Smartphone-Based Remote Monitoring in Heart Failure With Reduced Ejection Fraction: Retrospective Cohort Study of Secondary Care Use and Costs.

BACKGROUND: Despite effective therapies, the economic burden of heart failure with reduced ejection fraction (HFrEF) is driven by frequent hospitalizations. Treatment optimization and admission avoidance rely on frequent symptom reviews and monitoring of vital signs. Remote monitoring (RM) aims to prevent admissions by facilitating early intervention, but the impact of noninvasive, smartphone-based RM of vital signs on secondary health care use and costs in the months after a new diagnosis of HFrEF is unknown. OBJECTIVE: The purpose of this study is to conduct a secondary care health use and health-economic evaluation for patients with HFrEF using smartphone-based noninvasive RM and compare it with matched controls receiving usual care without RM. METHODS: We conducted a retrospective study of 2 cohorts of newly diagnosed HFrEF patients, matched 1:1 for demographics, socioeconomic status, comorbidities, and HFrEF severity. They are (1) the RM group, with patients using the RM platform for >3 months and (2) the control group, with patients referred before RM was available who received usual heart failure care without RM. Emergency department (ED) attendance, hospital admissions, outpatient use, and the associated costs of this secondary care activity were extracted from the Discover data set for a 3-month period after diagnosis. Platform costs were added for the RM group. Secondary health care use and costs were analyzed using Kaplan-Meier event analysis and Cox proportional hazards modeling. RESULTS: A total of 146 patients (mean age 63 years; 42/146, 29% female) were included (73 in each group). The groups were well-matched for all baseline characteristics except hypertension (P=.03). RM was associated with a lower hazard of ED attendance (hazard ratio [HR] 0.43; P=.02) and unplanned admissions (HR 0.26; P=.02). There were no differences in elective admissions (HR 1.03, P=.96) or outpatient use (HR 1.40; P=.18) between the 2 groups. These differences were sustained by a univariate model controlling for hypertension. Over a 3-month period, secondary health care costs were approximately 4-fold lower in the RM group than the control group, despite the additional cost of RM itself (mean cost per patient GBP £465, US $581 vs GBP £1850, US $2313, respectively; P=.04). CONCLUSIONS: This retrospective cohort study shows that smartphone-based RM of vital signs is feasible for HFrEF. This type of RM was associated with an approximately 2-fold reduction in ED attendance and a 4-fold reduction in emergency admissions over just 3 months after a new diagnosis with HFrEF. Costs were significantly lower in the RM group without increasing outpatient demand. This type of RM could be adjunctive to standard care to reduce admissions, enabling other resources to help patients unable to use RM.

Re-emphasising the importance of histopathological diagnosis in suspected bacterial endocarditis.

Infective endocarditis (IE) carries a high risk of morbidity and mortality. Timely diagnosis, effective treatment and prompt recognition of complications are essential to favourable patient outcomes. A collaborative, multidisciplinary team approach to the management of IE has been shown to improve prognosis. However, the clinical heterogeneity of IE and atypical presentations pose challenges to the endocarditis team. We present a case highlighting the role of valve histopathology in suspected IE, where there may be diagnostic uncertainty.

Restricted Use of Echocardiography in Suspected Endocarditis during COVID-19 Lockdown: A Multidisciplinary Team Approach.

BACKGROUND: Infective endocarditis (IE) is challenging to manage in the COVID-19 lockdown period, in part given its reliance on echocardiography for diagnosis and management and the associated virus transmission risks to patients and healthcare workers. This study assesses utilisation of the endocarditis team (ET) in limiting routine echocardiography, especially transoesophageal echocardiography (TOE), in patients with suspected IE, and explores the effect on clinical outcomes. METHODS: All patients discussed at the ET meeting at Imperial College Healthcare NHS Trust during the first lockdown in the UK (23 March to 8 July 2020) were prospectively included and analysed in this observational study. RESULTS: In total, 38 patients were referred for ET review (71% male, median age 54 [interquartile range 48, 65.5] years). At the time of ET discussion, 21% had no echo imaging, 16% had point-of-care ultrasound only, and 63% had formal TTE. In total, only 16% underwent TOE. The ability of echocardiography, in those where it was performed, to affect IE diagnosis according to the Modified Duke Criteria was significant (p=0.0099); however, sensitivity was not affected. All-cause mortality was 17% at 30 days and 25% at 12 months from ET discussion in those with confirmed IE. CONCLUSION: Limiting echocardiography in patients with a low pretest probability (not probable or definite IE according to the Modified Duke Criteria) did not affect the diagnostic ability of the Modified Duke Criteria to rule out IE in this small study. Moreover, restricting nonessential echocardiography, and importantly TOE, in patients with suspected IE through use of the ET did not impact all-cause mortality.

The effect of concomitant COVID-19 infection on outcomes in patients hospitalized with heart failure.

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.

Artificial Intelligence, Data Sensors and Interconnectivity: Future Opportunities for Heart Failure.

A higher proportion of patients with heart failure have benefitted from a wide and expanding variety of sensor-enabled implantable devices than any other patient group. These patients can now also take advantage of the ever-increasing availability and affordability of consumer electronics. Wearable, on- and near-body sensor technologies, much like implantable devices, generate massive amounts of data. The connectivity of all these devices has created opportunities for pooling data from multiple sensors - so-called interconnectivity - and for artificial intelligence to provide new diagnostic, triage, risk-stratification and disease management insights for the delivery of better, more personalised and cost-effective healthcare. Artificial intelligence is also bringing important and previously inaccessible insights from our conventional cardiac investigations. The aim of this article is to review the convergence of artificial intelligence, sensor technologies and interconnectivity and the way in which this combination is set to change the care of patients with heart failure.

Distinct impacts of heart rate and right atrial-pacing on left atrial mechanical activation and optimal AV delay in CRT.

BACKGROUND: Controversy 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. METHODS: Twenty patients were hemodynamically optimized using noninvasive beat-to-beat blood pressure at rest (62 ± 11 beats/min), during exercise (80 ± 6 beats/min), and at three atrially paced rates: 5, 25, and 45 beats/min above rest, denoted as Apaced,r+5 , Apaced,r+25 , and Apaced,r+45 , respectively. Left atrial myocardial motion and transmitral flow were timed echocardiographically. RESULTS: During atrial sensing, raising heart rate shortened optimal AV delay by 25 ± 6 ms (P < 0.001). During atrial pacing, raising heart rate from Apaced,r+5 to Apaced,r+25 shortened it by 16 ± 6 ms; Apaced,r+45 shortened it 17 ± 6 ms further (P < 0.001). In comparison to atrial-sensed activation, atrial pacing lengthened optimal AV delay by 76 ± 6 ms (P < 0.0001) at rest, and at ∼20 beats/min faster, by 85 ± 7 ms (P < 0.0001), 9 ± 4 ms more (P  =  0.017). Mechanically, atrial pacing delayed left atrial contraction by 63 ± 5 ms at rest and by 73 ± 5 ms (i.e., by 10 ± 5 ms more, P < 0.05) at ∼20 beats/min faster. Raising atrial rate by exercise advanced left atrial contraction by 7 ± 2 ms (P  =  0.001). Raising it by atrial pacing did not (P  =  0.2). CONCLUSIONS: Hemodynamic 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.

Cardiac resynchronization therapy and AV optimization increase myocardial oxygen consumption, but increase cardiac function more than proportionally.

BACKGROUND: The mechanoenergetic effects of atrioventricular delay optimization during biventricular pacing ("cardiac resynchronization therapy", CRT) are unknown. METHODS: Eleven patients with heart failure and left bundle branch block (LBBB) underwent invasive measurements of left ventricular (LV) developed pressure, aortic flow velocity-time-integral (VTI) and myocardial oxygen consumption (MVO2) at 4 pacing states: biventricular pacing (with VV 0 ms) at AVD 40 ms (AV-40), AVD 120 ms (AV-120, a common nominal AV delay), at their pre-identified individualised haemodynamic optimum (AV-Opt); and intrinsic conduction (LBBB). RESULTS: AV-120, relative to LBBB, increased LV developed pressure by a mean of 11(SEM 2)%, p=0.001, and aortic VTI by 11(SEM 3)%, p=0.002, but also increased MVO2 by 11(SEM 5)%, p=0.04. AV-Opt further increased LV developed pressure by a mean of 2(SEM 1)%, p=0.035 and aortic VTI by 4(SEM 1)%, p=0.017. MVO2 trended further up by 7(SEM 5)%, p=0.22. Mechanoenergetics at AV-40 were no different from LBBB. The 4 states lay on a straight line for Δexternal work (ΔLV developed pressure × Δaortic VTI) against ΔMVO2, with slope 1.80, significantly >1 (p=0.02). CONCLUSIONS: Biventricular pacing and atrioventricular delay optimization increased external cardiac work done but also myocardial oxygen consumption. Nevertheless, the increase in cardiac work was ~80% greater than the increase in oxygen consumption, signifying an improvement in cardiac mechanoenergetics. Finally, the incremental effect of optimization on external work was approximately one-third beyond that of nominal AV pacing, along the same favourable efficiency trajectory, suggesting that AV delay dominates the biventricular pacing effect - which may therefore not be mainly "resynchronization".

A novel fully automated method for mitral regurgitant orifice area quantification.

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.

Evidence-based recommendations for PISA measurements in mitral regurgitation: systematic review, clinical and in-vitro study.

BACKGROUND: Guidelines for quantifying mitral regurgitation (MR) using "proximal isovelocity surface area" (PISA) instruct operators to measure the PISA radius from valve orifice to Doppler flow convergence "hemisphere". Using clinical data and a physically-constructed MR model we (A) analyse the actually-observed colour Doppler PISA shape and (B) test whether instructions to measure a "hemisphere" are helpful. METHODS AND RESULTS: In part A, the true shape of PISA shells was investigated using three separate approaches. First, a systematic review of published examples consistently showed non-hemispherical, "urchinoid" shapes. Second, our clinical data confirmed that the Doppler-visualized surface is non-hemispherical. Third, in-vitro experiments showed that round orifices never produce a colour Doppler hemisphere. In part B, six observers were instructed to measure hemisphere radius rh and (on a second viewing) urchinoid distance (du) in 11 clinical PISA datasets; 6 established experts also measured PISA distance as the gold standard. rh measurements, generated using the hemisphere instruction significantly underestimated expert values (-28%, p<0.0005), meaning r(h)(2) was underestimated by approximately 2-fold. du measurements, generated using the non-hemisphere instruction were less biased (+7%, p=0.03). Finally, frame-to-frame variability in PISA distance was found to have a coefficient of variation (CV) of 25% in patients and 9% in in-vitro data. Beat-to-beat variability had a CV of 15% in patients. CONCLUSIONS: Doppler-visualized PISA shells are not hemispherical: we should avoid advising observers to measure a hemispherical radius because it encourages underestimation of orifice area by approximately two-fold. If precision is needed (e.g. to detect changes reliably) multi-frame averaging is essential.

A systematic approach to designing reliable VV optimization methodology: assessment of internal validity of echocardiographic, electrocardiographic and haemodynamic optimization of cardiac resynchronization therapy.

BACKGROUND: In 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 RESULTS: Twenty 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 10 ms for pressure, versus 8 ms (p=ns) for QRS and 34 ms (p<0.01) for flow. Singularity of optimum was 85% for pressure, 63% for ECG and 45% for flow (Chi(2)=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 9 ms, no different from the reproducibility SDD at both heart rates. CONCLUSIONS: Using 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.

The limit of plausibility for predictors of response: application to biventricular pacing.

OBJECTIVES: We sought a method for any reader to quantify the limit, imposed by variability, to sustainably observable R(2) between any baseline predictor and response marker. We then apply this to echocardiographic measurements of mechanical dyssynchrony and response. BACKGROUND: Can mechanical dyssynchrony markers strongly predict ventricular remodeling by biventricular pacing (cardiac resynchronization therapy)? METHODS: First, we established the mathematical depression of observable R(2) arising from: 1) spontaneous variability of response markers; and 2) test-retest variability of dyssynchrony measurements. Second, we contrasted published R(2) values between externally monitored randomized controlled trials and highly skilled single-center studies (HSSCSs). RESULTS: Inherent variability of response markers causes a contraction factor in R(2) of 0.48 (change in left ventricular ejection fraction [ΔLVEF]), 0.50 (change in end-systolic volume [ΔESV]), and 0.40 (change in end-diastolic volume [ΔEDV]). Simultaneously, inherent variability of mechanical dyssynchrony markers causes a contraction factor of between 0.16 and 0.92 (average, 0.6). Therefore the combined contraction factor, that is, limit on sustainably observable R(2) between mechanical dyssynchrony markers and response, is ~0.29 (ΔLVEF), ~0.24 (ΔESV), and ~0.30 (ΔEDV). Many R(2) values published in HSSCSs exceeded these mathematical limits; none in externally monitored trials did so. Overall, HSSCSs overestimate R(2) by 5- to 20-fold (p = 0.002). Absence of bias-resistance features in study design (formal enrollment and blinded measurements) was associated with more overstatement of R(2). CONCLUSIONS: 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.

Improvement in coronary blood flow velocity with acute biventricular pacing is predominantly due to an increase in a diastolic backward-travelling decompression (suction) wave.

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 65±12; ejection fraction 26±7%) with left bundle-branch block (LBBB; QRS duration 174±18 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%) (P=0.007), LV dP/dt(max) by 15% (10%-21%) (P=0.005), and (neg)dP/dt(max) by 17% (9%-22%) (P=0.005). The cumulative intensity of the diastolic backward decompression (suction) wave increased by 26% (18%-54%) (P=0.005). The majority of the increase in coronary flow velocity time integral occurred in diastole (69% [41%-84% ]; P=0.047). The systolic compression waves also increased: forward by 36% (6%-49%) (P=0.022) and backward by 38% (20%-55%) (P=0.022). Biventricular pacing at AV delays of 120 ms generated a smaller LV dP/dt(max) (by 12% [5%-23% ], P=0.013) and (neg)dP/dt(max) (by 15% [8%-40% ]; P=0.009) 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.

Fully automatable, reproducible, noninvasive simple plethysmographic optimization: proof of concept and potential for implantability.

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."

Cardiac resynchronization therapy is certainly cardiac therapy, but how much resynchronization and how much atrioventricular delay optimization?

Cardiac resynchronization therapy has become a standard therapy for patients who are refractory to optimal medical therapy and fulfill the criteria of QRS >120 ms, ejection fraction <35% and NYHA class II, III or IV. Unless there is some other heretofore unrecognized effect of pacing, the benefits of atrio-biventricular pacing on hard outcomes observed in randomized trials can only be attributed to the physiological changes it induces such as increases in cardiac output and/or reduction in myocardial oxygen consumption leading to an improvement in cardiac function efficiency. The term "Cardiac Resynchronization Therapy" for biventricular pacing presupposes that restoration of synchrony (simultaneity of timing) between left and right ventricles and/or between walls of the left ventricle is the mechanism of benefit. But could a substantial proportion of these benefits arise not from ventricular resynchronization but from favorable shortening of AV delay ("AV optimization") which cannot be termed "resynchronization" unless the meaning of the word is stretched to cover any change in timing, thus, rendering the word almost meaningless. Here, we examine the evidence on the relative balance of resynchronization and AV delay shortening as contributors to the undoubted clinical efficacy of CRT.

The acute effects of changes to AV delay on BP and stroke volume: potential implications for design of pacemaker optimization protocols.

BACKGROUND: The AV delay optimization of biventricular pacemakers (cardiac resynchronization therapy) may maximize hemodynamic benefit but consumes specialist time to conduct echocardiographically. Noninvasive BP monitoring is a potentially automatable alternative, but it is unknown whether it gives the same information and similar precision (signal/noise ratio). Moreover, the immediate BP increment on optimization has been reported to decay away: it is unclear whether this is the result of an (undesirable) decrease in stroke volume or a (desirable) compensatory relief of peripheral vasoconstriction. METHODS AND RESULTS: To discriminate between these alternative mechanisms, we measured simultaneous beat-to-beat stroke volume (flow) using Doppler echocardiography, and BP using finger photoplethysmography, during and after AV delay changes from 40 to 120 ms in 19 subjects with cardiac pacemakers. BP and stroke volume both increased immediately (P<0.001, within 1 heartbeat). BP showed a clear decline a few seconds later (average rate, -0.65 mm Hg/beat; r=0.95 [95% CI, 0.86-0.98]); in contrast, stroke volume did not decline (P=0.87). The immediate BP increment correlated strongly with the stroke volume increment (r=0.74, P<0.001). The signal/noise ratio was 3-fold better for BP than stroke volume (6.8±3.5 versus 2.3±1.4; P<0.001). CONCLUSIONS: Improving AV delay immediately increases BP, but the effect begins to decay within a few seconds. Reassuringly, this is because of compensatory vasodilatation rather than reduction in cardiac function. Pacemaker optimization will never be reliable unless there is an adequate signal/noise ratio. Using BP rather than Doppler minimizes noise. The early phase (before vascular compensation) has the richest signal lode.

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, [Formula: see text], 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.