The 200th anniversary of the stethoscope: Can this low-tech device survive in the high-tech 21st century?

Artificial intelligence-enhanced patient evaluation: bridging art and science

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

Beat-to-beat alterations of acoustic intensity and frequency at the maximum power of heart sounds are associated with NT-proBNP levels

Background

Auscultatory features of heart sounds (HS) in patients with heart failure (HF) have been studied intensively. Recent developments in digital and electrical devices for auscultation provided easy listening chances to recognize peculiar sounds related to diastolic HS such as S3 or S4. This study aimed to quantitatively assess HS by acoustic measures of intensity (dB) and audio frequency (Hz).

Methods

Forty consecutive patients aged between 46 and 87 years (mean age, 74 years) with chronic cardiovascular disease (CVD) were enrolled in the present study after providing written informed consent during their visits to the Kitasato University Outpatient Clinic. HS were recorded at the fourth intercostal space along the left sternal border using a highly sensitive digital device. Two consecutive heartbeats were quantified on sound intensity (dB) and audio frequency (Hz) at the peak power of each spectrogram of S1-S4 using audio editing and recording application software. The participants were classified into three groups, namely, the absence of HF (n = 27), HF (n = 8), and high-risk HF (n = 5), based on the levels of NT-proBNP < 300, ≥300, and ≥900 pg/ml, respectively, and also the levels of ejection fraction (EF), such as preserved EF (n = 22), mildly reduced EF (n = 12), and reduced EF (n = 6).

Results

The intensities of four components of HS (S1-S4) decreased linearly (p < 0.02-0.001) with levels of body mass index (BMI) (range, 16.2-33.0 kg/m2). Differences in S1 intensity (ΔS1) and its frequency (ΔfS1) between two consecutive beats were non-audible level and were larger in patients with HF than those in patients without HF (ΔS1, r = 0.356, p = 0.024; ΔfS1, r = 0.356, p = 0.024). The cutoff values of ΔS1 and ΔfS1 for discriminating the presence of high-risk HF were 4.0 dB and 5.0 Hz, respectively.

Conclusions

Despite significant attenuations of all four components of HS by BMI, beat-to-beat alterations of both intensity and frequency of S1 were associated with the severity of HF. Acoustic quantification of HS enabled analyses of sounds below the audible level, suggesting that sound analysis might provide an early sign of HF.

Developing an AI-assisted digital auscultation tool for automatic assessment of the severity of mitral regurgitation: protocol for a cross-sectional, non-interventional study

INTRODUCTION

Mitral regurgitation (MR) is the most common valvular heart disorder, with a morbidity rate of 2.5%. While echocardiography is commonly used in assessing MR, it has many limitations, especially for large-scale MR screening. Cardiac auscultation with electronic stethoscope and artificial intelligence (AI) can be a fast and economical modality for assessing MR severity. Our objectives are (1) to establish a deep neural network (DNN)-based cardiac auscultation method for assessing the severity of MR; and (2) to quantitatively measure the performance of the developed AI-based MR assessment method by virtual clinical trial.

METHODS AND ANALYSIS

In a cross-sectional design, phonocardiogram will be recorded at the mitral valve auscultation area of outpatients. The enrolled patients will be checked by echocardiography to confirm the diagnosis of MR or no MR. Echocardiographic parameters will be used as gold standard to assess the severity of MR, classified into four levels: none, mild, moderate and severe. The study consists of two stages. First, an MR-related cardiac sound database will be created on which a DNN-based MR severity classifier will be trained. The automatic MR severity classifier will be integrated with the Smartho-D2 electronic stethoscope. Second, the performance of the developed smart device will be assessed in an independent clinical validation data set. Sensitivity, specificity, precision, accuracy and F1 score of the developed smart MR assessment device will be evaluated. Agreement on the performance of the smart device between cardiologist users and patient users will be inspected. The interpretability of the developed model will also be studied with statistical comparisons of occlusion map-guided variables among the four severity groups.

ETHICS AND DISSEMINATION

The study protocol was approved by the Medical Ethics Committee of Huzhou Central Hospital, China (registration number: 202302009-01). Informed consent is required from all participants. Dissemination will be through conference presentations and peer-reviewed journals.

TRIAL REGISTRATION NUMBER

ChiCTR2300069496.

Clinical decision support system using a machine learning model to assist simultaneous cardiopulmonary auscultation: Open-label randomized controlled trial

Background

The utility of a clinical decision support system using a machine learning (ML) model for simultaneous cardiac and pulmonary auscultation is unknown.

Objective

This study aimed to develop and evaluate an ML system's utility for cardiopulmonary auscultation.

Methods

First, we developed an ML system for cardiopulmonary auscultation, using cardiopulmonary sound files from our previous study. The technique involved pre-processing, feature extraction, and classification through several neural network layers. After integration, the output class was categorized as "normal," "abnormal," or "undetermined." Second, we evaluated the ML system with 24 junior residents in an open-label randomized controlled trial at a university hospital. Participants were randomly assigned to the ML system group (intervention) or conventional auscultation group (control). During training, participants listened to four cardiac and four pulmonary sounds, all of which were correctly classified. Then, participants classified a series of 16 simultaneous cardiopulmonary sounds. The control group auscultated the sounds using noise-cancelling headphones, while the intervention group did so by watching recommendations from the ML system.

Results

The total scores for correctly identified normal or abnormal cardiopulmonary sounds in the intervention group were significantly higher than those in the control group (366/384 [95.3%] vs. 343/384 [89.3%], P = 0.003). The cardiac test score in the intervention group was better (111/192 [57.8%] vs. 90/192 [46.9%], P = 0.04); there was no significant difference in pulmonary auscultation.

Conclusions

The ML-based system improved the accuracy of cardiopulmonary auscultation for junior residents. This result suggests that the system can assist early-career physicians in accurate screening.

Markov-Based Neural Networks for Heart Sound Segmentation: Using Domain Knowledge in a Principled Way

This work considers the problem of segmenting heart sounds into their fundamental components. We unify statistical and data-driven solutions by introducing Markov-based Neural Networks (MNNs), a hybrid end-to-end framework that exploits Markov models as statistical inductive biases for an Artificial Neural Network (ANN) discriminator. We show that an MNN leveraging a simple one-dimensional Convolutional ANN significantly outperforms two recent purely data-driven solutions for this task in two publicly available datasets: PhysioNet 2016 (Sensitivity: 0.947 ±0.02; Positive Predictive Value : 0.937 ±0.025) and the CirCor DigiScope 2022 (Sensitivity: 0.950 ±0.008; Positive Predictive Value: 0.943 ±0.012). We also propose a novel gradient-based unsupervised learning algorithm that effectively makes the MNN adaptive to unseen datum sampled from unknown distributions. We perform a cross dataset analysis and show that an MNN pre-trained in the CirCor DigiScope 2022 can benefit from an average improvement of 3.90% Positive Predictive Value on unseen observations from the PhysioNet 2016 dataset using this method.

Point-of-Care Ultrasound Overview and Curriculum Implementation in Internal Medicine Residency Training Programs in the United States

Point-of-care ultrasonography (POCUS) augments physical examination and expedites diagnostic care and clinical decision-making. The use of POCUS in internal medicine (IM) appears inconsistent despite its commendable benefits. It is not fully incorporated into the IM residency core competency skills or academic curriculum. This narrative literature review explores the benefits of POCUS and evaluates the need for an IM-focused POCUS curriculum. The obstacles and a proposed curriculum are also described.

The utility of phonocardiograms in real-time remote cardiac auscultation using an internet-connected electronic stethoscope: Open-label randomized controlled pilot trial

Background

We have shown classical cardiac auscultation was superior to remote auscultation. We developed a phonocardiogram system to visualize sounds in remote auscultation.

Objective

This study aimed to evaluate the effect of phonocardiograms on the diagnostic accuracy in remote auscultation using a cardiology patient simulator.

Methods

In this open-label randomized controlled pilot trial, we randomly assigned physicians to the real-time remote auscultation group (control group) or the real-time remote auscultation with the phonocardiogram group (intervention group). Participants attended a training session in which they auscultated 15 sounds with the correct classification. After that, participants attended a test session where they had to classify 10 sounds. The control group auscultated the sounds remotely using an electronic stethoscope, an online medical program and a 4-K TV speaker without watching the TV screen. The intervention group performed auscultation like the control group but watched the phonocardiogram on the TV screen. The primary and secondary outcomes were the total test scores and each sound score, respectively.

Results

A total of 24 participants were included. The total test score in the intervention group (80/120, 66.7%) was higher than that in the control group (66/120, 55.0%), although the difference was statistically insignificant (P = .06). The correct answer rates of each sound were not different. Valvular/irregular rhythm sounds were not misclassified as normal sounds in the intervention group.

Conclusions

Using a phonocardiogram improved the total correct answer rate by more than 10% in remote auscultation, although statistically insignificant. The phonocardiogram could help physicians screen valvular/irregular rhythm sounds from normal sounds.

Trial registration

UMIN-CTR UMIN000045271; https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000051710.

Regularity and mechanism of fake crackle noise in an electronic stethoscope

Background: Electronic stethoscopes are widely used for cardiopulmonary auscultation; their audio recordings are used for the intelligent recognition of cardiopulmonary sounds. However, they generate noise similar to a crackle during use, significantly interfering with clinical diagnosis. This paper will discuss the causes, characteristics, and occurrence rules of the fake crackle and establish a reference for improving the reliability of the electronic stethoscope in lung auscultation. Methods: A total of 56 participants with healthy lungs (no underlying pulmonary disease, no recent respiratory symptoms, and no adventitious lung sound, as confirmed by an acoustic stethoscope) were enrolled in this study. A 30-s audio recording was recorded from each of the nine locations of the larynx and lungs of each participant with a 3M Littmann 3200 electronic stethoscope, and the audio was output in diaphragm mode and auscultated by the clinician. The doctor identified the fake crackles and analyzed their frequency spectrum. High-pass and low-pass filters were used to detect the frequency distribution of the fake crackles. Finally, the fake crackle was artificially regenerated to explore its causes. Results: A total of 500 audio recordings were included in the study, with 61 fake crackle audio recordings. Fake crackles were found predominantly in the lower lung. There were significant differences between lower lung and larynx (p < 0.001), lower lung and upper lung (p = 0.005), lower lung and middle lung (p = 0.005), and lower lung and infrascapular region (p = 0.027). Furthermore, more than 90% of fake crackles appeared in the inspiratory phase, similar to fine crackles, significantly interfering with clinical diagnosis. The spectral analysis revealed that the frequency range of fake crackles was approximately 250-1950 Hz. The fake crackle was generated when the diaphragm of the electronic stethoscope left the skin slightly but not completely. Conclusion: Fake crackles are most likely to be heard when using an electronic stethoscope to auscultate bilateral lower lungs, and the frequency of a fake crackle is close to that of a crackle, likely affecting the clinician's diagnosis.

Smartphone as an electronic stethoscope: factors influencing heart sound quality. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022;3:473-480. [PMID: 36712168 PMCID: PMC9708017 DOI: 10.1093/ehjdh/ztac044]

Aims

Smartphones are equipped with a high-quality microphone which may be used as an electronic stethoscope. We aim to investigate the factors influencing quality of heart sound recorded using a smartphone by non-medical users.

Methods and results

An app named Echoes was developed for recording heart sounds using iPhone. Information on phone version and users' characteristics including sex, age, and body mass index (BMI) was collected. Heart sound quality was visually assessed and its relation to phone version and users' characteristics was analysed. A total of 1148 users contributed to 7597 heart sound recordings. Over 80% of users were able to make at least one good-quality recording. Good-, unsure- and bad-quality recordings amounted to 5647 (74.6%), 466 (6.2%) and 1457 (19.2%), respectively. Most good recordings were collected in the first three attempts of the users. Phone version did not significantly change the users' success rate of making a good recording, neither was sex in the first attempt (P = 0.41) or the first three attempts (P = 0.21). Success rate tended to decrease with age in the first attempt (P = 0.06) but not the first three attempts (P = 0.70). BMI did not significantly affect the heart sound quality in a single attempt (P = 0.73) or in three attempts (P = 0.14).

Conclusion

Smartphone can be used by non-medical users to record heart sounds in good quality. Age may affect heart sound recording, but hardware, sex, and BMI do not alter the recording.

A novel echocardiographic right ventricular dysfunction score can identify hemodynamic severity profiles in left ventricular dysfunction

Purpose

Recognition of congestion and hypoperfusion in patients with chronic left ventricular dysfunction (LVD) has therapeutic and prognostic implications. In the present study we hypothesized that a multiparameter echocardiographic grading of right ventricular dysfunction (RVD) can facilitate the characterization of hemodynamic profiles.

Methods

Consecutive patients (n = 105, age 53 ± 14 years, males 77%, LV ejection fraction 28 ± 11%) referred for heart transplant or heart failure work-up, with catheterization and echocardiography within 48 h, were reviewed retrospectively. Three hemodynamic profiles were defined: compensated LVD (cLVD, normal pulmonary capillary wedge pressure (PCWP < 15 mmHg) and normal mixed venous saturation (SvO₂ ≥ 60%)); decompensated LVD (dLVD, with increased PCWP) and LV failure (LVF, increased PCWP and reduced SvO₂). We established a 5-point RVD score including pulmonary hypertension, reduced tricuspid annular plane systolic excursion, RV dilatation, ≥ moderate tricuspid regurgitation and increased right atrial pressure.

Results

The RVD score [median (IQR 25%;75%)] showed significant in-between the three groups differences with 1 (0;1), 1 (0.5;2) and 3.0 (2;3.5) in patients with cLVD, dLVD and LVF, respectively. The finding of RVD score ≥ 2 or ≥ 4 increased the likelihood of decompensation or LVF 5.2-fold and 6.7-fold, respectively. On the contrary, RVD score < 1 and < 2 reduced the likelihood 11.1-fold and 25-fold, respectively. The RVD score was more helpful than standard echocardiography regarding identification of hemodynamic profiles.

Conclusions

In this proof of concept study an echocardiographic RVD score identified different hemodynamic severity profiles in patients with chronic LVD and reduced ejection fraction. Further studies are needed to validate its general applicability.

Graphical abstract

Physiological Features of Cardiac Ventricle and Valve Dynamics from Wearable Radio-Frequency Sensors

Early detection of cardiovascular diseases via non-invasive, convenient, and continuous monitoring is crucial to reducing preventable deaths. This paper illustrates such monitoring using wearable near-field radio-frequency sensors to analyze ventricle and valve transients, which can be used as indicators of myriad cardiac disorders. We applied a novel vector injection signal processing method to improve timing consistency in ventricular contraction, ventricular relaxation, and valve opening extraction. The median relative timing error in valve opening detection was 14.7ms and 37.8ms for semilunar and atrioventricular valves, respectively, as benchmarked by the S1 and S2 heart sounds from a synchronous phonocardiogram. Clinical Relevance- No wearable sensor currently exists to conveniently and reliably evaluate ventricular and valvular dynamics, specifically valvular opening. Beyond extraction of the heart rate and its variation, the method in this paper has the potential to enable non-invasive measurements of detailed cardiac cycle timing features including valve openings, isovolumetric contraction/relaxation times, and ejection periods, improving the monitoring of patient health away from clinical healthcare centers.

Evaluation of a digitally enhanced cardiac auscultation learning method: a controlled study

BACKGROUND

Cardiac auscultation remains an efficient and accessible diagnostic tool, especially in resource-limited countries where modern diagnostic devices like cardiac ultrasound are expensive and difficult to access. However, cardiac auscultation skills of medical students and physicians are declining, mainly because of an ineffective teaching method for this technique. The objective of this study is to evaluate the effect of a digitally enhanced cardiac auscultation learning method on participants' theoretical knowledge and auscultation skills.

METHODS

This will be a controlled study with two parallel arms (1:1). Participants (fourth-year medical students) will be divided into two groups: an intervention group (receiving additional lectures, clinical internship and audio listening sessions) and a control group (receiving additional lectures and clinical internship). At the beginning of the study, all participants will undergo a pre-test that consist of two parts: a knowledge assessment based on multiple-choice questions and a skills assessment based on recognition of cardiac sounds from audio files. Thereafter, three specific additional lectures on cardiac auscultation will be delivered and all participants will take part in their official clinical internship. During these clinical internships (eight weeks), participants of the intervention group will be invited to two listening sessions based on five digital recordings of heart sounds. At the end of the clinical internship, all participants will be invited to a post-test to evaluate their knowledge, skills and satisfaction according to their learning method. The main outcome will be the participants' knowledge progression. The other outcomes will be the participants' skills progression, participants' total progression and satisfaction. Data will be collected and analyzed in per protocol.

DISCUSSION

This study could contribute to the development of a learning method that takes into account the advantages of the conventional method and the contribution of digital technology. Positive results could lead to improved cardiac auscultation skills among health professionals, especially in developing countries.

TRIAL REGISTRATION

The trial is registered on the Pan-African Clinical Trials Registry ( http://www.pactr.org ) under unique identification number: PACTR202001504666847 , registered the 29 November 2019.

Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope

Electronic stethoscopes offer several advantages over conventional acoustic stethoscopes, including noise reduction, increased amplification, and ability to store and transmit sounds. However, the acoustical characteristics of electronic and acoustic stethoscopes can differ significantly, introducing a barrier for clinicians to transition to electronic stethoscopes. This work proposes a method to process lung sounds recorded by an electronic stethoscope, such that the sounds are perceived to have been captured by an acoustic stethoscope. The proposed method calculates an electronic-to-acoustic stethoscope filter by measuring the difference between the average frequency responses of an acoustic and an electronic stethoscope to multiple lung sounds. To validate the method, a change detection experiment was conducted with 51 medical professionals to compare filtered electronic, unfiltered electronic, and acoustic stethoscope lung sounds. Participants were asked to detect when transitions occurred in sounds comprising several sections of the three types of recordings. Transitions between the filtered electronic and acoustic stethoscope sections were detected, on average, by chance (sensitivity index equal to zero) and also detected significantly less than transitions between the unfiltered electronic and acoustic stethoscope sections ( ), demonstrating the effectiveness of the method to filter electronic stethoscopes to mimic an acoustic stethoscope. This processing could incentivize clinicians to adopt electronic stethoscopes by providing a means to shift between the sound characteristics of acoustic and electronic stethoscopes in a single device, allowing for a faster transition to new technology and greater appreciation for the electronic sound quality.

Wearable Multimodal Stethoscope Patch for Wireless Biosignal Acquisition and Long-Term Auscultation

Detecting critical events in postoperative care and improving comfort, costs and availability in sleep assessment are two of many areas in which wearable biosignal acquisition can be a viable tool. Modern sensors as well as patch and textile integration facilitate unobtrusive biosignal acquisition, yet placing sensors at different locations across the body is still prevailing. Actigraphy and the electrocardiogram (ECG) are commonly integrated modalities. The stethoscope however, despite its wide range of applications, has been neglected from these developments. The introduction of digital stethoscopes, recently led to an objectification and increased interest in the field. We present the prototype of a wearable, Bluetooth 5.0 LE enabled multimodal sensor patch combining five modalities: MEMS stethoscope, ambient noise sensing, ECG, impedance pneumography (IP) and 9-axial actigraphy. The system alleviates the need for sensors at different body positions and enables long-term auscultation. Using high sampling rates and online synchronization, multimodal sensor fusion becomes feasible. The patch measures 70 mm x 60 mm and is attached using three 24 mm Ag/AgCl electrodes. High quality cardiac and pulmonary auscultation as well as ECG and IP acquisition are demonstrated. We derived respiration surrogates with linear correlations to a reference exceeding 0.91 and conclude that the system can be utilized in fields requiring unobtrusive yet high quality signal acquisition. Future research will include the integration of additional sensors and further size reduction.

Reliability of focused cardiac ultrasound performed by first-year internal medicine residents at a community hospital after a short training

The use of bedside ultrasound over the past few decades has created a new wave of options for visualizing pathological processes allowing for faster and better detection of disease. We aimed to evaluate the reliability of focused cardiac ultrasound (FCU) performed by first-year internal medicine residents at a community hospital after a short period of training. They received a two-hour lecture and initially performed a supervised FCU followed by ten unsupervised/independent FCUs each. The four parameters that were assessed were left systolic ventricular function, right systolic ventricular function, presence of pericardial effusion, and presence of IVC dilation. Interpretation and analysis of ultrasound images were then carried out by both the residents and an attending physician with expertise in FCU analysis and interpretation. Cohen's Kappa values were obtained comparing the results found by the interns versus the attending. Our findings indicate that more training is required for reliable analysis of FCU by first-year medical residents. Our results also emphasize the need to carefully evaluate the medical residents' FCU skills after the training.

Deep learning-based automatic blood pressure measurement: evaluation of the effect of deep breathing, talking and arm movement

Objectives: It is clinically important to evaluate the performance of a newly developed blood pressure (BP) measurement method under different measurement conditions. This study aims to evaluate the performance of using deep learning-based method to measure BPs and BP change under non-resting conditions.Materials and methods: Forty healthy subjects were studied. Systolic and diastolic BPs (SBPs and DBPs) were measured under four conditions using deep learning and manual auscultatory method. The agreement between BPs determined by the two methods were analysed under different conditions. The performance of using deep learning-based method to measure BP changes was finally evaluated.Results: There were no significant BPs differences between two methods under all measurement conditions (all p > .1). SBP and DBP measured by deep learning method changed significantly in comparison with the resting condition: decreased by 2.3 and 4.2 mmHg with deeper breathing (both p < .05), increased by 3.6 and 6.4 mmHg with talking, and increased by 5.9 and 5.8 mmHg with arm movement (all p < .05). There were no significant differences in BP changes measured by two methods (all p > .4, except for SBP change with deeper breathing).Conclusion: This study demonstrated that the deep learning method could achieve accurate BP measurement under both resting and non-resting conditions.Key messagesAccurate and reliable blood pressure measurement is clinically important. We evaluated the performance of our developed deep learning-based blood pressure measurement method under resting and non-resting measurement conditions.The deep learning-based method could achieve accurate BP measurement under both resting and non-resting measurement conditions.

Prognostic Implications of Congestion on Physical Examination Among Contemporary Patients With Heart Failure and Reduced Ejection Fraction

Background:

The contemporary prognostic value of the physical examination— beyond traditional risk factors including natriuretic peptides, risk scores, and symptoms—in heart failure (HF) with reduced ejection fraction is unknown. We aimed to determine the association between physical signs of congestion at baseline and during study follow-up with quality of life and clinical outcomes and to assess the treatment effects of sacubitril/valsartan on congestion.

Methods:

We analyzed participants from PARADIGM-HF (Prospective Comparison of Angiotensin Receptor-Neprilysin Inhibitor With Angiotensin Converting Enzyme Inhibitor to Determine Impact on Global Mortality and Morbidity in HF) with an available physical examination at baseline. We examined the association of the number of signs of congestion (jugular venous distention, edema, rales, and third heart sound) with the primary outcome (cardiovascular death or HF hospitalization), its individual components, and all-cause mortality using time-updated, multivariable-adjusted Cox regression. We further evaluated whether sacubitril/valsartan reduced congestion during follow-up and whether improvement in congestion is related to changes in clinical outcomes and quality of life, assessed by Kansas City Cardiomyopathy Questionnaire overall summary scores.

Results:

Among 8380 participants, 0, 1, 2, and 3+ signs of congestion were present in 70%, 21%, 7%, and 2% of patients, respectively. Patients with baseline congestion were older, more often female, had higher MAGGIC risk scores (Meta-Analysis Global Group in Chronic Heart Failure) and lower Kansas City Cardiomyopathy Questionnaire overall summary scores ( P <0.05). After adjusting for baseline natriuretic peptides, time-updated Meta-Analysis Global Group in Chronic Heart Failure score, and time-updated New York Heart Association class, increasing time-updated congestion was associated with all outcomes ( P <0.001). Sacubitril/valsartan reduced the risk of the primary outcome irrespective of clinical signs of congestion at baseline ( P =0.16 for interaction), and treatment with the drug improved congestion to a greater extent than did enalapril ( P =0.011). Each 1-sign reduction was independently associated with a 5.1 (95% CI, 4.7–5.5) point improvement in Kansas City Cardiomyopathy Questionnaire overall summary scores. Change in congestion strongly predicted outcomes even after adjusting for baseline congestion ( P <0.001).

Conclusions:

In HF with reduced ejection fraction, the physical exam continues to provide significant independent prognostic value even beyond symptoms, natriuretic peptides, and Meta-Analysis Global Group in Chronic Heart Failure risk score. Sacubitril/valsartan improved congestion to a greater extent than did enalapril. Reducing congestion in the outpatient setting is independently associated with improved quality of life and reduced cardiovascular events, including mortality.

Clinical Trial Registration:

https://www.clinicaltrials.gov . Unique identifier: NCT01035255.

Hand-held echocardiography in children with hypoplastic left heart syndrome

BACKGROUND

When performed by cardiologists, hand-held echocardiography (HHE) can assess ventricular systolic function and valve disease in adults, but its accuracy and utility in congenital heart disease is unknown. In hypoplastic left heart syndrome (HLHS), the echocardiographic detection of depressed right ventricular (RV) systolic function and higher grade tricuspid regurgitation (TR) can identify patients who are at increased risk of morbidity and mortality and who may benefit from additional imaging or medical therapies.

METHODS

Children with HLHS after Stage I or II surgical palliation (Norwood or Glenn procedures) were prospectively enrolled. Subjects underwent HHE by a pediatric cardiologist on the same day as standard echocardiography (SE). Using 4-point scales, bedside HHE assessment of RV systolic function and TR were compared with blinded assessment of offline SE images. Concordance correlation coefficient (CCC) was used to evaluate agreement.

RESULTS

Thirty-two HHEs were performed on 15 subjects (Stage I: n = 17 and Stage II: n = 15). Median subject age was 3.4 months (14 days-4.2 years). Median weight was 5.9 kg (2.6-15.4 kg). Bedside HHE assessment of RV systolic function and TR severity had substantial agreement with SE (CCC = 0.80, CCC = 0.74, respectively; P < .001). HHE sensitivity and specificity for any grade of depressed RV systolic function were 100% and 92%, respectively, and were 94% and 88% for moderate or greater TR, respectively. Average HHE scan time was 238 seconds.

CONCLUSIONS

HHE offers a rapid, bedside tool for pediatric cardiologists to detect RV systolic dysfunction and hemodynamically significant TR in HLHS.

Novel algorithm to screen for heart murmurs using computer-aided auscultation in neonates: a prospective single center pilot observational study

BACKGROUND

Automated detection of heart murmurs with computer-aided auscultation is not yet in clinical routine use. Aim of this study was to test sensitivity and specificity of a novel prototype algorithm in automated detection of heart murmurs from digitally recorded phonocardiograms in neonates admitted at the Neonatal Intensive Care Unit.

METHODS

In a prospective pilot observational study from November 2012 to December 2013 auscultations by pediatricians and computer aided auscultation were performed within 12 hours of neonatal echocardiography. Echocardiography was defined as pathological when resulting in any clinical consequences or causing murmur. Phonocardiograms and auscultation were defined as pathological if a murmur was detected. Phonocardiograms were analyzed offline with a novel algorithm prototype (CSD Labs, Graz, Austria) for detection of murmurs in neonates in a first run and with an optimized algorithm in a second run and were compared with echocardiography. Sensitivity and specificity of auscultation by pediatrician and computer aided auscultation were analyzed.

RESULTS

Thirty-six neonates (gestational age: 36±3 weeks) were included. Twenty-three (64%) neonates had pathological or murmur causing findings in echocardiography (positive echocardiography). Sensitivity and specificity of auscultation by pediatrician were 17% and 100%, respectively. In comparison to auscultation by pediatrician sensitivity of first run and second run were significantly higher with 70% and 83%, respectively. Specificity of first run and second run were 77% and 85%, respectively.

CONCLUSIONS

Phonocardiogram analysis using the novel algorithm prototype had a higher sensitivity than auscultation by pediatrician in detecting positive echocardiography findings in neonates.

A description of externally recorded womb sounds in human subjects during gestation

Objective

Reducing environmental noise benefits premature infants in neonatal intensive care units (NICU), but excessive reduction may lead to sensory deprivation, compromising development. Instead of minimal noise levels, environments that mimic intrauterine soundscapes may facilitate infant development by providing a sound environment reflecting fetal life. This soundscape may support autonomic and emotional development in preterm infants. We aimed to assess the efficacy and feasibility of external non-invasive recordings in pregnant women, endeavoring to capture intra-abdominal or womb sounds during pregnancy with electronic stethoscopes and build a womb sound library to assess sound trends with gestational development. We also compared these sounds to popular commercial womb sounds marketed to new parents.

Study design

Intra-abdominal sounds from 50 mothers in their second and third trimester (13 to 40 weeks) of pregnancy were recorded for 6 minutes in a quiet clinic room with 4 electronic stethoscopes, placed in the right upper and lower quadrants, and left upper and lower quadrants of the abdomen. These recording were partitioned into 2-minute intervals in three different positions: standing, sitting and lying supine. Maternal and gestational age, Body Mass Index (BMI) and time since last meal were collected during recordings. Recordings were analyzed using long-term average spectral and waveform analysis, and compared to sounds from non-pregnant abdomens and commercially-marketed womb sounds selected for their availability, popularity, and claims they mimic the intrauterine environment.

Results

Maternal sounds shared certain common characteristics, but varied with gestational age. With fetal development, the maternal abdomen filtered high (500–5,000 Hz) and mid-frequency (100–500 Hz) energy bands, but no change appeared in contributions from low-frequency signals (10–100 Hz) with gestational age. Variation appeared between mothers, suggesting a resonant chamber role for intra-abdominal space. Compared to commercially-marketed sounds, womb signals were dominated by bowel sounds, were of lower frequency, and showed more variation in intensity.

Conclusions

High-fidelity intra-abdominal or womb sounds during pregnancy can be recorded non-invasively. Recordings vary with gestational age, and show a predominance of low frequency noise and bowel sounds which are distinct from popular commercial products. Such recordings may be utilized to determine whether sounds influence preterm infant development in the NICU.

Cardiac Auscultation Using Smartphones: Pilot Study

BACKGROUND

Cardiac auscultation is a cost-effective, noninvasive screening tool that can provide information about cardiovascular hemodynamics and disease. However, with advances in imaging and laboratory tests, the importance of cardiac auscultation is less appreciated in clinical practice. The widespread use of smartphones provides opportunities for nonmedical expert users to perform self-examination before hospital visits.

OBJECTIVE

The objective of our study was to assess the feasibility of cardiac auscultation using smartphones with no add-on devices for use at the prehospital stage.

METHODS

We performed a pilot study of patients with normal and pathologic heart sounds. Heart sounds were recorded on the skin of the chest wall using 3 smartphones: the Samsung Galaxy S5 and Galaxy S6, and the LG G3. Recorded heart sounds were processed and classified by a diagnostic algorithm using convolutional neural networks. We assessed diagnostic accuracy, as well as sensitivity, specificity, and predictive values.

RESULTS

A total of 46 participants underwent heart sound recording. After audio file processing, 30 of 46 (65%) heart sounds were proven interpretable. Atrial fibrillation and diastolic murmur were significantly associated with failure to acquire interpretable heart sounds. The diagnostic algorithm classified the heart sounds into the correct category with high accuracy: Galaxy S5, 90% (95% CI 73%-98%); Galaxy S6, 87% (95% CI 69%-96%); and LG G3, 90% (95% CI 73%-98%). Sensitivity, specificity, positive predictive value, and negative predictive value were also acceptable for the 3 devices.

CONCLUSIONS

Cardiac auscultation using smartphones was feasible. Discrimination using convolutional neural networks yielded high diagnostic accuracy. However, using the built-in microphones alone, the acquisition of reproducible and interpretable heart sounds was still a major challenge.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03273803; https://clinicaltrials.gov/ct2/show/NCT03273803 (Archived by WebCite at http://www.webcitation.org/6x6g1fHIu).