Noninvasive Prediction of Left Ventricular Systolic Dysfunction in Patients With Clinically Suspected Heart Failure Using Acoustic Cardiography

Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study

BACKGROUND

Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients.

OBJECTIVE

This study describes a novel HF diagnostic technology using audio recordings from a standard mobile phone.

METHODS

This prospective study of acoustic microphone recordings enrolled convenience samples of patients from 2 different clinical sites in 2 separate areas of the United States. Recordings were obtained at the aortic (second intercostal) site with the patient sitting upright. The team used recordings to create predictive algorithms using physics-based (not neural networks) models. The analysis matched mobile phone acoustic data to ejection fraction (EF) and stroke volume (SV) as evaluated by echocardiograms. Using the physics-based approach to determine features eliminates the need for neural networks and overfitting strategies entirely, potentially offering advantages in data efficiency, model stability, regulatory visibility, and physical insightfulness.

RESULTS

Recordings were obtained from 113 participants. No recordings were excluded due to background noise or for any other reason. Participants had diverse racial backgrounds and body surface areas. Reliable echocardiogram data were available for EF from 113 patients and for SV from 65 patients. The mean age of the EF cohort was 66.3 (SD 13.3) years, with female patients comprising 38.3% (43/113) of the group. Using an EF cutoff of ≤40% versus >40%, the model (using 4 features) had an area under the receiver operating curve (AUROC) of 0.955, sensitivity of 0.952, specificity of 0.958, and accuracy of 0.956. The mean age of the SV cohort was 65.5 (SD 12.7) years, with female patients comprising 34% (38/65) of the group. Using a clinically relevant SV cutoff of <50 mL versus >50 mL, the model (using 3 features) had an AUROC of 0.922, sensitivity of 1.000, specificity of 0.844, and accuracy of 0.923. Acoustics frequencies associated with SV were observed to be higher than those associated with EF and, therefore, were less likely to pass through the tissue without distortion.

CONCLUSIONS

This work describes the use of mobile phone auscultation recordings obtained with unaltered cellular microphones. The analysis reproduced the estimates of EF and SV with impressive accuracy. This technology will be further developed into a mobile app that could bring screening and monitoring of HF to several clinical settings, such as home or telehealth, rural, remote, and underserved areas across the globe. This would bring high-quality diagnostic methods to patients with HF using equipment they already own and in situations where no other diagnostic and monitoring options exist.

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.

Evaluation of the Audicor Acoustic Cardiography Device as a Diagnostic Tool in Horses with Mitral or Aortic Valve Insufficiency

Mitral and aortic valve insufficiencies have been commonly reported in horses. The objective of this study was to establish the use of acoustic cardiography (Audicor®) in horses with aortic (AI) or mitral valve insufficiency (MI). A total of 17 healthy horses, 18 horses with AI, and 28 horses with MI were prospectively included. None of the horses was in heart failure. Echocardiography and Audicor® analyses were conducted. Electromechanical activating time (EMAT), rate-corrected EMATc, left ventricular systolic time (LVST), rate-corrected LVSTc, and intensity and persistence of the third and fourth heart sound (S3, S4) were reported by Audicor®. Graphical analysis of the three-dimensional (3D) phonocardiogram served to visually detect murmurs. Audicor® snapshot variables were compared between groups using one-way ANOVA followed by Tukey's multiple-comparisons test. The association between Audicor® snapshot variables and the corresponding echocardiographic variables was investigated by linear regression and Bland-Altman analyses. Heart murmurs were not displayed on Audicor® phonocardiograms. No significant differences were found between Audicor® variables obtained in clinically healthy horses and horses with valvular insufficiency. The Audicor® device is unable to detect heart murmurs in horses. Audicor® variables representing cardiac function are not markedly altered, and their association with corresponding echocardiographic variables is poor in horses with valvular insufficiency that are not in heart failure.

Health Monitoring via Heart, Breath, and Korotkoff Sounds by Wearable Piezoelectret Patches

Real-time monitoring of vital sounds from cardiovascular and respiratory systems via wearable devices together with modern data analysis schemes have the potential to reveal a variety of health conditions. Here, a flexible piezoelectret sensing system is developed to examine audio physiological signals in an unobtrusive manner, including heart, Korotkoff, and breath sounds. A customized electromagnetic shielding structure is designed for precision and high-fidelity measurements and several unique physiological sound patterns related to clinical applications are collected and analyzed. At the left chest location for the heart sounds, the S1 and S2 segments related to cardiac systole and diastole conditions, respectively, are successfully extracted and analyzed with good consistency from those of a commercial medical device. At the upper arm location, recorded Korotkoff sounds are used to characterize the systolic and diastolic blood pressure without a doctor or prior calibration. An Omron blood pressure monitor is used to validate these results. The breath sound detections from the lung/ trachea region are achieved a signal-to-noise ration comparable to those of a medical recorder, BIOPAC, with pattern classification capabilities for the diagnosis of viable respiratory diseases. Finally, a 6×6 sensor array is used to record heart sounds at different locations of the chest area simultaneously, including the Aortic, Pulmonic, Erb's point, Tricuspid, and Mitral regions in the form of mixed data resulting from the physiological activities of four heart valves. These signals are then separated by the independent component analysis algorithm and individual heart sound components from specific heart valves can reveal their instantaneous behaviors for the accurate diagnosis of heart diseases. The combination of these demonstrations illustrate a new class of wearable healthcare detection system for potentially advanced diagnostic schemes.

A Wearable Multi-Sensor Array Enables the Recording of Heart Sounds in Homecare

The home monitoring of patients affected by chronic heart failure (CHF) is of key importance in preventing acute episodes. Nevertheless, no wearable technological solution exists to date. A possibility could be offered by Cardiac Time Intervals extracted from simultaneous recordings of electrocardiographic (ECG) and phonocardiographic (PCG) signals. Nevertheless, the recording of a good-quality PCG signal requires accurate positioning of the stethoscope over the chest, which is unfeasible for a naïve user as the patient. In this work, we propose a solution based on multi-source PCG. We designed a flexible multi-sensor array to enable the recording of heart sounds by inexperienced users. The multi-sensor array is based on a flexible Printed Circuit Board mounting 48 microphones with a high spatial resolution, three electrodes to record an ECG and a Magneto-Inertial Measurement Unit. We validated the usability over a sample population of 42 inexperienced volunteers and found that all subjects could record signals of good to excellent quality. Moreover, we found that the multi-sensor array is suitable for use on a wide population of at-risk patients regardless of their body characteristics. Based on the promising findings of this study, we believe that the described device could enable the home monitoring of CHF patients soon.

Predictive value of acoustic cardiography for post-PCI early ventricular remodeling in acute myocardial infarction

Acoustic cardiography is a completely new technology, it has great advantages in the rapid diagnosis of cardiovascular diseases. The purpose of this study was to investigate the clinical value of the fourth heart sound (S4), cardiac systolic dysfunction index (SDI), and the cardiac cycle time-corrected electromechanical activation time (EMATc) in the prediction of post-percutaneous coronary intervention (PCI) early ventricular remodeling (EVR) in patients with acute myocardial infarction (AMI). We recruited 161 patients with AMI of 72-h post-PCI, including 44 EVR patients with left ventricular ejection fraction (LVEF) < 50% and 117 Non-EVR patients (normal left ventricular systolic function group, LVEF ≥ 50%). EMATc, S4, and SDI were independent risk factors for post-PCI early ventricular remodeling in patients with AMI [S4 (OR 2.860, 95% CI 1.297-6.306, p = 0.009), SDI (OR 4.068, 95% CI 1.800-9.194, p = 0.001), and EMATc (OR 1.928, 95% CI 1.420-2.619, p < 0.001)]. The area under the receiver operating characteristic curve for EMATc was 0.89, with an optimal cutoff point of 12.2, EMATc had a sensitivity of 80% and a specificity of 83%. By contrast, an optimal cutoff point of 100 pg/ml, Serum brain natriuretic peptide had a sensitivity of 46% and a specificity of 83%. Our findings suggest the predictive value of EMATc for the occurrence of EVR in these patients was also identified; EMATc may be a simple, quick, and effective way to diagnose EVR after AMI.

A systemic review and meta-analysis comparing the ability of diagnostic of the third heart sound and left ventricular ejection fraction in heart failure

Objective

This study aimed to compare the sensitivity and specificity of diagnosis between the third heart sound (S3) and left ventricular ejection fraction (LVEF) in heart failure (HF).

Methods

Relevant studies were searched in PubMed, SinoMed, China National Knowledge Infrastructure, and the Cochrane Trial Register until February 20, 2022. The sensitivity, specificity, likelihood ratio (LR), and diagnostic odds ratio (DOR) were pooled. The symmetric receiver operator characteristic curve (SROC) and Fagan’s nomogram were drawn. The source of heterogeneity was explored by meta-regression and subgroup analysis.

Results

A total of 19 studies, involving 5,614 participants, were included. The combined sensitivity of S3 was 0.23 [95% confidence interval (CI) (0.15–0.33), specificity was 0.94 [95% CI (0.82–0.98)], area under the SROC curve was 0.49, and the DOR was 4.55; while the sensitivity of LVEF was 0.70 [95% CI (0.53–0.83)], specificity was 0.79 [95% CI (0.75–0.82)], area under the SROC curve was 0.79, and the DOR was 8.64. No publication bias was detected in Deeks’ funnel plot. The prospective design, partial verification bias, and blind contributed to the heterogeneity in specificity, while adequate description of study participants contributed to the heterogeneity in sensitivity. In Fagan’s nomogram, the post-test probability was 48% when the pre-test probability was set as 20%, while in LVEF, the post-test probability was 45% when the pre-test probability was set as 20%.

Conclusion

The use of S3 alone presented lower sensitivity in diagnosing HF compared with LVEF, whereas it was useful in early pathological assessment.

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.

Left Ventricular Electromechanical Remodeling Detected by Acoustic Cardiography in Paroxysmal Atrial Fibrillation

This study aimed to investigate the electromechanical function detected by acoustic cardiography before and after radiofrequency ablation therapy (RFA) in paroxysmal AF (PAF) patients with preserved left ventricular ejection fraction (LVEF). Seventy-five symptomatic PAF patients and 69 patients without arrhythmia were enrolled. Thirty-seven PAF patients received RFA therapy. Acoustic cardiographic exam was performed to check S3 and S4 heart sound, electromechanical activation time (EMAT), LV systolic time percentage (LVST), and systolic dysfunction index (SDI) in all participants. Furthermore, 37 PAF patients also received follow-up acoustic cardiography postRFA. PAF had impaired electromechanical systolic function compared with health participants (%EMAT 14.69 ± 3.62 vs. 10.84 ± 2.62; %LVST 40.83 ± 5.14 vs. 36.70 ± 3.87; SDI 4.75 ± 1.61 vs. 3.26 ± 0.96 all p < 0.001). RFA can improve electromechanical systolic function. Improvement below 13.1% could predict the recurrent AF postcatheter ablation. Higher degree of improved electromechanical systolic function postRFA contribute to lower recurrence of AF. Graphical Abstract Receiver operating characteristic (ROC) curve analysis for % change of systolic dysfunction index (SDI) postRFA in the detection of recurrent AF.

Investigation of Acoustic Cardiographic Parameters before and after Hemodialysis

Patients with end-stage renal disease are at an increased risk of cardiovascular diseases and associated mortality. Acoustic cardiography is a technique in which cardiac acoustic data is synchronized with electric information to detect and characterize heart sounds and detect heart failure early. The aim of this study was to investigate acoustic cardiographic parameters before and after hemodialysis (HD) and their correlations with ankle-brachial index (ABI), brachial-ankle pulse wave velocity (baPWV), and ratio of brachial preejection period to ejection time (bPEP/bET) obtained from an ABI-form device in HD patients. This study enrolled 162 HD patients between October 2016 and April 2018. Demographic, medical, and laboratory data were collected. Acoustic cardiography was performed before and after HD to assess parameters including third heart sound (S3), fourth heart sound (S4), systolic dysfunction index (SDI), electromechanical activation time (EMAT), and left ventricular systolic time (LVST). The mean age of the enrolled patients was 60.4 ± 10.9 years, and 86 (53.1%) patients were male. S4 (p < 0.001) and LVST (p < 0.001) significantly decreased after HD, but EMAT (p < 0.001) increased. Multivariate forward linear regression analysis showed that EMAT/LVST before HD was negatively associated with albumin (unstandardized coefficient β = ‐0.076; p = 0.004) and ABI (unstandardized coefficient β = ‐0.115; p = 0.011) and positively associated with bPEP/bET (unstandardized coefficient β = 0.278; p = 0.003). Screening HD patients with acoustic cardiography may help to identify patients at a high risk of malnutrition, peripheral artery disease, and left ventricular systolic dysfunction.

Night-time electromechanical activation time, pulsatile hemodynamics, and discharge outcomes in patients with acute heart failure

Aims

Both electromechanical activation time (EMAT) and pulsatile hemodynamics measured during the hospitalization course are useful in the prediction of cardiovascular outcomes in patients with acute heart failure syndrome (AHFS). We investigated whether night‐time monitoring of EMAT with the ambulatory acoustic cardiography is superior to the measures of pulsatile hemodynamics for prediction of AHFS post‐discharge outcomes.

Methods and results

A total of 97 patients (71.1 ± 15.4 years old, 81% male, and 73.8% systolic heart failure) hospitalized for AHFS were included. Before discharge, 24 h ambulatory acoustic cardiography and a comprehensive echocardiographic and pulsatile hemodynamic study were performed to assess the mean 24 h, daytime, and night‐time EMAT, carotid systolic blood pressure (SBP) and pulse pressure (PP), amplitude of the reflected pressure wave from a decomposed carotid pressure wave (Pb), and carotid–femoral pulse wave velocity (cfPWV), in addition to measurement of N‐terminal pro‐brain natriuretic peptide (NT‐proBNP) levels. During a mean follow‐up of 389 ± 281 days, 49 patients (50.5%) experienced events including re‐hospitalization for heart failure, myocardial infarction, stroke, or death. Pulsatile hemodynamics, including carotid SBP and PP and Pb, but not cfPWV, and night‐time EMAT, but not daytime EMAT, significantly predicted post‐discharge events when age and NT‐proBNP were accounted for (all P < 0.05). In a final model with adjustment for age and NT‐proBNP, night‐time EMAT, but not Pb, significantly predicted post‐discharge events [hazard ratio per 1 SD and 95% confidence intervals: 1.33 (1.05–1.69), P < 0.05].

Conclusion

Pre‐discharge night‐time EMAT may be a better predictor for post‐discharge adverse events than the measures of the pulsatile hemodynamics in patients with AHFS.