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Annual International Conference of the... Jul 2022Cardiac auscultation is the key exam to screen cardiac diseases both in developed and developing countries. A heart sound auscultation procedure can detect the presence...
Cardiac auscultation is the key exam to screen cardiac diseases both in developed and developing countries. A heart sound auscultation procedure can detect the presence of murmurs and point to a diagnosis, thus it is an important first-line assessment and also cost-effective tool. The design automatic recommendation systems based on heart sound auscultation can play an important role in boosting the accuracy and the pervasiveness of screening tools. One such as step, consists in detecting the fundamental heart sound states, a process known as segmentation. A faulty segmentation or a wrong estimation of the heart rate might result in an incapability of heart sound classifiers to detect abnormal waves, such as murmurs. In the process of understanding the impact of a faulty segmentation, several common heart sound segmentation errors are studied in detail, namely those where the heart rate is badly estimated and those where S1/S2 and Systolic/Diastolic states are swapped in comparison with the ground truth state sequence. From the tested algorithms, support vector machine (SVMs) and random forest (RFs) shown to be more sensitive to a wrong estimation of the heart rate (an expected drop of 6% and 8% on the overall performance, respectively) than to a swap in the state sequence of events (an expected drop of 1.9% and 4.6%, respectively).
Topics: Algorithms; Heart Auscultation; Heart Murmurs; Heart Sounds; Humans; Support Vector Machine
PubMed: 36086341
DOI: 10.1109/EMBC48229.2022.9871111 -
Circulation. Cardiovascular Imaging Apr 2022The echocardiographic assessment of left ventricular (LV) diastolic dysfunction (LVDD) in patients with hypertrophic cardiomyopathy is complex and not well-established....
BACKGROUND
The echocardiographic assessment of left ventricular (LV) diastolic dysfunction (LVDD) in patients with hypertrophic cardiomyopathy is complex and not well-established. We investigated whether the left atrial reservoir strain (LARS) could be used to categorize LVDD and whether this grading is predictive of heart failure (HF) events in hypertrophic cardiomyopathy.
METHODS
A total of 414 patients with hypertrophic cardiomyopathy (aged 58.3±12.8 years; 65.7% male) were categorized using LARS-defined LVDD (LARS-DD) grades: ≥35% (grade 0), ≥24% to <35%, ≥19% to <24%, and <19% (grade 3). Patients were followed for a median of 6.9 years to assess hospitalization for HF or HF-related death.
RESULTS
An increase in LARS-DD grade was associated with worse conventional echocardiographic parameters of LVDD, such as lower e', higher E/e' ratio, greater maximum tricuspid regurgitation velocity, and restrictive mitral inflow pattern. Higher LARS-DD grade was also associated with parameters reflecting increased LV filling pressure, such as greater LV wall thickness, greater extent of fibrosis, obstructive physiology, and decreased LV longitudinal strain. Furthermore, higher LARS-DD grade was associated with worse HF-free survival (log-rank <0.001). Patients with LARS-DD grades 0, 1, 2, and 3 showed 10-year HF-free survival of 100%, 91.6%, 84.1%, and 67.5%, respectively. LARS-DD grade was an independent predictor of HF events after adjusting for clinical and echocardiographic variables (hazard ratio, 1.53 [95% CI, 1.03-2.28], per 1-grade increase). The LARS-DD grade also had incremental prognostic value for incident HF events over the traditional echocardiographic LVDD parameters and grading system. The prognostic value of advanced LARS-DD grade was consistent in sensitivity analyses and various patient subgroups.
CONCLUSIONS
LARS can be used as a simple single or supplemental index to categorize LV diastolic function and predict HF events in hypertrophic cardiomyopathy.
Topics: Atrial Function, Left; Cardiomyopathy, Hypertrophic; Diastole; Female; Heart Failure; Heart Murmurs; Humans; Male; Ventricular Dysfunction, Left; Ventricular Function, Left
PubMed: 35439039
DOI: 10.1161/CIRCIMAGING.121.013556 -
MedEdPORTAL : the Journal of Teaching... 2022Physicians need adequate physical exam skills. Unfortunately, interns have variable physical exam skills, and teaching is often limited to rounds, an inconsistent...
INTRODUCTION
Physicians need adequate physical exam skills. Unfortunately, interns have variable physical exam skills, and teaching is often limited to rounds, an inconsistent setting. Physical exam skills, particularly those involving auscultation, require practice. Our goal was to create a cardiac physical exam workshop for pediatric interns that would improve their performance on an interactive assessment of their ability and understanding in physical exam and murmur interpretation.
METHODS
We completed a targeted needs assessment and then developed a 2-hour workshop on the pediatric cardiac physical exam targeted to pediatrics residents. The workshop included didactics, group discussion, and practice interpreting common pediatric murmurs. Pediatrics residents completed the assessment as a pretest and then participated in the workshop. At the end of the workshop, the assessment was administered as a posttest, followed by a reassessment 3 months later. Nonparametric statistical analysis was conducted. Pre- and posttest scores were compared using the Wilcoxon signed rank test.
RESULTS
Twenty-five residents completed the workshop, including 22 pediatrics residents, one pediatrics/anesthesia combined resident, one pediatric neurology resident, and one resident completing a preliminary year in pediatrics prior to dermatology residency. There was a significant increase in the mean score on the assessment from pre- to posttest (pretest = 54%, posttest = 71%, < .001). This increase was sustained at the 3-month reassessment ( = 67%).
DISCUSSION
This cardiac physical exam workshop demonstrated improvement in physical exam knowledge and interpretation ability as measured by an online pre-/posttest.
Topics: Child; Humans; Internship and Residency; Clinical Competence; Physical Examination; Heart Murmurs; Auscultation
PubMed: 36605544
DOI: 10.15766/mep_2374-8265.11289 -
Journal of Paediatrics and Child Health May 2020
Topics: Anxiety; Child; Echocardiography; Heart Murmurs; Humans; Parents
PubMed: 32416048
DOI: 10.1111/jpc.14893 -
Journal of Paediatrics and Child Health Apr 2020
Topics: Anxiety; Child; Echocardiography; Heart Murmurs; Humans; Parents
PubMed: 32307779
DOI: 10.1111/jpc.14854 -
International Journal of Environmental... Oct 2021Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD)...
Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD) diagnostics. The cardiac auscultation using the classical stethoscope phonological cardiogram is known as the most famous exam method to detect heart anomalies. This exam requires a qualified cardiologist, who relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in the heart during the pumping action. Phonocardiogram (PCG) signal represents the recording of sounds and murmurs resulting from the heart auscultation, typically with a stethoscope, as a part of medical diagnosis. For the sake of helping physicians in a clinical environment, a range of artificial intelligence methods was proposed to automatically analyze PCG signal to help in the preliminary diagnosis of different heart diseases. The aim of this research paper is providing an accurate CVD recognition model based on unsupervised and supervised machine learning methods relayed on convolutional neural network (CNN). The proposed approach is evaluated on heart sound signals from the well-known, publicly available PASCAL and PhysioNet datasets. Experimental results show that the heart cycle segmentation and segment selection processes have a direct impact on the validation accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR). Based on PASCAL dataset, we obtained encouraging classification results with overall accuracy 0.87, overall precision 0.81, and overall sensitivity 0.83. Concerning Micro classification results, we obtained Micro accuracy 0.91, Micro sensitivity 0.83, Micro precision 0.84, and Micro specificity 0.92. Using PhysioNet dataset, we achieved very good results: 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity.
Topics: Algorithms; Artificial Intelligence; Cardiovascular Diseases; Heart Rate; Heart Sounds; Humans; Neural Networks, Computer
PubMed: 34682696
DOI: 10.3390/ijerph182010952 -
Chest Apr 2022Multiparametric risk assessment is used in pulmonary arterial hypertension (PAH) to target therapy. However, this strategy is imperfect because most patients remain at...
BACKGROUND
Multiparametric risk assessment is used in pulmonary arterial hypertension (PAH) to target therapy. However, this strategy is imperfect because most patients remain at intermediate or high risk after initial treatment, with low risk being the goal. Metrics of right ventricular (RV) adaptation are promising tools that may help refine our therapeutic strategy.
RESEARCH QUESTION
Does RV adaptation predict therapeutic response over time?
STUDY DESIGN AND METHODS
We evaluated 52 incident treatment-naive patients with advanced PAH by catheterization and cardiac imaging longitudinally at baseline, follow-up 1 (∼3 months), and follow-up 2 (∼18 months). All patients received goal-directed therapy with parenteral treprostinil and/or combination therapy with treatment escalation if functional class I or II was not achieved. On the basis of their therapeutic response, patients were evaluated at follow-up 1 as nonresponders (died) or as responders, and again at follow-up 2 as super-responders (low risk) or partial responders (high/intermediate risk). Multiparametric risk was based on a simplified European Respiratory Society/European Society of Cardiology guideline score. RV adaptation was evaluated with the single-beat coupling ratio (Ees/Ea) and diastolic function with diastolic elastance (Eed). Data are expressed as mean ± SD or as OR (95% CI).
RESULTS
Nine patients (17%) were nonresponders. PAH-directed therapy improved the European Respiratory Society low-risk score from 1 (2%) at baseline to 23 (55%) at follow-up 2. Ees/Ea at presentation was nonsignificantly higher in responders (0.9 ± 0.4) vs nonresponders (0.6 ± 0.4; P = .09) but could not be used to predict super-responder status at follow-up 2 (OR, 1.40 [95% CI, 0.28-7.0]; P = .84). Baseline RV ejection fraction and change in Eed were successfully used to predict super-responder status at follow-up 2 (OR, 1.15 [95% CI, 1.0-1.27]; P = .009 and OR, 0.29 [95% CI, 0.86-0.96]; P = .04, respectively).
INTERPRETATION
In patients with advanced PAH, RV-pulmonary arterial coupling could not discriminate irreversible RV failure (nonresponders) at presentation but showed a late trend to improvement by follow-up 2. Early change in Eed and baseline RV ejection fraction were the best predictors of therapeutic response.
Topics: Familial Primary Pulmonary Hypertension; Heart Murmurs; Humans; Hypertension, Pulmonary; Prospective Studies; Pulmonary Arterial Hypertension; Pulmonary Artery; Ventricular Dysfunction, Right; Ventricular Function, Right
PubMed: 34637777
DOI: 10.1016/j.chest.2021.09.040 -
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 -
Journal of Cardiology Nov 2022The effectiveness of cardiac auscultation training with a cardiology patient simulator for medical students is still unclear. Starting such training earlier may help...
BACKGROUND
The effectiveness of cardiac auscultation training with a cardiology patient simulator for medical students is still unclear. Starting such training earlier may help students improve their proficiency. We investigated whether cardiac auscultation training using a simulator for first-year students is feasible and effective.
METHODS
A total of 43 first-year medical students (5-12 in each year, 2015-2019) participated in three 1.5-hour extra-curricular classes comprising mini-lectures, facilitated training, two different auscultation tests (the second test closer to clinical setting than the first), and a questionnaire. The test results were compared with those of 556 fourth-year medical students who participated in a compulsory 3-hour cardiac auscultation class in 2016-2019.
RESULTS
The accuracy rate of all heart sounds and murmurs was higher in the first-year students than in the fourth-year students in both the first (85.8 vs. 79.4 %, p = 0.001) and second (71.3 vs. 61.2 %, p = 0.02) tests. That of second/third/fourth sounds was also higher in the first-year students than in the fourth-year students in both the first (86.0 vs. 79.7 %, p = 0.01) and second (70.9 vs. 53.9 %, p = 0.002) tests. The accuracy rate of murmurs was higher in the first-year students than in the fourth-year students in the first test (85.5 vs. 78.9 %, p = 0.04), but not in the second test (72.1 vs. 75.7 %, p = 0.58). All the first-year students and 65 % of them agreed that they had received sufficient knowledge and built sufficient skills, respectively. All the first-year students and 93 % of them agreed that they were satisfied with the program, and that the program was suitable for first-year students, respectively.
CONCLUSIONS
Although training time was different between the two groups and it is possible that only motivated first-year students participated in the program, these results suggest that our cardiac auscultation training is feasible and effective for first-year medical students.
Topics: Cardiology; Clinical Competence; Feasibility Studies; Heart Auscultation; Heart Murmurs; Humans; Students, Medical
PubMed: 35750554
DOI: 10.1016/j.jjcc.2022.06.007 -
Micromachines Dec 2022In light of a need for low-frequency, high sensitivity and broadband cardiac murmur signal detection, the present work puts forward an integrated MEMS-based heart sound...
In light of a need for low-frequency, high sensitivity and broadband cardiac murmur signal detection, the present work puts forward an integrated MEMS-based heart sound sensor with a hollow concave ciliary micro-structure. The advantages of a hollow MEMS structure, in contrast to planar ciliated micro-structures, are that it reduces the ciliated mass and enhances the operating bandwidth. Meanwhile, the area of acoustic-wave reception is enlarged by the concave architecture, thereby enhancing the sensitivity at low frequencies. By rationally designing the acoustic encapsulation, the loss of heart acoustic distortion and weak cardiac murmurs is reduced. As demonstrated by experimentation, the proposed hollow MEMS structure cardiac sound sensor has a sensitivity of up to -206.9 dB at 200 Hz, showing 6.5 dB and 170 Hz increases in the sensitivity and operating bandwidth, respectively, in contrast to the planar ciliated MEMS sensor. The SNR of the sensor is 26.471 dB, showing good detectability for cardiac sounds.
PubMed: 36557472
DOI: 10.3390/mi13122174