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Tidsskrift For Den Norske Laegeforening... Feb 2024Ventricular septal rupture (VSR) following acute myocardial infarction is rare in the modern revascularisation era. Nevertheless, clinical awareness is paramount, as...
BACKGROUND
Ventricular septal rupture (VSR) following acute myocardial infarction is rare in the modern revascularisation era. Nevertheless, clinical awareness is paramount, as presentation may vary.
CASE PRESENTATION
A middle-aged male with no history of cardiovascular disease developed progressive heart failure symptoms while travelling abroad. Initial workup revealed a prominent systolic murmur, but findings were inconsistent with acute coronary syndrome. Transthoracic echocardiogram showed a small hypokinetic area in the basal septum, preserved left ventricular function and no significant valvulopathy. Despite the absence of chest pain, an invasive angiography revealed occlusion of a septal branch emerging from the left anterior descending artery, otherwise patent coronary arteries. Despite administration of diuretics, the patient remained symptomatic and presented two months later to his primary care provider with a persisting systolic murmur. He was subsequently referred to the outpatient cardiology clinic where echocardiography revealed a large VSR involving the basal anteroseptum of the left ventricle with a significant left-to-right shunt. After accurate radiological and haemodynamic assessment of the defect, he successfully underwent elective surgical repair.
INTERPRETATION
Although traditionally associated with large transmural myocardial infarctions, VSR may arise also from minor, subclinical events. A new-onset murmur is a valuable hint for the alert clinician.
Topics: Humans; Male; Middle Aged; Systolic Murmurs; Myocardial Infarction; Ventricular Septal Rupture; Echocardiography; Dyspnea
PubMed: 38349103
DOI: 10.4045/tidsskr.23.0373 -
PLOS Digital Health Sep 2023Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and... (Review)
Review
Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and treatment for abnormal cardiac function. However, experts are needed to interpret the heart sounds, limiting the accessibility of cardiac auscultation in resource-constrained environments. Therefore, the George B. Moody PhysioNet Challenge 2022 invited teams to develop algorithmic approaches for detecting heart murmurs and abnormal cardiac function from phonocardiogram (PCG) recordings of heart sounds. For the Challenge, we sourced 5272 PCG recordings from 1452 primarily pediatric patients in rural Brazil, and we invited teams to implement diagnostic screening algorithms for detecting heart murmurs and abnormal cardiac function from the recordings. We required the participants to submit the complete training and inference code for their algorithms, improving the transparency, reproducibility, and utility of their work. We also devised an evaluation metric that considered the costs of screening, diagnosis, misdiagnosis, and treatment, allowing us to investigate the benefits of algorithmic diagnostic screening and facilitate the development of more clinically relevant algorithms. We received 779 algorithms from 87 teams during the Challenge, resulting in 53 working codebases for detecting heart murmurs and abnormal cardiac function from PCG recordings. These algorithms represent a diversity of approaches from both academia and industry, including methods that use more traditional machine learning techniques with engineered clinical and statistical features as well as methods that rely primarily on deep learning models to discover informative features. The use of heart sound recordings for identifying heart murmurs and abnormal cardiac function allowed us to explore the potential of algorithmic approaches for providing more accessible diagnostic screening in resource-constrained environments. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and clinical relevance of the research from the Challenge.
PubMed: 37695769
DOI: 10.1371/journal.pdig.0000324 -
Sensors (Basel, Switzerland) Jun 2023(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to...
(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital stethoscopes exist but none are dedicated to pediatrics. Our goal was to develop a digital auscultation platform for pediatric medicine. (2) Methods: We developed StethAid-a digital platform for artificial intelligence-assisted auscultation and telehealth in pediatrics-that consists of a wireless digital stethoscope, mobile applications, customized patient-provider portals, and deep learning algorithms. To validate the StethAid platform, we characterized our stethoscope and used the platform in two clinical applications: (1) Still's murmur identification and (2) wheeze detection. The platform has been deployed in four children's medical centers to build the first and largest pediatric cardiopulmonary datasets, to our knowledge. We have trained and tested deep-learning models using these datasets. (3) Results: The frequency response of the StethAid stethoscope was comparable to those of the commercially available Eko Core, Thinklabs One, and Littman 3200 stethoscopes. The labels provided by our expert physician offline were in concordance with the labels of providers at the bedside using their acoustic stethoscopes for 79.3% of lungs cases and 98.3% of heart cases. Our deep learning algorithms achieved high sensitivity and specificity for both Still's murmur identification (sensitivity of 91.9% and specificity of 92.6%) and wheeze detection (sensitivity of 83.7% and specificity of 84.4%). (4) Conclusions: Our team has created a technically and clinically validated pediatric digital AI-enabled auscultation platform. Use of our platform could improve efficacy and efficiency of clinical care for pediatric patients, reduce parental anxiety, and result in cost savings.
Topics: Humans; Child; Artificial Intelligence; Auscultation; Stethoscopes; Heart Murmurs; Algorithms; Respiratory Sounds
PubMed: 37420914
DOI: 10.3390/s23125750 -
Journal of Cardiology Apr 2024In the aging global society, heart failure and valvular heart diseases, including aortic stenosis, are affecting millions of people and healthcare systems worldwide.... (Review)
Review
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.
Topics: Humans; Stethoscopes; Heart Sounds; Artificial Intelligence; Auscultation; Heart Diseases; Heart Auscultation
PubMed: 37734656
DOI: 10.1016/j.jjcc.2023.09.007 -
The Journal of Veterinary Medical... Sep 2023A 1-month-old crossbred calf was referred for examination due to marked systolic heart murmurs and poor growth. The heart murmur was most audible on the right side of...
A 1-month-old crossbred calf was referred for examination due to marked systolic heart murmurs and poor growth. The heart murmur was most audible on the right side of the cranial thorax. Cardiomegaly was evident on chest radiography, and echocardiography demonstrated aortic regurgitation and decreased fractional shortening. Cardiomegaly, aortic root dilation and cardiac displacement were confirmed by computed tomography. At necropsy, the heart was enlarged, and all three aortic valve leaflets were irregularly shaped. In calves with chronic aortic insufficiency, remodeling displacement of the heart and aorta causes changes in the location and timing of heart murmurs. Therefore, aortic insufficiency cannot be ruled out when a systolic heart murmur can be observed in the right chest wall.
Topics: Animals; Cattle; Aortic Valve Insufficiency; Aortic Valve; Heart Murmurs; Echocardiography; Cardiomegaly; Cattle Diseases
PubMed: 37532587
DOI: 10.1292/jvms.23-0139 -
Journal of the American Heart... Oct 2023Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying...
Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration-cleared algorithms trained via deep learning on >15 000 heart sound recordings. Methods and Results We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board-certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) Conclusions The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care.
Topics: Adult; Humans; Deep Learning; Heart Murmurs; Heart Diseases; Heart Auscultation; Algorithms
PubMed: 37830333
DOI: 10.1161/JAHA.123.030377 -
Journal of Family Medicine and Primary... Jul 2023Cardiac diseases in the pediatric population can be congenital or acquired. If the diagnosis and treatment are early, the chance for survival increases. Thus, this study...
OBJECTIVES
Cardiac diseases in the pediatric population can be congenital or acquired. If the diagnosis and treatment are early, the chance for survival increases. Thus, this study aimed to determine the indications for pediatric cardiology consultations in a single tertiary hospital in Jeddah, Saudi Arabia.
MATERIALS AND METHODS
This study was conducted in 2020-2021 at a tertiary center in Jeddah, Saudi Arabia. Patients younger than 14 years of age who were referred by outpatient clinics or those who presented to the emergency department and needed outpatient cardiac evaluation were included in this study. Inpatient referrals were excluded. The Statistical Package for the Social Sciences version 21 was used for statistical analyses.
RESULTS
A total of 416 referred patients were included in this study. New patients accounted for 74% of the referrals, while known patients accounted for 26%. The median age was 2.728 years, with 56.3% being male participants. The three most common reasons for referral were: evaluation of cardiac function (21.6%), follow-up evaluation of fetal/neonatal diagnosis (19.5%), and heart murmurs (16.8%).
CONCLUSION
Most of the referrals were new patients. Of those who underwent echocardiography, 48.2% had abnormal results. We recommend further studies to help guide the direction of the residents' education and to provide better patient healthcare services.
PubMed: 37649738
DOI: 10.4103/jfmpc.jfmpc_65_23 -
PLOS Digital Health Mar 2024Cardiovascular diseases (CVDs) account for a high fatality rate worldwide. Heart murmurs can be detected from phonocardiograms (PCGs) and may indicate CVDs. Still, they...
OBJECTIVE
Cardiovascular diseases (CVDs) account for a high fatality rate worldwide. Heart murmurs can be detected from phonocardiograms (PCGs) and may indicate CVDs. Still, they are often overlooked as their detection and correct clinical interpretation require expert skills. In this work, we aim to predict the presence of murmurs and clinical outcomes from multiple PCG recordings employing an explainable multitask model.
APPROACH
Our approach consists of a two-stage multitask model. In the first stage, we predict the murmur presence in single PCGs using a multiple instance learning (MIL) framework. MIL also allows us to derive sample-wise classifications (i.e. murmur locations) while only needing one annotation per recording ("weak label") during training. In the second stage, we fuse explainable hand-crafted features with features from a pooling-based artificial neural network (PANN) derived from the MIL framework. Finally, we predict the presence of murmurs and the clinical outcome for a single patient based on multiple recordings using a simple feed-forward neural network.
MAIN RESULTS
We show qualitatively and quantitatively that the MIL approach yields useful features and can be used to detect murmurs on multiple time instances and may thus guide a practitioner through PCGs. We analyze the second stage of the model in terms of murmur classification and clinical outcome. We achieved a weighted accuracy of 0.714 and an outcome cost of 13612 when using the PANN model and demographic features on the CirCor dataset (hidden test set of the George B. Moody PhysioNet challenge 2022, team "Heart2Beat", rank 12 / 40).
SIGNIFICANCE
To the best of our knowledge, we are the first to demonstrate the usefulness of MIL in PCG classification. Also, we showcase how the explainability of the model can be analyzed quantitatively, thus avoiding confirmation bias inherent to many post-hoc methods. Finally, our overall results demonstrate the merit of employing MIL combined with handcrafted features for the generation of explainable features as well as for a competitive classification performance.
PubMed: 38502666
DOI: 10.1371/journal.pdig.0000461 -
Tropical Medicine and Infectious Disease Apr 2024Infective endocarditis (IE) is characterised by fever, heart murmurs, and emboli. Splenic emboli are frequent in left-sided IE. A systematic review of the literature... (Review)
Review
Infective endocarditis (IE) is characterised by fever, heart murmurs, and emboli. Splenic emboli are frequent in left-sided IE. A systematic review of the literature published on splenic embolism (SE) between 2000 and 2023 was conducted. Search strategies in electronic databases identified 2751 studies published between 1 January 2000 and 4 October 2023, of which 29 were finally included. The results showed that the imaging tests predominantly used to detect embolisms were computed tomography (CT), magnetic resonance imaging, positron emission tomography (PET)/CT, single-photon emission computed tomography/CT, ultrasound, and contrast-enhanced ultrasound. More recent studies typically used F-FDG PET-CT. The proportion of SE ranged from 1.4% to 71.7%. Only seven studies performed systematic conventional CT screening for intra-abdominal emboli, and the weighted mean frequency of SE was 22% (range: 8-34.8%). F-FDG PET-CT was performed systematically in seven studies, and splenic uptake was found in a weighted mean of 4.5%. There was a lack of uniformity in the published literature regarding the frequency and management of splenic embolisation. CT scans were the most frequently used method, until recently, when F-FDG PET-CT scans began to predominate. More data are necessary regarding the frequency of SE, especially focusing on their impact on IE management and prognosis.
PubMed: 38668544
DOI: 10.3390/tropicalmed9040083