Restoration of LV function and reversal of LV remodeling can be achieved with successful elimination of tachycardia in the majority of patients. NT-proBNP level elevates in subjects with TCM and decreases sharply after ablation.

Background: We attempted to identify a regulatory competing endogenous RNA (ceRNA) network and a hub gene of Hypertrophic Cardiomyopathy (HCM).Methods: Microarray datasets of HCM tissue were obtained from NCBI Gene Expression Omnibus (GEO) database. The R package “limma” was used to identify differentially expressed genes. Online search databases were utilized to match the relation among differentially expressed long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs. Weighted correlation network analysis (WGCNA) was used to identify the correlations between key modules and HCM. STRING database was applied to construct PPI networks. Gene Set Enrichment Analysis (GSEA) was used to perform functional annotations and verified the hub genes.Results: A total of 269 DE-lncRNAs, 63 DE-miRNAs and 879 DE-mRNAs were identified in myocardial tissues from microarray datasets GSE130036, GSE36946 and GSE36961, respectively. According to online databases, we found 1 upregulated miRNA hsa-miR-184 that was targeted by 2 downregulated lncRNAs (SNHG9, AC010980.2), potentially targeted 2 downregulated mRNAs (LRRC8A, SLC7A5). 3 downregulated miRNAs (hsa-miR-17-5p, hsa-miR-876-3p, hsa-miR-139-5p) that were targeted by 9 upregulated lncRNAs, potentially targeted 21 upregulated mRNAs. Black and blue modules significantly related to HCM were identified by WGCNA. Hub gene IGFBP5 regulated by hsa-miR-17-5p, AC007389.5, AC104667.1, and AC002511.2 was identified. GSEA indicated that IGFBP5 might involve in the synthesis of myosin complex, participate in kinesin binding, motor activity and function via the regulation of actin cytoskeleton.Conclusion: The results provide a potential molecular regulatory mechanism for the diagnosis and treatment of HCM. IGFBP5 might play an important role in the progression of HCM.

Background: Heart failure is a global health problem, and elevated left atrial pressure (LAP) is a precursor to identifying decompensated heart failure. At present, out-of-hospital monitoring of patients with heart failure is mostly based on the patient's symptoms and signs, and the use of non-invasive technology is scarce.In this study, a non-invasive ballistocardiography (BCG) device was used to collect thoracic vibration signals generated by heartbeat. We collected these signals from more than 1,000 adults, including those with different heart diseases, and used a sensor system and a composite index related to LAP recognition named the LAP-index, to analyze them. This study aimed to verify the reliability and accuracy of the LAP-index in identifying elevated LAP within heart failure patients.Methods: We prospectively included 158 patients with various extent of diastolic function, some of whom had various underlying diseases, and collected BCG and echocardiographic data using a cross-section methodology.The BCG signal was recorded from multiple optical fiber vibration sensors placed on the back of each patient. We adopted the 2016 ASE/EACVI echocardiography guideline as the standard for determining LAP level from echocardiography parameters. To evaluate the diagnostic efficacy of the LAP-index, we drew a receiver operating characteristic (ROC) curve and calculated the area under the ROC curve (AUC). Results:The LAP-index of the 158 patients ranged from 6 to 32. Of them, 39 were diagnosed as high LAP by echocardiography, and 119 cases had normal or slightly elevated LAP. Comparison of the LAP-index results and echocardiographic results revealed the ROC c-statistic of the LAP-index for identifying high LAP was 0.86 (95% CI: 0.79-0.93; P<0.0001). When the LAP-index was at the best cut-off value of 15.5, the positive agreement rate between it and echocardiography LAP was 0.85, the negative agreement rate was 0.80, and the overall agreement rate was 0.81. Conclusions:The sensor system and the LAP-index, a composite index derived from BCG, have high reliability and accuracy in identifying elevated LAP, which provides a novel possibility for the non-invasive detection of hemodynamic congestion in heart failure patients.

Background: Although mortality remains high in patients with atrial fibrillation (AF), there have been limited studies exploring machine learning (ML) models on mortality risk prediction in patients with AF.Objectives: This study sought to develop an ML model that captures important variables in order to predict all-cause mortality in AF patients.Methods: In this single center prospective study, an ML-based mortality prediction model was developed and validated using a dataset of 2,012 patients who experienced AF from November 2018 to February 2020 at the First Affiliated Hospital of Shantou University Medical College. The dataset was randomly divided into a training set (70%, n = 1,223) and a validation set (30%, n = 552). A total of 122 features were collected for variable selection. Least absolute shrinkage and selection operator (LASSO) and random forest (RF) algorithms were used for variable selection. Ten ML models were developed using variables selected by LASSO or RF. The best model was selected and compared with conventional risk scores. A nomogram and user-friendly online tool were developed to facilitate the mortality predictions and management recommendations.Results: Thirteen features were selected by the LASSO regression algorithm. The LASSO-Cox model achieved an area under the curve (AUC) of 0.842 in the training dataset, and 0.854 in the validation dataset. A nomogram based on eight independent features was developed for the prediction of survival at 30, 180, and 365 days following discharge. Both the time dependent receiver operating characteristic (ROC) and decision curve analysis (DCA) showed better performances of the nomogram compared to the CHA2DS2-VASc and HAS-BLED models.Conclusions: The LASSO-Cox mortality predictive model shows potential benefits in death risk evaluation for AF patients over the 365-day period following discharge. This novel ML approach may also provide physicians with personalized management recommendations.