Effect of Acoustic Cardiography-guided Management on 1-year Outcomes in Patients With Acute Heart Failure

Non-invasive heart failure monitoring: leveraging smart scales and digital biomarkers to improve heart failure outcomes

Heart failure (HF) is a significant global concern, impacting patient morbidity, mortality, and healthcare costs. Guideline-directed medical therapy and various preventive measures have proven effective in improving clinical outcomes and reducing HF hospitalizations. Recent data indicates that remote HF monitoring facilitates early detection of HF decompensation by observing upstream events and parameters before clinical signs and symptoms manifest. Moreover, these innovative devices have been shown to decrease unnecessary HF hospitalizations and, in some cases, provide predictive insights before an actual HF incident. In this review, we aim to explore the data regarding smart scales and digital biomarkers and summarize both FDA-approved devices and emerging technologies by assessing their clinical utility, mechanism of HF decompensation detection, and ongoing trials. Furthermore, we also discuss the future trend of integrating these devices into routine clinical practice to improve patient clinical outcomes.

AI diagnosis of heart sounds differentiated with super StethoScope

In the aging global society, heart failure and valvular heart diseases, including aortic stenosis, are affecting millions of people and healthcare systems worldwide. Although the number of effective treatment options has increased in recent years, the lack of effective screening methods is provoking continued high mortality and rehospitalization rates. Appropriately, auscultation has been the primary option for screening such patients, however, challenges arise due to the variability in auscultation skills, the objectivity of the clinical method, and the presence of sounds inaudible to the human ear. To address challenges associated with the current approach towards auscultation, the hardware of Super StethoScope was developed. This paper is composed of (1) a background literature review of bioacoustic research regarding heart disease detection, (2) an introduction of our approach to heart sound research and development of Super StethoScope, (3) a discussion of the application of remote auscultation to telemedicine, and (4) results of a market needs survey on traditional and remote auscultation. Heart sounds and murmurs, if collected properly, have been shown to closely represent heart disease characteristics. Correspondingly, the main characteristics of Super StethoScope include: (1) simultaneous collection of electrocardiographic and heart sound for the detection of heart rate variability, (2) optimized signal-to-noise ratio in the audible frequency bands, and (3) acquisition of heart sounds including the inaudible frequency ranges. Due to the ability to visualize the data, the device is able to provide quantitative results without disturbance by sound quality alterations during remote auscultations. An online survey of 3648 doctors confirmed that auscultation is the common examination method used in today's clinical practice and revealed that artificial intelligence-based heart sound analysis systems are expected to be integrated into clinicians' practices. Super StethoScope would open new horizons for heart sound research and telemedicine.

Cardiac Electromechanical Activity in Healthy Cats and Cats with Cardiomyopathies

Optimal heart function depends on perfect synchronization between electrical and mechanical activity. In this pilot study, we aimed to investigate the electromechanical activity of the heart in healthy cats and cats with cardiomyopathy with phonocardiography (PCG) synchronized to an electrocardiography (ECG) pilot device. We included 29 cats (12 healthy cats and 17 cats diagnosed with cardiomyopathy) and performed a clinical examination, PCG synchronized with ECG and echocardiography. We measured the following durations with the pilot PCG device synchronized with ECG: QRS (ventricular depolarization), QT interval (electrical systole), QS1 interval (electromechanical activation time (EMAT)), S1S2 (mechanical systole), QS2 interval (electrical and mechanical systole) and electromechanical window (end of T wave to the beginning of S2). The measured parameters did not differ between healthy cats and cats with cardiomyopathy; however, in cats with cardiomyopathy, EMAT/RR, QS2/RR and S1S2/RR were significantly longer than in healthy cats. This suggests that the hypertrophied myocardium takes longer to generate sufficient pressure to close the mitral valve and that electrical systole, i.e., depolarization and repolarization, and mechanical systoles are longer in cats with cardiomyopathy. The PCG synchronized with the ECG pilot device proved to be a valuable tool for evaluating the electromechanical activity of the feline heart.

Heart Failure Remote Monitoring: A Review and Implementation How-To

Heart failure (HF) is a significant clinical and financial burden worldwide. Remote monitoring (RM) devices capable of identifying early physiologic changes in decompensation have the potential to reduce the HF burden. However, few trials have discussed at length the practical aspects of implementing RM in real-world clinical practice. The present paper reviews current RM devices and clinical trials, focusing on patient populations, outcomes, data collection, storage, and management, and describes the implementation of an RM device in clinical practice, providing a pragmatic and adaptable framework.

Machine learning-powered, device-embedded heart sound measurement can optimize AV delay in patients with CRT

BACKGROUND

Continuous optimization of atrioventricular (AV) delay for cardiac resynchronization therapy (CRT) is mainly performed by electrical means.

OBJECTIVE

The purpose of this study was to develop an estimation model of cardiac function that uses a piezoelectric microphone embedded in a pulse generator to guide CRT optimization.

METHODS

Electrocardiogram, left ventricular pressure (LVP), and heart sounds were simultaneously collected during CRT device implantation procedures. A piezoelectric alarm transducer embedded in a modified CRT device facilitated recording of heart sounds in patients undergoing a pacing protocol with different AV delays. Machine learning (ML) was used to produce a decision-tree ensemble model capable of estimating absolute maximal LVP (LVPmax) and maximal rise of LVP (LVdP/dtmax) using 3 heart sound-based features. To gauge the applicability of ML in AV delay optimization, polynomial curves were fitted to measured and estimated values.

RESULTS

In the data set of ∼30,000 heartbeats, ML indicated S1 amplitude, S2 amplitude, and S1 integral (S1 energy for LVdP/dtmax) as most prominent features for AV delay optimization. ML resulted in single-beat estimation precision for absolute values of LVPmax and LVdP/dtmax of 67% and 64%, respectively. For 20-30 beat averages, cross-correlation between measured and estimated LVPmax and LVdP/dtmax was 0.999 for both. The estimated optimal AV delays were not significantly different from those measured using invasive LVP (difference -5.6 ± 17.1 ms for LVPmax and +5.1 ± 6.7 ms for LVdP/dtmax). The difference in function at estimated and measured optimal AV delays was not statiscally significant (1 ± 3 mm Hg for LVPmax and 9 ± 57 mm Hg/s for LVdP/dtmax).

CONCLUSION

Heart sound sensors embedded in a CRT device, powered by a ML algorithm, provide a reliable assessment of optimal AV delays and absolute LVPmax and LVdP/dtmax.

Value of acoustic cardiography in the clinical diagnosis of coronary heart disease

Background

To investigate the clinical value of acoustic cardiography in the diagnosis of coronary artery disease (CAD) and post‐percutaneous coronary intervention (PCI) early asymptomatic left ventricular systolic dysfunction.

Methods

Inpatients in the department of cardiology were included in the research (n = 315); including 180 patients with angina pectoris and 135 patients with acute anterior wall myocardial infarction after emergency PCI did not present with signs and symptoms of heart failure. Color Doppler echocardiography, brain natriuretic peptide, acoustic cardiography examination were performed. The patients were divided into four groups: non‐CAD group (n = 60), CAD group (n = 120), MIREF group (EF% < 50%, n = 75), and MINEF group (EF% ≥ 50%, n = 60).

Results

Acoustic cardiography parameters EMATc, systolic dysfunction index, S3 strength and S4 strength in the MIREF group were higher than those in MINEF group (p < .05), and the MINEF group was higher than CAD group (p < .05). S3 strength (area under the curve [AUC] 0.67, 95% CI 0.585–0.755, p < .001) and S4 strength (AUC 0.617, 95% CI 0.536–0.698, p = .011) are useful in the diagnosis of CAD. S3 strength (AUC 0.942, 95% CI 0.807–0.978, p < .001) was superior to other indicators in the diagnosis of early left ventricular systolic dysfunction after myocardial infarction.

Conclusion

S4 combined with STT standard change can improve the diagnosis of CAD. Acoustic cardiography can be used as a non‐invasive, rapid, effective, and simple method for the diagnosis of asymptomatic left ventricular systolic dysfunction in the early stage after myocardial infarction.

Temporal changes of cardiac acoustic biomarkers and cardiac function in acute decompensated heart failure

AIMS

Relationships between cardiac acoustic biomarkers (CABs) measured by acoustic cardiography and clinical outcomes have been reported in heart failure (HF) patients. However, no studies have investigated the temporal change of CABs and the corresponding changes in HF status. The purpose of this study was to assess whether the temporal changes of CABs in patients with acute decompensated heart failure (ADHF) reflect changes in cardiac function and status.

METHODS AND RESULTS

Sixty ADHF patients were enrolled prospectively. CABs and echocardiography data were collected at admission, before discharge, and at the first clinic visit. CABs included electromechanical activation time (EMAT); the time interval from Q wave onset on electrocardiography to the first heart sound (S1), QoS2; the time interval from Q wave onset on electrocardiography to the second heart sound (S2); and third heart sound (S3) and fourth heart sound (S4) intensities, defined as the peak-to-peak amplitudes of S3 and S4. EMATc (EMAT/RR) (P = 0.001), S3 intensity (P < 0.001), and S4 intensity (P < 0.001) were significantly decreased, and QoS2 (P = 0.005) was significantly increased from admission to discharge. The change in S3 intensity was significantly correlated with that of E/A (ρ = 0.571, P < 0.001), and the extended QoS2 was also significantly correlated with the increase in the stroke volume index (ρ = 0.383, P = 0.004).

CONCLUSIONS

Some CABs in ADHF patients changed significantly in the normal direction throughout the treatment course and could be useful biomarkers in ADHF management.

Changes in acoustic cardiographic parameters before and after hemodialysis are associated with overall and cardiovascular mortality in hemodialysis patients

Acoustic cardiography can provide simultaneous electrocardiography and acoustic cardiac data to assess the electronic and mechanical heart functions. The aim of this study was to assess whether changes in acoustic cardiographic parameters (ACPs) before and after hemodialysis (HD) are associated with overall and cardiovascular (CV) mortality in HD patients. A total of 162 HD patients was enrolled and ACPs were measured before and after HD, including left ventricular systolic time (LVST), systolic dysfunction index (SDI), third (S3) and fourth (S4) heart sounds, and electromechanical activation time (EMAT). During a follow-up of 2.9 years, 25 deaths occurred with 16 from CV causes. Multivariate analysis showed that high △SDI (per 1; hazard ratio [HR], 2.178; 95% confidence interval [CI], 1.189–3.990), high △EMAT (per 1%; HR, 2.218; 95% CI 1.382–3.559), and low △LVST (per 1 ms; HR, 0.947; 95% CI 0.912–0.984) were independently associated with increased overall mortality. In addition, high △EMAT (per 1%; HR, 2.141; 95% CI 1.117–4.102), and low △LVST (per 1 ms; HR, 0.777; 95% CI 0.637–0.949) were associated with increased CV mortality. In conclusion, the changes in ACPs before and after HD may be a useful clinical marker and stronger prognostic marker of overall and CV mortality than ACPs before HD.

Influence of decompensated heart failure on cardiac acoustic biomarkers: impact on early readmissions

Aims

Preventing hospitalization by detecting early evidence of heart failure (HF) decompensation in an outpatient setting can improve patient's quality of life and reduce costs of care. The purpose of this study was to assess the value of cardiac acoustic biomarkers (CABs), a combination of cardiohaemic vibrations synchronized with ECG signals, and heart rate (HR) for detecting HF decompensation during first 3 months after hospital discharge for HF.

Methods and results

Patients with an ejection fraction ≤35% (HFrEF) and hospitalized for decompensated HF were enrolled in a prospective observational study. All subjects wore a wearable cardioverter‐defibrillator (ZOLL LifeVest^®, Pittsburgh, PA, USA) that is capable of recording CABs and HR. The primary endpoint of the study was the first HF event, defined as HF readmission or HF emergency room visit. From June 2017 through August 2019, 671 patients with HFrEF were enrolled. Eighty‐one patients (12.1%) had a total of 112 HF events. The algorithm detected HF events with a median of 32 days (interquartile range = 11‐45) in advance of the first HF event. The algorithm had a sensitivity of 69%, specificity of 60%, positive predictive value of 19%, and a negative predictive value of 94%. Of note, the baseline (first 7 days post‐enrolment) algorithm using CABs and HR was superior to New York Heart Association classification in detecting patients more likely to have HF decompensation (sensitivity and specificity of 61% and 68% vs. 46% and 55%, respectively).

Conclusions

This prospective international registry showed that an algorithm incorporating CABs and HR data detected HF events 30 days in advance of the event in patients with HFrEF during first 3 months after hospital discharge. Therefore, integrating CAB technology into clinical practice may prevent HF rehospitalizations.