郭涛
云南省阜外心血管病医院 心内科
BACKGROUND International studies have shown that use of a subcutaneous implantable cardioverter defibrillator (S-ICD) could reduce lead-related complications while maintaining adequate defibrillation performance; however, data from the Chinese population or other Asian groups are limited. MATERIAL AND METHODS SCOPE is a prospective, multicenter, observational cohort study. Two hundred patients with primary prevention indication for sudden cardiac death (SCD), who are candidates for S-ICD, will be enrolled. From the same population, another 200 patients who are candidates for transvenous implantable cardioverter defibrillator (TV-ICD) will be enrolled after being matched for age, sex, SCD high-risk etiology (ischemic cardiomyopathy, and non-ischemic cardiomyopathy, ion channel disease, and other) and atrial fibrillation in a 1: 1 ratio with enrolled S-ICD patients. All the patients will be followed for 18 months under standard of care. RESULTS The primary endpoint is proportion of patients free from inappropriate shock (IAS) at 18 months in the S-ICD group. The lower 95% confidence bound of the proportion will be compared with a performance goal of 90.3%, which was derived from the previous meta-analysis. The comparisons between S-ICD and TV-ICD on IAS, appropriate shock, and complications will be used as secondary endpoints without formal assumptions. CONCLUSIONS This is the first prospective multicenter study focusing on the long-term performance of S-ICD in a Chinese population. By comparing with the data derived from international historical studies and a matched TV-ICD group, data from SCOPE will allow for the assessment of S-ICD in the Chinese population in a contemporary real-world implantation level and programming techniques, which will help us to further modify the device implantation and programming protocol in this specific population in the future.
Medical science monitor : international medical journal of experimental and clinical research 2024
The multi-window time-frequency reassignment helps to improve the time-frequency resolution of bark-frequency spectral coefficient (BFSC) analysis of heart sounds. For this purpose, a new heart sound classification algorithm combining feature extraction based on multi-window time-frequency reassignment BFSC with deep learning was proposed in this paper. Firstly, the randomly intercepted heart sound segments are preprocessed with amplitude normalization, the heart sounds were framed and time-frequency rearrangement based on short-time Fourier transforms were computed using multiple orthogonal windows. A smooth spectrum estimate is calculated by arithmetic averaging each of the obtained independent spectra. Finally, the BFSC of reassignment spectrum is extracted as a feature by the Bark filter bank. In this paper, convolutional network and recurrent neural network are used as classifiers for model comparison and performance evaluation of the extracted features. Eventually, the multi-window time-frequency rearrangement improved BFSC method extracts more discriminative features, with a binary classification accuracy of 0.936, a sensitivity of 0.946, and a specificity of 0.922. These results present that the algorithm proposed in this paper does not need to segment the heart sounds and randomly intercepts the heart sound segments, which greatly simplifies the computational process and is expected to be used for screening of congenital heart disease.
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 2024
BACKGROUND:Dual-phase cardiac computed tomography (CCT) has been applied to detect left atrial appendage (LAA) thrombosis, which is characterized as the presence of left atrial appendage filling defects (LAADF) in both early- and delayed-phase scanning. However, the clinical implication of LAAFD in exclusive early-phase scanning (LAAFD-EEpS) of CCT in patients with atrial fibrillation (AF) is unclear.METHODS:The baseline clinical data and dual-phase CCT findings in 1183 AF patients (62.1 ± 11.6 years, 59.9% male) was collected and analyzed. A further analysis of CCT and transesophageal echocardiography (TEE) data (within 5 days) in a subgroup of 687 patients was performed. LAAFD-EEpS was defined as LAAFD present in early-phase and absent in delayed-phase scanning of dual-phase CCT.RESULTS:A total of 133 (11.2%) patients were detected with LAAFD-EEpS. Patients with LAAFD-EEpS had a higher prevalence of ischemic stroke or transient ischemic attack (TIA) (p < 0.001) and a higher predefined thromboembolic risk (p < 0.001). In multivariate analysis, a history of ischemic stroke or TIA was independently associated with LAAFD-EEpS (odds ratio [OR] 11.412, 95% confidence interval [CI] 6.561-19.851, p < 0.001). When spontaneous echo contrast in TEE was used as the reference standard, the sensitivity, specificity, positive predictive value, and negative predictive value of LAAFD-EEpS was 77.0% (95% CI 66.5-87.6%), 89.0% (95% CI 86.5-91.4%), 40.5% (95% CI 31.6-49.5%), 97.5% (96.3-98.8%), respectively.CONCLUSIONS:In AF patients, LAAFD-EEpS is not an uncommon finding in dual-phase CCT scanning, and is associated with elevated thromboembolic risk.
Cardiology journal 2024
Aiming at the problems of obscure clinical auscultation features of pulmonary hypertension associated with congenital heart disease and the complexity of existing machine-aided diagnostic algorithms, an algorithm based on the statistical characteristics of the high-frequency components of the second heart sound signal is proposed. Firstly, an endpoint detection adaptive segmentation method is employed to extract the second heart sounds. Subsequently, the high-frequency component of the heart sound is decomposed using the discrete wavelet transform. Statistical features including the Hurst exponent, Lempel-Ziv information and sample entropy are extracted from this component. Finally, the extracted features are utilized to train an extreme gradient boosting algorithm (XGBoost) classifier, which achieves an accuracy of 80.45% in triple classification. Notably, this method eliminates the need for a noise reduction algorithm, allows for swift feature extraction, and achieves effective multi-classification using only three features. It is promising for early screening of pulmonary hypertension associated with congenital heart disease.
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 2024
Feature extraction methods and classifier selection are two critical steps in heart sound classification. To capture the pathological features of heart sound signals, this paper introduces a feature extraction method that combines mel-frequency cepstral coefficients (MFCC) and power spectral density (PSD). Unlike conventional classifiers, the adaptive neuro-fuzzy inference system (ANFIS) was chosen as the classifier for this study. In terms of experimental design, we compared different PSDs across various time intervals and frequency ranges, selecting the characteristics with the most effective classification outcomes. We compared four statistical properties, including mean PSD, standard deviation PSD, variance PSD, and median PSD. Through experimental comparisons, we found that combining the features of median PSD and MFCC with heart sound systolic period of 100-300 Hz yielded the best results. The accuracy, precision, sensitivity, specificity, and F1 score were determined to be 96.50%, 99.27%, 93.35%, 99.60%, and 96.35%, respectively. These results demonstrate the algorithm's significant potential for aiding in the diagnosis of congenital heart disease.
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 2023
INTRODUCTION:Optimal occlusion of pulmonary vein (PV) is essential for atrial fibrillation (AF) cryoballoon ablation (CBA). The aim of the study was to investigate the performance of two different tools for the assessment of PV occlusion with a novel navigation system in CBA procedure.METHODS:In consecutive patients with paroxysmal AF who underwent CBA procedure with the guidance of the novel 3-dimentional mapping system, the baseline tool, injection tool and pulmonary venography were all employed to assess the degree of PV occlusion, and the corresponding cryoablation parameters were recorded.RESULTS:In 23 patients (mean age 60.0 ± 13.9 years, 56.5% male), a total of 149 attempts of occlusion and 122 cryoablations in 92 PVs were performed. Using pulmonary venography as the gold standard, the overall sensitivity, specificity of the baseline tool was 96.7% (95% confidence interval [CI] 90.0%-99.1%), and 40.5% (95% CI 26.0%-56.7%), respectively, while the corresponding value of the injection tool was 69.6% (95% CI 59.7%-78.1%), and 100.0% (95% CI 90.6%-100.0%), respectively. Cryoablation with optimal occlusion showed lower nadir temperature (baseline tool: -44.3 ± 8.4°C vs. -35.1 ± 6.5°C, p < .001; injection tool: -46.7 ± 6.4°C vs. -38.3 ± 9.2°C, p < .001) and longer total thaw time (baseline tool: 53.3 ± 17.0 s vs. 38.2 ± 14.9 s, p = .003; injection tool: 58.5 ± 15.5 s vs. 41.7 ± 15.2 s, p < .001) compared with those without.CONCLUSIONS:Both tools were able to accurately assess the degree of PV occlusion and predict the acute cryoablation effect, with the baseline tool being more sensitive and the injection tool more specific.
Journal of cardiovascular electrophysiology 2023
BACKGROUND:To evaluate the application value of artificial intelligence-based auxiliary diagnosis for congenital heart disease.METHODS:From May 2017 to December 2019, 1892 cases of congenital heart disease heart sounds were collected for learning- and memory-assisted diagnosis. The diagnosis rate and classification recognition were verified in 326 congenital heart disease cases. Auscultation and artificial intelligence-assisted diagnosis were used in 518 258 congenital heart disease screenings, and the detection accuracies of congenital heart disease and pulmonary hypertension were compared.RESULTS:Female sex and age > 14 years were predominant in atrial septal defect (P <.001) compared with ventricular septal defect/patent ductus arteriosus cases. Family history was more prominent in patent ductus arteriosus patients (P <.001). Compared with no pulmonary arterial hypertension, a male predominance was seen in cases of congenital heart disease-pulmonary arterial hypertension (P <.001), and age was significantly associated with pulmonary arterial hypertension (P =.008). A high prevalence of extracardiac anomalies was found in the pulmonary arterial hypertension group. A total of 326 patients were examined by artificial intelligence. The detection rate of atrial septal defect was 73.8%, which was different from that of auscultation (P =.008). The detection rate of ventricular septal defect was 78.8, and the detection rate of patent ductus arte-riosus was 88.9%. A total of 518 258 people from 82 towns and 1220 schools were screened including 15 453 suspected and 3930 (7.58%) confirmed cases. The detection accuracy of artificial intelligence in ventricular septal defect (P =.007) and patent ductus arteriosus (P =.021) classification was higher than that of auscultation. For normal cases, the recurrent neural network had a high accuracy of 97.77% in congenital heart disease-pulmonary arterial hypertension diagnosis (P =.032).CONCLUSION:Artificial intelligence-based diagnosis is an effective assistance method for congenital heart disease screening.
Anatolian journal of cardiology 2023
Introduction: Congenital heart disease (CHD) is a cardiovascular disorder caused by structural defects in the heart. Early screening holds significant importance for the effective treatment of this condition. Heart sound analysis is commonly employed to assist in the diagnosis of CHD. However, there is currently a lack of an efficient automated model for heart sound classification, which could potentially replace the manual process of auscultation. Methods: This study introduces an innovative and efficient screening and classification model, combining a locally concatenated fusion approach with a convolutional neural network based on coordinate attention (LCACNN). In this model, Mel-frequency spectral coefficients (MFSC) and envelope features are locally fused and employed as input to the LCACNN network. This model automatically analyzes feature map energy information, eliminating the need for denoising processes. Discussion: The proposed classification model in this study demonstrates a robust capability for identifying congenital heart disease, potentially substituting manual auscultation to facilitate the detection of patients in remote areas. Results: This study introduces an innovative and efficient screening and classification model, combining a locally concatenated fusion approach with a convolutional neural network based on coordinate attention (LCACNN). In this model, Mel-frequency spectral coefficients (MFSC) and envelope features are locally fused and employed as input to the LCACNN network. This model automatically analyzes feature map energy information, eliminating the need for denoising processes. To assess the performance of the classification model, comparative ablation experiments were conducted, achieving classification accuracies of 91.78% and 94.79% on the PhysioNet and HS databases, respectively. These results significantly outperformed alternative classification models.
Frontiers in physiology 2023
Pulmonary arterial hypertension associated with congenital heart disease (CHD-PAH) is a fatal cardiovascular disease. A novel method for non-invasive initial diagnosis of the CHD-PAH was put forward in this work. First, original heart sounds were segmented into each cardiac cycle by using double-threshold adaptive method. According to clinical auscultation, the pathological information of CHD-PAH is concentrated in S2, so the time-frequency features in both of an entire cardiac cycle and S2 were extracted. Then the time-frequency features combine with the deep learning features to form a feature vector. It is the fusion feature, which will be input into a classifier. Finally, the majority voting algorithm was used to obtain the optimal classification results. A classification accuracy of 88.61% was achieved using this novel method. Three points are essential: •A double-threshold adaptive method is used to segment heart sound into each cardiac cycle.•The time-frequency domain features in both of an entire cardiac cycle and S2 were extracted, which are combined with deep learning features to form the fusion feature.•The XGBoost was used as three-class classifier for the classification of normal, CHD and CHD-PAH. The majority voting algorithm was used to obtain the optimal classification results.
MethodsX 2023
To clarify the mechanisms underlying TRPV4 regulating angiogenesis by enhancing the activity of CACs, we detected the angiogenesis ability of HUVEC co-cultured with CACs, the effects of ILK on TRPV4 expression and CACs activity, and the impacts of TRPV4 agonist or inhibitor on cardio-protection of AMI rats with or without CAC transplantation. ILK overexpression or TRPV4 agonist promoted the angiogenesis in HUVEC co-cultured with CACs. ILK overexpression or activation upregulated TRPV4 expression in CACs, while TRPV4 agonist stimulation also regulated ILK expression. TRPV4 agonist effectively improved the myocardial function of AMI rats. Moreover, this effect could be strengthened when combined with CAC transplantation, as CAC transplantation dramatically upregulated the expression of ILK and TRPV4 in heart tissues of AMI rats. Thus, the application of TRPV4 agonist may maintain the activity of CACs to promote angiogenesis and microcirculation reconstruction in the area of myocardial infarction and substantially improve the therapeutic effect.
Journal of cardiovascular translational research 2023
