-
The Canadian Veterinary Journal = La... Jan 2024Cardiovascular dysfunction associated with acute kidney injury has been recently described in veterinary medicine, but limited information is available for cats with...
BACKGROUND
Cardiovascular dysfunction associated with acute kidney injury has been recently described in veterinary medicine, but limited information is available for cats with urinary tract obstruction (UTO).
OBJECTIVE
This retrospective study aimed to describe the type, frequency, timeline, and risk factors for cardiovascular events (CVEs) in cats treated for acute UTO.
ANIMALS AND PROCEDURES
Medical records of cats admitted to the intensive care unit for either upper (ureteral: UUTO) or lower (urethral: LUTO) UTO from 2016 to 2021 were reviewed. Cardiovascular events were defined as development of arrhythmia, heart murmur or gallop sound, clinical signs consistent with fluid overload (CRFO), or decreased tissue perfusion (DTP).
RESULTS
One hundred and sixty-eight cats with UTO were recruited (56 with UUTO and 112 with LUTO). Cardiovascular events were reported in 61.9% of cases, including arrhythmia (33.6%), gallop rhythm (28.1%), heart murmur (15.3%), CRFO (14.4%), and DTP (8.6%). Potassium concentration, preexisting chronic kidney disease, and renal pelvic dilation at abdominal ultrasonography were associated with CVE occurrence in multivariate analysis.
CONCLUSIONS
This study highlighted frequent CVEs in cats treated for UTO, with a potential strong impact on outcome. Therefore, cardiovascular parameters of cats with preexisting chronic kidney disease or those admitted with hyperkalemia or renal pelvic dilation should be closely monitored.
Topics: Cats; Animals; Retrospective Studies; Urethral Diseases; Kidney; Renal Insufficiency, Chronic; Arrhythmias, Cardiac; Heart Murmurs; Cardiovascular Diseases; Cat Diseases; Urethral Obstruction; Ureteral Obstruction
PubMed: 38164379
DOI: No ID Found -
Journal of the American Heart... Sep 2021Background Echocardiography is considered the cornerstone of the diagnostic workup of heart failure with preserved ejection fraction. Thus far, validation of the 2016...
Background Echocardiography is considered the cornerstone of the diagnostic workup of heart failure with preserved ejection fraction. Thus far, validation of the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) echo-algorithm for evaluation of diastolic (dys)function in a patient suspected of heart failure with preserved ejection fraction has been limited. Methods and Results The diagnostic performance of the 2016 ASE/EACVI algorithm was assessed in 204 patients evaluated for unexplained dyspnea or pulmonary hypertension with echocardiogram and right heart catheterization. Invasively measured pulmonary capillary wedge pressure (PCWP) was used as the gold standard. In addition, the diagnostic performance of HFPEF score and NT-proBNP (N-terminal pro-B-type natriuretic peptide) were evaluated. There was a poor correlation between indexed left atrial volume, E/e' (septal and average) or early mitral inflow (E), and PCWP (=0.25-0.30, values all <0.01). No correlation was found in our cohort between e' (septal or lateral) or tricuspid valve regurgitation and PCWP. The correlation between diastolic function grades of the ASE/EACVI algorithm and PCWP was poor (=0.17, <0.05). The ASE/EACVI algorithm had a sensitivity and specificity of 35% and 87%, respectively; an accuracy of 67% and an area under the curve of 0.56. Moreover, in 30% of cases the algorithm was not applicable or indeterminate. HFPEF score had a modest correlation with PCWP (=0.44, <0.0001), and accuracy was 73%; NT-proBNP correlated weakly with PCWP (=0.24, <0.001), and accuracy was 57%. Conclusions The 2016 ASE/EACVI algorithm for the assessment of diastolic function has a limited diagnostic accuracy in patients evaluated for unexplained dyspnea and/or pulmonary hypertension, and especially sensitivity to detect diastolic dysfunction was low.
Topics: Algorithms; Diastole; Dyspnea; Heart Failure; Heart Murmurs; Humans; Hypertension, Pulmonary; Stroke Volume; Ventricular Function, Left
PubMed: 34476984
DOI: 10.1161/JAHA.121.021165 -
Journal of Cardiology Cases Jul 2022Infective endocarditis (IE) is not a common disease, but it remains a serious condition. Antineutrophil cytoplasmic antibodies (ANCA) are often positive in IE, and...
UNLABELLED
Infective endocarditis (IE) is not a common disease, but it remains a serious condition. Antineutrophil cytoplasmic antibodies (ANCA) are often positive in IE, and discrimination between IE and ANCA-associated vasculitis is important because misdirected selection of therapy can lead to catastrophic consequences. We report a case of IE mimicking ANCA-associated vasculitis in which we were able to make a correct diagnosis and perform treatment. This case suggests that it is important to consider IE as a differential diagnosis in ANCA-positive patients.
LEARNING OBJECTIVE
Antineutrophil cytoplasmic antibodies (ANCA) are associated with primary systemic vasculitis. However, ANCA have also been described in other conditions and infective endocarditis (IE) was considered an important cause of ANCA.Discrimination between IE and ANCA-associated vasculitis is important, although it is sometimes difficult. We report a case of IE mimicking ANCA-associated vasculitis. ANCA-positive patients with nonspecific symptoms should be suspected of having IE, checked for heart murmurs, and tested by echocardiography and blood cultures.
PubMed: 35923533
DOI: 10.1016/j.jccase.2022.02.001 -
IEEE Journal of Biomedical and Health... Jun 2022Cardiac auscultation is one of the most cost-effective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on...
Cardiac auscultation is one of the most cost-effective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can support physicians in their decisions. Unfortunately, the application of such systems in clinical trials is still minimal since most of them only aim to detect the presence of extra or abnormal waves in the phonocardiogram signal, i.e., only a binary ground truth variable (normal vs abnormal) is provided. This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e.g., cardiac murmurs) exists. To pave the way to more effective research on healthcare recommendation systems based on auscultation, our team has prepared the currently largest pediatric heart sound dataset. A total of 5282 recordings have been collected from the four main auscultation locations of 1568 patients, in the process, 215780 heart sounds have been manually annotated. Furthermore, and for the first time, each cardiac murmur has been manually annotated by an expert annotator according to its timing, shape, pitch, grading, and quality. In addition, the auscultation locations where the murmur is present were identified as well as the auscultation location where the murmur is detected more intensively. Such detailed description for a relatively large number of heart sounds may pave the way for new machine learning algorithms with a real-world application for the detection and analysis of murmur waves for diagnostic purposes.
Topics: Algorithms; Auscultation; Child; Heart Auscultation; Heart Murmurs; Heart Sounds; Humans
PubMed: 34932490
DOI: 10.1109/JBHI.2021.3137048 -
BMC Medical Education Dec 2021We have provided fourth-year medical students with a three-hour cardiac auscultation class using a cardiology patient simulator since 2010. The test results of 2010-2012...
Employment of color Doppler echocardiographic video clips in a cardiac auscultation class with a cardiology patient simulator: discrepancy between students' satisfaction and learning.
BACKGROUND
We have provided fourth-year medical students with a three-hour cardiac auscultation class using a cardiology patient simulator since 2010. The test results of 2010-2012 revealed that as compared with aortic stenosis murmur, students correctly identified murmurs of other valvular diseases less often. We investigated whether employment of color Doppler echocardiographic video clips would improve proficiency in identifying murmurs of aortic regurgitation and mitral regurgitation, and whether students' favorable responses to a questionnaire were associated with improved proficiency.
METHODS
A total of 250 fourth-year medical students were divided into groups of 7-9 students in 2014 and 2015. Each group attended a three-hour cardiac auscultation class comprising a mini-lecture, facilitated training, two different auscultation tests (the second test being closer to clinical setting than the first) and a questionnaire. We provided each student with color Doppler echocardiographic videos of aortic regurgitation and mitral regurgitation using a tablet computer, which they freely referred to before and after listening to corresponding murmurs. The test results were compared with those in 2010-2012. The students had already completed the course of cardiovascular medicine, comprising lectures including those of physical examination, echocardiography, and valvular heart diseases, before participating in this auscultation training class.
RESULTS
Most students indicated that the videos were useful or somewhat useful regarding aortic regurgitation (86.3%) and mitral regurgitation (85.7%). The accuracy rates were 78.4% (81.2% in 2010-2012) in aortic regurgitation and 76.0% (77.8%) in mitral regurgitation in the first test, and 83.3% (71.4%) in aortic regurgitation and 77.1% (77.6%) in mitral regurgitation in the second test, showing no significant differences as compared to 2010-2012. Overall accuracy rate of all heart sounds and murmurs in the first test and that of second/third/fourth sounds in the first and second tests were significantly lower in 2014-2015 than in 2010-2012.
CONCLUSIONS
Referring to color Doppler echocardiographic video clips in the way employed in the present study, which most students regarded as useful, did not improve their proficiency in identifying the two important regurgitant murmurs, revealing a discrepancy between students' satisfaction and learning. Video clips synchronized with their corresponding murmurs may contribute toward improving students' proficiency.
Topics: Cardiology; Echocardiography; Employment; Heart Auscultation; Humans; Patient Satisfaction; Personal Satisfaction; Students, Medical
PubMed: 34872540
DOI: 10.1186/s12909-021-03033-8 -
Frontiers in Cardiovascular Medicine 2022Cardiac auscultation is a traditional method that is most frequently used for identifying congenital heart disease (CHD). Failure to diagnose CHD may occur in patients...
BACKGROUND
Cardiac auscultation is a traditional method that is most frequently used for identifying congenital heart disease (CHD). Failure to diagnose CHD may occur in patients with faint murmurs or obesity. We aimed to develop an intelligent diagnostic method of detecting heart murmurs in patients with ventricular septal defects (VSDs) and atrial septal defects (ASDs).
MATERIALS AND METHODS
Digital recordings of heart sounds and phonocardiograms of 184 participants were obtained. All participants underwent echocardiography by pediatric cardiologists to determine the type of CHD. The phonocardiogram data were classified as normal, ASD, or VSD. Then, the phonocardiogram signal was used to extract features to construct diagnostic models for disease classification using an advanced optical coherence tomography network (AOCT-NET). Cardiologists were asked to distinguish normal heart sounds from ASD/VSD murmurs after listening to the electronic sound recordings. Comparisons of the cardiologists' assessment and AOCT-NET performance were performed.
RESULTS
Echocardiography results revealed 88 healthy participants, 50 with ASDs, and 46 with VSDs. The AOCT-NET had no advantage in detecting VSD compared with cardiologist assessment. However, AOCT-NET performance was better than that of cardiologists in detecting ASD (sensitivity, 76.4 vs. 27.8%, respectively; specificity, 90 vs. 98.5%, respectively).
CONCLUSION
The proposed method has the potential to improve the ASD detection rate and could be an important screening tool for patients without symptoms.
PubMed: 36523363
DOI: 10.3389/fcvm.2022.1041082 -
Sensors (Basel, Switzerland) Jun 2024The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs...
The phonocardiogram (PCG) can be used as an affordable way to monitor heart conditions. This study proposes the training and testing of several classifiers based on SVMs (support vector machines), k-NN (k-Nearest Neighbor), and NNs (neural networks) to perform binary ("Normal"/"Pathologic") and multiclass ("Normal", "CAD" (coronary artery disease), "MVP" (mitral valve prolapse), and "Benign" (benign murmurs)) classification of PCG signals, without heart sound segmentation algorithms. Two datasets of 482 and 826 PCG signals from the Physionet/CinC 2016 dataset are used to train the binary and multiclass classifiers, respectively. Each PCG signal is pre-processed, with spike removal, denoising, filtering, and normalization; afterward, it is divided into 5 s frames with a 1 s shift. Subsequently, a feature set is extracted from each frame to train and test the binary and multiclass classifiers. Concerning the binary classification, the trained classifiers yielded accuracies ranging from 92.4 to 98.7% on the test set, with memory occupations from 92.7 kB to 11.1 MB. Regarding the multiclass classification, the trained classifiers achieved accuracies spanning from 95.3 to 98.6% on the test set, occupying a memory portion from 233 kB to 14.1 MB. The NNs trained and tested in this work offer the best trade-off between performance and memory occupation, whereas the trained k-NN models obtained the best performance at the cost of large memory occupation (up to 14.1 MB). The classifiers' performance slightly depends on the signal quality, since a denoising step is performed during pre-processing. To this end, the signal-to-noise ratio (SNR) was acquired before and after the denoising, indicating an improvement between 15 and 30 dB. The trained and tested models occupy relatively little memory, enabling their implementation in resource-limited systems.
Topics: Humans; Phonocardiography; Machine Learning; Signal Processing, Computer-Assisted; Algorithms; Neural Networks, Computer; Wearable Electronic Devices; Support Vector Machine
PubMed: 38931636
DOI: 10.3390/s24123853 -
BMC Infectious Diseases May 2023As a member of the HACEK group, Aggregatibacter segnis (A. segnis) is a fastidious Gram-negative coccobacillus that resides in the human oropharyngeal flora. Infective...
BACKGROUND
As a member of the HACEK group, Aggregatibacter segnis (A. segnis) is a fastidious Gram-negative coccobacillus that resides in the human oropharyngeal flora. Infective endocarditis caused by A. segnis is rarely reported.
CASE PRESENTATION
A 31-year-old male was admitted to our hospital for a 3-month history of intermittent high fever, chills, and chest distress. On presentation, he was febrile and tachycardic but otherwise with stable vital signs. Physical examination revealed systolic murmurs in the aortic and mitral valve areas. Pitting edema was evident in the lower extremities. Transthoracic echocardiography demonstrated multiple vegetations in the mitral and aortic valves. Severe regurgitation of the aortic valve and left heart dysfunction were also detected. With the suspicion of infective endocarditis and heart failure, we immediately performed microbiological tests and arranged the cardiac replacement surgery. Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry and metagenomic next-generation sequencing (mNGS) identified A. segnis from the bloodstream. While the surgical specimen culture was negative, the mNGS was positive for A. segnis. The patient was treated with ceftriaxone for four weeks and discharged. He remained clinically well, with laboratory results restored.
CONCLUSION
This is the first report of A. segnis infective endocarditis that combined MALDI-TOF and metagenomic next-generation sequencing in the diagnosis. The hypothesis-independent molecular techniques can outperform conventional tools to prevent diagnostic delay.
Topics: Male; Humans; Adult; Aggregatibacter segnis; Delayed Diagnosis; Endocarditis; Endocarditis, Bacterial; Heart Failure; Fever
PubMed: 37158846
DOI: 10.1186/s12879-023-08231-x -
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi =... Feb 2021Auscultation of heart sounds is an important method for the diagnosis of heart conditions. For most people, the audible component of heart sound are the first heart...
Auscultation of heart sounds is an important method for the diagnosis of heart conditions. For most people, the audible component of heart sound are the first heart sound (S1) and the second heart sound (S2). Different diseases usually generate murmurs at different stages in a cardiac cycle. Segmenting the heart sounds precisely is the prerequisite for diagnosis. S1 and S2 emerges at the beginning of systole and diastole, respectively. Locating S1 and S2 accurately is beneficial for the segmentation of heart sounds. This paper proposed a method to classify the S1 and S2 based on their properties, and did not take use of the duration of systole and diastole. S1 and S2 in the training dataset were transformed to spectra by short-time Fourier transform and be feed to the two-stream convolutional neural network. The classification accuracy of the test dataset was as high as 91.135%. The highest sensitivity and specificity were 91.156% and 92.074%, respectively. Extracting the features of the input signals artificially can be avoid with the method proposed in this article. The calculation is not complicated, which makes this method effective for distinguishing S1 and S2 in real time.
Topics: Diastole; Heart; Heart Sounds; Neural Networks, Computer; Rivers
PubMed: 33899438
DOI: 10.7507/1001-5515.201909071 -
Biomedical Engineering Online Mar 2023Heart auscultation is an easy and inexpensive tool for early diagnosis of congenital heart defects. In this regard, a simple device which can be used easily by...
BACKGROUND
Heart auscultation is an easy and inexpensive tool for early diagnosis of congenital heart defects. In this regard, a simple device which can be used easily by physicians for heart murmur detection will be very useful. The current study was conducted to evaluate the validity of a Doppler-based device named "Doppler Phonolyser" for the diagnosis of structural heart diseases in pediatric patients. In this cross-sectional study, 1272 patients under 16 years who were referred between April 2021 and February 2022, to a pediatric cardiology clinic in Mofid Children Hospital, Tehran, Iran, were enrolled. All the patients were examined by a single experienced pediatric cardiologist using a conventional stethoscope at the first step and a Doppler Phonolyser device at the second step. Afterward, the patient underwent trans-thoracic echocardiography, and the echocardiogram results were compared with the conventional stethoscope as well as the Doppler Phonolyser findings.
RESULTS
Sensitivity of the Doppler Phonolyser for detecting congenital heart defects was 90.5%. The specificity of the Doppler Phonolyser in detecting heart disease was 68.9% in compared with the specificity of the conventional stethoscope, which was 94.8%. Among the most common congenital heart defects in our study population, the sensitivity of the Doppler Phonolyser was 100% for detection of tetralogy of Fallot (TOF); In contrast, sensitivity of both the conventional stethoscope and the Doppler Phonolyser was relatively low for detecting atrial septal defect.
CONCLUSIONS
Doppler Phonolyser could be useful as a diagnostic tool for the detection of congenital heart defects. The main advantages of the Doppler Phonolyser over the conventional stethoscope are no need for operator experience, the ability to distinguish innocent murmurs from the pathologic ones and no effect of environmental sounds on the performance of the device.
Topics: Humans; Child; Heart Sounds; Cross-Sectional Studies; Sensitivity and Specificity; Iran; Heart Murmurs; Heart Defects, Congenital
PubMed: 36899353
DOI: 10.1186/s12938-023-01084-0