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.

New horizons in cardiogeriatrics: geriatricians and heart failure care-the custard in the tart, not the icing on the cake.

Heart failure (HF) can be considered a disease of older people. It is a leading cause of hospitalisation and is associated with high rates of morbidity and mortality in the over-65s. In 2012, an editorial in this journal detailed the latest HF research and guidelines, calling for greater integration of geriatricians in HF care. This current article reflects upon what has been achieved in this field in recent years, highlighting some future challenges and promising areas. It is written from the perspective of one such integrated team and explores the new role of cardiogeriatrician, working in a multidisciplinary team to deliver and improve care to increasingly complex, older, frail patients with multiple comorbidities who present with primary cardiology problems, especially decompensated HF. Geriatric liaison has improved the care of frail patients in orthopaedics, cancer services, stroke, acute medicine and numerous community settings. We propose that this vital role should now be extended to cardiology teams in general and to HF in particular.

COVID-19 and its impact on the cardiovascular system.

OBJECTIVES: The clinical impact of SARS-CoV-2 has varied across countries with varying cardiovascular manifestations. We review the cardiac presentations, in-hospital outcomes and development of cardiovascular complications in the initial cohort of SARS-CoV-2 positive patients at Imperial College Healthcare National Health Service Trust, UK. METHODS: We retrospectively analysed 498 COVID-19 positive adult admissions to our institute from 7 March to 7 April 2020. Patient data were collected for baseline demographics, comorbidities and in-hospital outcomes, especially relating to cardiovascular intervention. RESULTS: Mean age was 67.4±16.1 years and 62.2% (n=310) were male. 64.1% (n=319) of our cohort had underlying cardiovascular disease (CVD) with 53.4% (n=266) having hypertension. 43.2%(n=215) developed acute myocardial injury. Mortality was significantly increased in those patients with myocardial injury (47.4% vs 18.4%, p<0.001). Only four COVID-19 patients had invasive coronary angiography, two underwent percutaneous coronary intervention and one required a permanent pacemaker implantation. 7.0% (n=35) of patients had an inpatient echocardiogram. Acute myocardial injury (OR 2.39, 95% CI 1.31 to 4.40, p=0.005) and history of hypertension (OR 1.88, 95% CI 1.01 to 3.55, p=0.049) approximately doubled the odds of in-hospital mortality in patients admitted with COVID-19 after other variables had been controlled for. CONCLUSION: Hypertension, pre-existing CVD and acute myocardial injury were associated with increased in-hospital mortality in our cohort of COVID-19 patients. However, only a low number of patients required invasive cardiac intervention.

Urinary drug metabolite testing in chronic heart failure patients indicates high levels of adherence with life-prolonging therapies.

AIMS: Despite medical therapy for heart failure (HF) having proven benefits of improving quality of life and survival, many patients remain under-treated. This may be due to a combination of under-prescription by medical professionals and poor adherence from patients. In HF, as with many other chronic diseases, adherence to medication can deteriorate over time particularly when symptoms are well controlled. Therefore, detecting and addressing non-adherence has a crucial role in the management of HF. Significant flaws and inaccuracies exist in the methods currently used to assess adherence such as patient reporting, pill counts, and pharmacy fill records. We aim to use high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS) to detect metabolites of HF medications in the urine samples of chronic HF patients. METHODS AND RESULTS: Urine samples were collected from 35 patients in a specialist HF clinic. Patients were included if they had an ejection fraction <45% and were taking at least two disease-modifying HF medications. They were excluded if they had been admitted to hospital for HF in the 3 months preceding clinic attendance. These samples were sent for HPLC-MS and tested for all HF medications prescribed for that patient. A high rate of complete adherence of 89% was detected in these patients, with 94% being partially adherent (at least one HF medication detected) to therapy (at least one HF medication detected). This analysis also highlighted that mineralocorticoid antagonists represent both the most under-prescribed (67%) and poorly adhered (75%) medication class. CONCLUSIONS: This analysis revealed a surprisingly high level of adherence to disease-modifying therapy in chronic HF patients and highlights that most of our 'total' under-treatment is likely to be from a failure to prescribe rather than a failure to adhere. Testing for metabolites of disease-modifying HF drugs in urine using HPLC-MS is feasible and is a useful adjunct to a specialist HF service. At present, the distinction between treatment failure and failure to take treatment is not always clear, which is important because the investigation and potential solutions are different. The former needs initiation of additional therapies and consideration of additional diagnoses, whereas the latter requires strategies to understand reasons underlying poor adherence and collaborative working to improve this: the wrong strategy will be ineffective.

Adapting the role of handheld echocardiography during the COVID-19 pandemic: A practical guide.

The COVID-19 pandemic has altered our approach to inpatient echocardiography delivery. There is now a greater focus to address key clinical questions likely to make an immediate impact in management, particularly during the period of widespread infection. Handheld echocardiography (HHE) can be used as a first-line assessment tool, limiting scanning time and exposure to high viral load. This article describes a potential role for HHE during a pandemic. We propose a protocol with a reporting template for a focused core dataset necessary in delivering an acute echocardiography service in the setting of a highly contagious disease, minimising risk to the operator. We cover the scenarios typically encountered in the acute cardiology setting and how an expert trained echocardiography team can identify such pathologies using a limited imaging format and include cardiac presentations encountered in those patients acutely unwell with COVID-19.

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.

Improving haemodynamic optimization of cardiac resynchronization therapy for heart failure.

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.

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 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.

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.

Multinational evaluation of the interpretability of the iterative method of optimisation of AV delay for CRT.

BACKGROUND: AV delay optimisation of biventricular pacing devices (cardiac resynchronisation therapy, CRT) is performed in trials and recommended by current guidelines. The Doppler echocardiographic iterative method is the most commonly recommended. Yet whether it can be executed reliably has never been tested formally. METHODS: 36 multinational specialists, familiar with using the echocardiographic iterative method of CRT optimisation, were shown 20-40 sets of transmitral Doppler traces at 6-8 AV settings and asked to select the optimal AV delay. Unknown to the specialists, some Doppler datasets appeared in duplicate, allowing assessment of both inter and intra-specialist interpretation. RESULTS: On the Kappa scale of agreement (1 = perfect agreement, 0 = chance alone), the agreement regarding optimal AV delay between specialists was poor (kappa=0.12 ± 0.08). More importantly, agreement of specialists with themselves (i.e. viewing identical sets of traces, twice) was also poor, with Kappa=0.23 ± 0.07 and mean absolute difference in optimum AV delay of 83 ms between first and second viewing of the same traces. CONCLUSION: Iterative AV optimisation is not executed reliably by experts, even in an artificially simplified context where biological variability and variation in image acquisition are eliminated by use of identical traces. This cannot be blamed on insufficient skills of some experts or discordant methods of selecting the optimum, because operators also showed poor agreement with themselves when assessing the same trace. Instead, guidelines should retract any recommendation for this algorithm. Guideline-development processes might usefully begin with a rudimentary check on proposed algorithms, to establish at least minimal credibility.

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.

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.