of the increase in ICC, from sinus rhythm to fast atrial pacing, is achieved by simply atrially pacing just above sinus rate. Atrial pacing increased signal (blood pressure difference between best and worst AV delay) from 6.5 ± 0.6 mmHg at rest to 13.3 ± 1.1 mmHg during slow atrial pacing (p < 0.0001) and 17.2 ± 1.3 mmHg during fast atrial pacing (p < 0.001 versus slow atrial pacing) (Figure 1). Atrial pacing reduced noise (average SEM of systolic blood pressure measurements) from 1.6 ± 0.1 mmHg at rest to 1.4 ± 0.1 mmHg during slow atrial pacing (p = 0.02). At faster atrial pacing the noise was 1.5 ± 0.1 mmHg (p = 0.2 versus slow-paced, p = 0.3 versus resting sensed). In the exercise subgroup ICC was 0.23 ± 0.19 (p = 1 versus rest-sensed). Conclusions Atrial pacing, rather than the increase in heart rate, contributes to ˜80% of the observed information content improvement from sinus rhythm to fast atrial pacing. This is predominantly through increase in measured signal.

Background Head-up tilt (HUT) is used for diagnosis of vasovagal syncope (VVS), and can provoke cardioinhibition. VVS is usually considered benign, however pacemaker insertion may be indicated in some patients. We sought to characterize the long-term outcomes of patients with prolonged asystole (>15 s) on HUT. Methods We conducted a retrospective study on patients with asystole >15 s on HUT identified from 5133 patients who were investigated between 1998 and 2012 at our institution. Patients were mailed questionnaires or telephoned to ascertain outcomes. Where contact was not possible, the patients' general practitioners were contacted to request up-to-date information. Results A total of 26 patients with a mean age of 45 ± 18 years and a mean duration of asystole on HUT of 26 ± 7 s were successfully followed up from a total of 77 patients identified. The follow-up duration was 99 ± 39 months. Six patients had undergone pacemaker (PPM) implantation. Of the patients without PPM, 16 reported spontaneously improved symptoms. Ten patients sustained injury prior to HUT compared with one after HUT, when a clear diagnosis was made and management advice was given. There were no major injuries or deaths after HUT. The 6 patients with PPMs had a mean age of 60 ± 16 (67% male) at HUT. Four patients had no further syncope after PPM and two demonstrated improvement but still experienced recurrent syncope. Conclusions Prolonged asystole (>15 s) on tilt does not necessarily predict adverse outcomes with most patients improving spontaneously over the long-term. Pacemaker insertion in selected patients may reduce syncope recurrence but does not always abolish it.

Abstract. Obtaining a “correct” view in echocardiography is a subjective process in which an operator attempts to obtain images conforming to consensus standard views. Real-time objective quantification of image alignment may assist less experienced operators, but no reliable index yet exists. We present a fully automated algorithm for detecting incorrect medial/lateral translation of an ultrasound probe by image analysis. The ability of the algorithm to distinguish optimal from sub-optimal four-chamber images was compared to that of specialists—the current “gold-standard.” The orientation assessments produced by the automated algorithm correlated well with consensus visual assessments of the specialists (r=0.87) and compared favourably with the correlation between individual specialists and the consensus, 0.82±0.09. Each individual specialist’s assessments were within the consensus of other specialists, 75±14% of the time, and the algorithm’s assessments were within the consensus of specialists 85% of the time. The mean discrepancy in probe translation values between individual specialists and their consensus was 0.97±0.87  cm, and between the automated algorithm and specialists’ consensus was 0.92±0.70  cm. This technology could be incorporated into hardware to provide real-time guidance for image optimisation—a potentially valuable tool both for training and quality control.

Background Constant flow and concentration CO2 has previously been efficacious in attenuating ventilatory oscillations in periodic breathing (PB) where oscillations in CO2 drive ventilatory oscillations. However, it has the undesirable effect of increasing end-tidal CO2, and ventilation. We tested, in a model of PB, a dynamic CO2 therapy that aims to attenuate pacemaker-induced ventilatory oscillations while minimising CO2 dose. Methods First, pacemakers were manipulated in 12 pacemaker recipients, 6 with heart failure (ejection fraction (EF)=23.7±7.3%) and 6 without heart failure, to experimentally induce PB. Second, we applied a real-time algorithm of pre-emptive dynamic exogenous CO2 administration, and tested different timings. Results We found that cardiac output alternation using pacemakers successfully induced PB. Dynamic CO2 therapy, when delivered coincident with hyperventilation, attenuated 57% of the experimentally induced oscillations in end-tidal CO2: SD/mean 0.06±0.01 untreated versus 0.04±0.01 with treatment (p<0.0001) and 0.02±0.01 in baseline non-modified breathing. This translated to a 56% reduction in induced ventilatory oscillations: SD/mean 0.19±0.09 untreated versus 0.14±0.06 with treatment (p=0.001) and 0.10±0.03 at baseline. Of note, end-tidal CO2 did not significantly rise when dynamic CO2 was applied to the model (4.84±0.47 vs 4.91± 0.45 kPa, p=0.08). Furthermore, mean ventilation was also not significantly increased by dynamic CO2 compared with untreated (7.8±1.2 vs 8.4±1.2 L/min, p=0.17). Conclusions Cardiac pacemaker manipulation can be used to induce PB experimentally. In this induced PB, delivering CO2 coincident with hyperventilation, ventilatory oscillations can be substantially attenuated without a significant increase in end-tidal CO2 or ventilation. Dynamic CO2 administration might be developed into a clinical treatment for PB. Trial Registration number ISRCTN29344450.

In clinical practice, echocardiographers are often unkeen to make the significant time investment to make additional multiple measurements of Doppler velocity. Main hurdle to obtaining multiple measurements is the time required to manually trace a series of Doppler traces. To make it easier to analyze more beats, we present the description of an application system for automated aortic Doppler envelope quantification, compatible with a range of hardware platforms. It analyses long Doppler strips, spanning many heartbeats, and does not require electrocardiogram to separate individual beats. We tested its measurement of velocity-time-integral and peak-velocity against the reference standard defined as the average of three experts who each made three separate measurements. The automated measurements of velocity-time-integral showed strong correspondence (R2 = 0.94) and good Bland-Altman agreement (SD = 1.39 cm) with the reference consensus expert values, and indeed performed as well as the individual experts ( R2 = 0.90 to 0.96, SD = 1.05 to 1.53 cm). The same performance was observed for peak-velocities; ( R2 = 0.98, SD = 3.07 cm/s) and ( R2 = 0.93 to 0.98, SD = 2.96 to 5.18 cm/s). This automated technology allows > 10 times as many beats to be analyzed compared to the conventional manual approach. This would make clinical and research protocols more precise for the same operator effort.

Some of the challenges with tissue Doppler measurement include: apparent inconsistency between manufacturers, uncertainty over which part of the trace to make measurements and a lack of calibration of measurements. We develop and test tools to solve these problems in echocardiography laboratories. We designed and constructed an actuator and phantom setup to produce automatic reproducible motion, and used it to compare velocities measured using 3 echocardiographic modalities: M-mode, speckle tracking, and tissue Doppler, against a non-ultrasound, optical gold standard. In the clinical phase, 25 patients underwent M-mode, speckle tracking and tissue Doppler measurements of tissue velocities. In-vitro, the M-mode and speckle tracking velocities were concordant with optical assessment. Of the three possible tissue Doppler measurement conventions (outer, middle and inner line) only the middle line agreed with the optical assessment (discrepancy -0.20 (95% confidence interval -0.44 to 0.03)cm/s, p=0.11, outer +5.19(4.65 to 5.73)cm/s, p<0.0001, inner -6.26(-6.87 to -5.65)cm/s, p<0.0001). All 4 studied manufacturers showed a similar pattern. M-mode was therefore chosen as the in-vivo gold standard. Clinical measurements of tissue velocities by speckle tracking and the middle line of the tissue Doppler were concordant with M-mode, while the outer line significantly overestimated (+1.27(0.96 to 1.59)cm/s, p<0.0001) and the inner line underestimated (-1.81(-2.11 to -1.52)cm/s, p<0.0001). Echocardiographic velocity measurements can be calibrated by simple, inexpensive tools. We found that the middle of the tissue Doppler trace represents velocity correctly. Echocardiographers requiring velocities to match between different equipment, settings or modalities should use the middle line as the “guideline”.

BACKGROUNDVariability has been described between different echo machines and different modalities when measuring tissue velocities. We assessed the consistency of tissue velocity measurements across different modalities and different manufacturers in an in vitro model and in patients. Furthermore, we present freely available software tools to repeat these evaluations.METHODS AND RESULTSWe constructed a simple setup to generate reproducible motion and used it to compare velocities measured using three echocardiographic modalities: M-mode, speckle tracking, and tissue Doppler, with a straightforward, non-ultrasound, optical gold standard. In the clinical phase, 25 patients underwent M-mode, speckle tracking, and tissue Doppler measurements of s', e', and a' velocities. In vitro, the M-mode and speckle tracking velocities agreed with optical assessment. Of the three possible tissue Doppler measurement conventions (outer, middle, and inner edge) only the middle agreed with optical assessment (discrepancy -0.20 (95% CI -0.44 to 0.03) cm/s, P = 0.11, outer +5.19 (4.65 to 5.73) cm/s, P < 0.0001, inner -6.26 (-6.87 to -5.65) cm/s, P < 0.0001). A similar pattern occurred across all four studied manufacturers. M-mode was therefore chosen as the in vivo gold standard. Clinical measurements of s' velocities by speckle tracking and the middle line of the tissue Doppler showed concordance with M-mode, while the outer line overestimated significantly (+1.27(0.96 to 1.59) cm/s, P < 0.0001) and the inner line underestimated (-1.82 (-2.11 to -1.52) cm/s, P < 0.0001).CONCLUSIONSEchocardiographic velocity measurements can be more consistent than previously suspected. The statistically modal velocity, found at the centre of the spectral pulsed wave tissue Doppler envelope, most closely represents true tissue velocity. This article includes downloadable, vendor-independent software enabling calibration of echocardiographic machines using a simple, inexpensive in vitro setup.

Echocardiographers are often unkeen to make the considerable time investment to make additional multiple measurements of Doppler velocity Main hurdle to obtaining multiple measurements is the time required to manually trace a series of Doppler traces. To make it easier to analyse more beats, we present an automated system for Doppler envelope quantification. It analyses long Doppler strips, spanning many heartbeats, and does not require the electrocardiogram to isolate individual beats. We tested its measurement of velocity-time-integral and peak-velocity against the reference standard defined as the average of three experts who each made three separate measurements. The automated measurements of velocity-time-integral showed strong correspondence (R-2 = 0.94) and good Bland-Altman agreement (SD = 6.92%) with the reference consensus expert values, and indeed performed as well as the individual experts (R-2 = 0.90 to 0.96, SD = 5.66% to 7.64%). The same performance was observed for peak-velocities; (R-2 = 0.98, SD = 2.95%) and (R-2 = 0.93 to 0.98, SD = 2.94% to 5.12%). This automated technology allows >10 times as many beats to be acquired and analysed compared to the conventional manual approach, with each beat maintaining its accuracy.

BACKGROUND:Effective regurgitant orifice area (EROA) in mitral regurgitation (MR) is difficult to quantify. Clinically it is measured using the proximal isovelocity surface area (PISA) method, which is intrinsically not automatable, because it requires the operator to manually identify the mitral valve orifice. We introduce a new fully automated algorithm, ("AQURO"), which calculates EROA directly from echocardiographic colour M-mode data, without requiring operator input. METHODS:Multiple PISA measurements were compared to multiple AQURO measurements in twenty patients with MR. For PISA analysis, three mutually blinded observers measured EROA from the four stored video loops. For AQURO analysis, the software automatically processed the colour M-mode datasets and analysed the velocity field in the flow-convergence zone to extract EROA directly without any requirement for manual radius measurement. RESULTS:Reproducibility, measured by intraclass correlation (ICC), for PISA was 0.80, 0.83 and 0.83 (for 3 observers respectively). Reproducibility for AQURO was 0.97. Agreement between replicate measurements calculated using Bland-Altman standard deviation of difference (SDD) was 21,17 and 17mm(2)for the three respective observers viewing independent video loops using PISA. Agreement between replicate measurements for AQURO was 6, 5 and 7mm(2)for automated analysis of the three pairs of datasets. CONCLUSIONS:By eliminating the need to identify the orifice location, AQURO avoids an important source of measurement variability. Compared with PISA, it also reduces the analysis time allowing analysis and averaging of data from significantly more beats, improving the consistency of EROA quantification. AQURO, being fully automated, is a simple, effective enhancement for EROA quantification using standard echocardiographic equipment.

BackgroundIn atrial fibrillation (AF), VV optimization of biventricular pacemakers can be examined in isolation. We used this approach to evaluate internal validity of three VV optimization methods by three criteria.Methods and resultsTwenty patients (16 men, age 75±7) in AF were optimized, at two paced heart rates, by LVOT VTI (flow), non-invasive arterial pressure, and ECG (minimizing QRS duration). Each optimization method was evaluated for: singularity (unique peak of function), reproducibility of optimum, and biological plausibility of the distribution of optima.The reproducibility (standard deviation of the difference, SDD) of the optimal VV delay was 10ms for pressure, versus 8ms (p=ns) for QRS and 34ms (p<0.01) for flow.Singularity of optimum was 85% for pressure, 63% for ECG and 45% for flow (Chi2=10.9, p<0.005).The distribution of pressure optima was biologically plausible, with 80% LV pre-excited (p=0.007). The distributions of ECG (55% LV pre-excitation) and flow (45% LV pre-excitation) optima were no different to random (p=ns).The pressure-derived optimal VV delay is unaffected by the paced rate: SDD between slow and fast heart rate is 9ms, no different from the reproducibility SDD at both heart rates.ConclusionsUsing non-invasive arterial pressure, VV delay optimization by parabolic fitting is achievable with good precision, satisfying all 3 criteria of internal validity. VV optimum is unaffected by heart rate. Neither QRS minimization nor LVOT VTI satisfy all validity criteria, and therefore seem weaker candidate modalities for VV optimization. AF, unlinking interventricular from atrioventricular delay, uniquely exposes resynchronization concepts to experimental scrutiny.

BackgroundReproducibility and hemodynamic efficacy of optimization of AV delay (AVD) of cardiac resynchronization therapy (CRT) using invasive LV dp/dtmax are unknown.Method and results25 patients underwent AV delay (AVD) optimisation twice, using continuous left ventricular (LV) dp/dtmax, systolic blood pressure (SBP) and pulse pressure (PP). We compared 4 protocols for comparing dp/dtmax between AV delays:Immediate absolute: mean of 10 s recording of dp/dtmax acquired immediately after programming the tested AVD,Delayed absolute: mean of 10 s recording acquired 30 s after programming AVD,Single relative: relative difference between reference AVD and the tested AVD,Multiple relative: averaged difference, from multiple alternations between reference and tested AVD.We assessed for dp/dtmax, LVSBP and LVPP, test–retest reproducibility of the optimum.Optimization using immediate absolute dp/dtmax had poor reproducibility (SDD of replicate optima=41 ms; R2=0.45) as did delayed absolute (SDD 39 ms; R2=0.50). Multiple relative had better reproducibility: SDD 23 ms, R2=0.76, and (p<0.01 by F test).Compared with AAI pacing, the hemodynamic increment from CRT, with the nominal AV delay was LVSBP 2% and LVdp/dtmax 5%, while CRT with pre-determined optimal AVD gave 6% and 9% respectively.ConclusionsBecause of inevitable background fluctuations, optimization by absolute dp/dtmax has poor same-day reproducibility, unsuitable for clinical or research purposes. Reproducibility is improved by comparing to a reference AVD and making multiple consecutive measurements. More than 6 measurements would be required for even more precise optimization — and might be advisable for future study designs. With optimal AVD, instead of nominal, the hemodynamic increment of CRT is approximately doubled.

Wave reflection is thought to be important in the augmentation of blood pressure. However, identification of distal reflections sites remains unclear. One possible explanation for this is that wave reflection is predominately determined by an amalgamation of multiple proximal small reflections rather than large discrete reflections originating from the distal peripheries. In 19 subjects (age, 35-73 years), sensor-tipped intra-arterial wires were used to measure pressure and Doppler velocity at 10-cm intervals along the aorta, starting at the aortic root. Incident and reflected waves were identified and timings and magnitudes quantified using wave intensity analysis. Mean wave speed increased along the length of the aorta (proximal, 6.8±0.9 m/s; distal, 10.7±1.5 m/s). The incident wave was tracked moving along the aorta, taking 55±4 ms to travel from the aortic root to the distal aorta. However, the timing to the refection site distance did not differ between proximal and distal aortic measurement sites (proximal aorta, 48±5 ms versus distal aorta, 42±4 ms; P=0.3). We performed a second analysis using aortic waveforms in a nonlinear model of pulse-wave propagation. This demonstrated very similar results to those observed in vivo and also an exponential attenuation in reflection magnitude. There is no single dominant refection site in or near the distal aorta. Rather, there are multiple reflection sites along the aorta, for which the contributions are attenuated with distance. We hypothesize that rereflection of reflected waves leads to wave entrapment, preventing distal waves being seen in the proximal aorta.

Background: Hemodynamic optimization of cardiac resynchronization therapy (CRT) can be achieved reproducibly and—with bulky, nonimplantable equipment—noninvasively. We explored whether a simple photoplethysmogram signal might be used instead. Method: Twenty patients (age 65 ± 12) with CRT underwent automatic atrioventricular (AV) delay optimization, using a multiple‐transitions protocol, at two atrially paced heart rates: just above sinus rate (“slow ApVp,” 77 ± 11 beats per minute [bpm]) and 100 bpm (“fast ApVp”). We then retested to assess short‐term reproducibility. Results: All 80 optimizations identified an optimum (correctly oriented parabola). At 100 bpm, the simple photoplethysmogram had wider scatter between repeat optimizations than did Finometer: standard deviation of difference (SDD) 22 ms versus 14 ms, respectively, P = 0.028. The simple photoplethysmogram improved in reproducibility when slope (instead of peak) of its signal was used for optimization, becoming as reproducible as Finometer (SDD 14 ms vs 14 ms, P = 0.50). At slow heart rate, reproducibility of simple photoplethysmogram‐based optimization worsened from 14 to 22 ms (P = 0.028), and Finometer‐based optimization from 14 to 26 ms (P = 0.005). Increasing the number of replicates averaged improved reproducibility. For example, SDD of simple photoplethysmogram optimization (using peak) fell from 62 ms with two replicates to 22 ms with eight replicates (P < 0.0001).At 100 bpm, the eight‐replicate protocol takes ∼12 minutes. Conclusions : A 12‐minute protocol of simple photoplethysmographic AV optimization can be processed fully automatically. Blinded test‐retest reproducibility of the optimum AV is good and improves with more replicates. If benefits to some patients are not to be neutralized by harm to others, endpoint studies should first test check narrowness of “within‐patient error bars.” (PACE 2012; 35:948–960)

Background: During optimization of the atrioventricular (AV) delay of cardiac resynchronization therapy (CRT), it is not known exactly which windows of time around the transition are most informative for identification of the optimum. Method and Results: In 22 patients with CRT, we performed AV delay optimization using continuous noninvasive hemodynamics. We used signal‐to‐noise ratio to determine the most efficient averaging window location and width. We found that it is most efficient to position the averaging windows immediately before and immediately after the transition in AV delay. For example, skipping five beats after the transition decreases signal‐to‐noise ratio by 17.5% (P < 0.0001). Similarly, skipping five beats immediately before the transition reduces signal‐to‐noise ratio by 11.7% (P < 0.0001). The best choice of “fixed” averaging window width was found to be six beats, with signal‐to‐noise ratio falling by, for example, 41% for a one‐beat window (P = 0.0002). However, even better was to set the window width for each patient to match one respiratory cycle. We observed that the pre‐ and posttransition signal‐to‐noise ratio traces begin to diverge three beats after the transition in AV delay. We believe this represents the time taken for the peripheral response to pacing‐induced changes in stroke volume to occur. Conclusions: The most efficient way to use alternating transitions for the hemodynamic optimization of CRT is to use an averaging window of one respiratory cycle, and not to skip any beats between the pretransition and posttransition averaging windows. (PACE 2011; 34:217–225)