A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound.

Objective:Echocardiography (ECG) is the most common method used to diagnose heart failure (HF). However, its accuracy relies on the experience of the operator. Additionally, the video format of the data makes it challenging for patients to bring them to referrals and reexaminations. Therefore, this study used a deep learning approach to assist physicians in assessing cardiac function to promote the standardization of echocardiographic findings and compatibility of dynamic and static ultrasound data.Methods:A deep spatio-temporal convolutional model r2plus1d-Pan (trained on dynamic data and applied to static data) was improved and trained using the idea of "regression training combined with classification application," which can be generalized to dynamic ECG and static cardiac ultrasound views to identify HF with a reduced ejection fraction (EF < 40%). Additionally, three independent datasets containing 8976 cardiac ultrasound views and 10085 cardiac ultrasound videos were established. Subsequently, a multinational, multi-center dataset of EF was labeled. Furthermore, model training and independent validation were performed. Finally, 15 registered ultrasonographers and cardiologists with different working years in three regional hospitals specialized in cardiovascular disease were recruited to compare the results.Results:The proposed deep spatio-temporal convolutional model achieved an area under the receiveroperating characteristic curve (AUC) value of 0.95 (95% confidence interval [CI]: 0.947 to 0.953) on the training set of dynamic ultrasound data and an AUC of 1 (95% CI, 1 to 1) on the independent validation set. Subsequently, the model was applied to the static cardiac ultrasound view (validation set) with simultaneous input of 1, 2, 4, and 8 images of the same heart, with classification accuracies of 85%, 81%, 93%, and 92%, respectively. On the static data, the classification accuracy of the artificial intelligence (AI) model was comparable with the best performance of ultrasonographers and cardiologists with more than 3 working years (P = 0.344), but significantly better than the median level (P = 0.0000008).Conclusion:A new deep spatio-temporal convolution model was constructed to identify patients with HF with reduced EF accurately (< 40%) using dynamic and static cardiac ultrasound images. The model outperformed the diagnostic performance of most senior specialists. This may be the first HF-related AI diagnostic model compatible with multi-dimensional cardiac ultrasound data, and may thereby contribute to the improvement of HF diagnosis. Additionally, the model enables patients to carry "on-the-go" static ultrasound reports for referral and reexamination, thus saving healthcare resources.

Cardiac function evaluated by two-dimensional speckle tracking imaging in fetuses with congenital heart disease of ventricular afterload increase.

AIMS:To study myocardial deformation in fetuses with ventricular afterload increase compared with gestational age-matched controls using speckle tracking echocardiography.METHODS AND RESULTS:Eighty-nine fetuses were retrospectively selected from the pregnancy screen by echocardiography. There are 41 fetuses with gestational age-matched normal heart served as the control group, 25 fetuses with congenital heart disease (CHD) leading to left ventricular (LV) afterload increase as group LVA and 23 fetuses with CHD leading to right ventricular(RV) afterload increases as group RVA. LV and RV fractional shortening (FS) were measured by conventional methods. The longitudinal strain (LS) and strain rate (LSr) were analyzed by EchoPac software. Group LVA and RVA compared with control group, the LV FS was no significant difference, but LS and LSr values of LV were lower in fetuses with LVA compared to the control group (LS:-15.97(-12.50,-22.52)vs -27.53(-24.33,-29.16) %, p < .01; systolic strain rate (SRs):-1.34(-1.12,-2.16) vs -2.55(-2.28,-2.92) 1/sec, p < .01; early diastolic strain rate (SRe):1.70 ± 0.57 vs 2.46 ± 0.61 1/sec, p < 0.01; late diastolic strain rate (SRa):1.62 ± 0.82 vs 2.39 ± 0.81 1/sec, p < .01). LS and LSr values of LV or RV were lower in fetuses with RVA compared to the control group (LV: LS:-21.52 ± 6.68 vs -26.79 ± 3.22%, p < .01; SRs:-2.11 ± 0.78 vs -2.56 ± 0.43 1/sec; p = .02; RV: LS:-17.64 ± 7.58 vs -26.38 ± 3.97%, p < .01; SRs:-1.62 ± 0.67 vs -2.37 ± 0.44 1/sec; p < .01).CONCLUSION:The results of this study showed that the ventricular LS, LSr, SRs, SRe, SRa values were lower in fetuses with LV or RV afterload increasing CHD estimated by speckle tracking imaging but LV and RV FS were normal,which indicated the strain imaging is feasible in evaluating cardiac function of fetus, and may be more sensitive.

Automated deep neural network-based identification, localization, and tracking of cardiac structures for ultrasound-guided interventional surgery.

Background:The increase in the use of ultrasound-guided interventional therapy for cardiovascular diseases has increased the importance of intraoperative real-time cardiac ultrasound image interpretation. We thus aimed to develop a deep learning-based model to accurately identify, localize, and track the critical cardiac structures and lesions (9 kinds in total) and to validate the algorithm's performance using independent data sets.Methods:This diagnostic study developed a deep learning-based model using data collected from Fuwai Hospital between January 2018 and June 2019. The model was validated with independent French and American data sets. In total, 17,114 cardiac structures and lesions were used to develop the algorithm. The model findings were compared with those of 15 specialized physicians in multiple centers. For external validation, 516,805 tags and 27,938 tags were used from 2 different data sets.Results:Regarding structure identification, the area under the receiver operating characteristic curve (AUC) of each structure in the training data set, optimal performance in the test data set, and median AUC of each structure identification were 1 (95% CI: 1-1), 1 (95% CI: 1-1), and 1 (95% CI: 1-1), respectively. Regarding structure localization, the optimal average accuracy was 0.83. As for structure identification, the accuracy of the model significantly outperformed the median performance of the experts (P<0.01). The optimal identification accuracies of the model in 2 independent external data sets were 89.5% and 90%, respectively (P=0.626).Conclusions:The model outperformed most human experts and was comparable to the optimal performance of all human experts in cardiac structure identification and localization, and could be used in the external data sets.

Successful management of the leaflet perforation after the transcatheter edge-to-edge repair procedure using transcatheter occlusion.

Transcatheter closure of perimembranous ventricular septal defect using a novel fully bioabsorbable occluder: multicenter randomized controlled trial.

Although the use of bioabsorbable occluder is expected to reduce the risk of metal occluder-related complications, it has not been approved due to incomplete degradation and new complications. Novel fully bioabsorbable occluders were designed to overcome such limitations. The aim of this study was to investigate the efficacy and safety of a fully biodegradable occluder in patients with ventricular septal defects. 125 patients with perimembranous ventricular septal defect (VSD) larger than 3 mm were screened from April 2019 to January 2020 in seven centers. 108 patients were enrolled and randomized into the bioabsorbable occluder group (n = 54 patients) and nitinol occluder group (n = 54). A non-inferiority design was utilized and all patients underwent transcatheter device occlusion. Outcomes were analyzed with a 24-month follow-up. All patients were successfully implanted and completed the trial. No residual shunt >2 mm was observed during follow-up. Transthoracic echocardiography showed a hyperechoic area corresponding to the bioabsorbable occluder which decreased primarily during the first year after implantation and disappeared within 24 months. Postprocedural arrhythmia was the only occluder-related complication with an incidence of 5.56% and 14.81% for the bioabsorbable and nitinol groups, respectively (P = 0.112). The incidence of sustained conduction block was lower in the bioabsorbable occluder group (0/54 vs. 6/54, P = 0.036) at 24-month follow-up. In conclusion, the novel fully bioabsorbable occluder can be successfully and safely implanted under echocardiography guidance and reduce the incidence of sustained postprocedural arrythmia. The efficacy and safety of this fully biodegradable occluder are non-inferior to that of a traditional nitinol one.

A Detailed Study to Discover the Trade between Left Atrial Blood Flow, Expression of Calcium-Activated Potassium Channels and Valvular Atrial Fibrillation.

Background: The present study aimed to explore the correlation between calcium-activated potassium channels, left atrial flow field mechanics, valvular atrial fibrillation (VAF), and thrombosis. The process of transforming mechanical signals into biological signals has been revealed, which offers new insights into the study of VAF. Methods: Computational fluid dynamics simulations use numeric analysis and algorithms to compute flow parameters, including turbulent shear stress (TSS) and wall pressure in the left atrium (LA). Real-time PCR and western blotting were used to detect the mRNA and protein expression of IKCa2.3/3.1, ATK1, and P300 in the left atrial tissue of 90 patients. Results: In the valvular disease group, the TSS and wall ressure in the LA increased, the wall pressure increased in turn in all disease groups, mainly near the mitral valve and the posterior portion of the LA, the increase in TSS was the most significant in each group near the mitral valve, and the middle and lower part of the back of the LA and the mRNA expression and protein expression levels of IKCa2.3/3.1, AKT1, and P300 increased (p < 0.05) (n = 15). The present study was preliminarily conducted to elucidate whether there might be a certain correlation between IKCa2.3 and LA hemodynamic changes. Conclusions: The TSS and wall pressure changes in the LA are correlated with the upregulation of mRNA and protein expression of IKCa2.3/3.1, AKT1, and P300.

Are There Any Differences in the Prognostic Value of Left Ventricular Ejection Fraction in Coronary Artery Disease Patients With or Without Moderate and Severe Mitral Regurgitation?

Background:Left ventricular ejection fraction (LVEF) is a vital variable to describe left ventricle systolic function and contractility of left ventricle. However, the association between LVEF and the prognostic effect in patients with moderate or severe mitral regurgitation (MR) is still controversial.Methods:This study comprised 30,775 coronary artery disease (CAD) patients who underwent coronary arteriography (CAG) in the Cardiorenal ImprovemeNt (CIN) registry from January 2007 to December 2018. Patients were divided into none or mild MR group and moderate or severe MR group, and 3 levels of LVEF ≥50, 40-50%, and <40% were further distinguished according to hospital baseline. Univariate and multivariate Cox proportional analyses were used to investigate the association between LVEF levels and long-term all-cause mortality in patients with different MR severities.Results:Of 30,775 CAD patients (62.9 ± 10.6 years, females 23.8%), 26,474 (86.0%) patients had none or mild MR. Compared with none or mild MR patients, patients with moderate or severe MR were older and had worse cardio-renal function. In multivariable Cox proportional analysis, LVEF <40% was independently associated with higher mortality compared with LVEF ≥ 50% in all kinds of MR severity {none or mild MR [adjusted hazard ratio (HR): 1.79; 95% CI: 1.56-2.05, p < 0.001], moderate or severe MR [adjusted HR: 1.57; 95% CI: 1.29-1.91, p < 0.001]}.Conclusions:LVEF is a reliable prognostic index in CAD patients, even in those with moderate or severe MR. LVEF monitoring would still be clinically useful in CAD patients with moderate or severe MR. Clinical trials are needed to prospectively evaluate the optimal threshold for LVEF in patients with moderate or severe MR.

Initial experiences of transapical beating-heart mitral valve repair with a novel artificial chordal implantation device.

BACKGROUND:Severe mitral regurgitation (MR) is associated with progressive heart failure and impairment of survival. Degenerative MR accounts for most MV repair surgeries. Conventional mitral valve repair surgery requires cardiopulmonary bypass and is associated with significant morbidity and risks. Transapical beating-heart mitral valve repair by artificial chordae implantation with transesophageal echocardiography (TEE) guidance has the potential to significantly reduce surgical morbidity. We report the first-in-human experience of degenerative MR repair using a novel artificial chordae implantation device (MitralstitchTM system).METHODS:Ten patients with severe MR underwent transapical artificial chordae implantation using MitralstitchTM system. The procedure was performed through a small left thoracotomy under general anesthesia and TEE guidance. Patients underwent transthoracic echocardiography and other assessments during the follow-up.RESULTS:All 10 patients with an average age of 63.7 ± 9.6 years successfully received transapical artificial chordae implantation. Their MR reduced from severe to none or trace in five patients, mild in five patients before discharge. Five patients received one artificial chordal implantation, four patients received two, and one patient received three and edge-to-edge repair by locking two of them. The safety and efficacy endpoint were achieved in all patients at 1-month follow-up. At 1-year follow-up, six patients had mild MR, three patients had moderate MR, one patient had recurrence of severe MR and underwent surgical repair.CONCLUSIONS:The results of this first-in-human study show safety and feasibility of transapical mitral valve repair using MitralStitch system. Patient selection and technical refinement are crucial to improve the outcomes.

Down-regulation of the insulin signaling pathway by SHC may correlate with congenital heart disease in Chinese populations.

BACKGROUND/AIMS:Congenital heart disease (CHD) is one of the most common and severe congenital defects. The incidence of fetal cardiac malformation is increased in the context of maternal gestational diabetes mellitus (GDM). Therefore, we wanted to determine whether abnormalities in the insulin signaling pathway are associated with the occurrence of nonsyndromic CHD (ns-CHD).METHODS:We used digital gene expression profiling (DGE) of right atrial myocardial tissue samples from eight ns-CHD patients and four controls. The genes potentially associated with CHD were validated by real-time fluorescence quantitative PCR analysis of right atrial myocardial tissues from 37 patients and 10 controls and the H9C2 cell line.RESULTS:The results showed that the insulin signaling pathway, which is mediated by the SHC gene family, was inhibited in the ns-CHD patients. The expression levels of five genes (PTPRF, SHC4, MAP2K2, MKNK2, and ELK1) in the pathway were significantly down-regulated in the patients' atrial tissues (P<0.05 for all). In vitro, the H9C2 cells cultured in high glucose (33 mmol/l) expressed less SHC4, MAP2K2, and Elk-1 than those cultured in low glucose (25 mmol/l). Furthermore, the high glucose concentration down-regulated the 25 genes associated with blood vessel development based on Gene Ontology (GO) term enrichment analyses of RNA-seq data.CONCLUSION:We considered that changes in the insulin signaling pathway mediated by SHC might be involved in the heart development process. This mechanism might account for the increase in the incidence of fetal cardiac malformations in the context of GDM.

Clinical Application of a Fully Ultrasound-Guided Transapical Transcatheter Mitral Valve Replacement Device.

A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound.

Objective:Echocardiography (ECG) is the most common method used to diagnose heart failure (HF). However, its accuracy relies on the experience of the operator. Additionally, the video format of the data makes it challenging for patients to bring them to referrals and reexaminations. Therefore, this study used a deep learning approach to assist physicians in assessing cardiac function to promote the standardization of echocardiographic findings and compatibility of dynamic and static ultrasound data.Methods:A deep spatio-temporal convolutional model r2plus1d-Pan (trained on dynamic data and applied to static data) was improved and trained using the idea of "regression training combined with classification application," which can be generalized to dynamic ECG and static cardiac ultrasound views to identify HF with a reduced ejection fraction (EF < 40%). Additionally, three independent datasets containing 8976 cardiac ultrasound views and 10085 cardiac ultrasound videos were established. Subsequently, a multinational, multi-center dataset of EF was labeled. Furthermore, model training and independent validation were performed. Finally, 15 registered ultrasonographers and cardiologists with different working years in three regional hospitals specialized in cardiovascular disease were recruited to compare the results.Results:The proposed deep spatio-temporal convolutional model achieved an area under the receiveroperating characteristic curve (AUC) value of 0.95 (95% confidence interval [CI]: 0.947 to 0.953) on the training set of dynamic ultrasound data and an AUC of 1 (95% CI, 1 to 1) on the independent validation set. Subsequently, the model was applied to the static cardiac ultrasound view (validation set) with simultaneous input of 1, 2, 4, and 8 images of the same heart, with classification accuracies of 85%, 81%, 93%, and 92%, respectively. On the static data, the classification accuracy of the artificial intelligence (AI) model was comparable with the best performance of ultrasonographers and cardiologists with more than 3 working years (P = 0.344), but significantly better than the median level (P = 0.0000008).Conclusion:A new deep spatio-temporal convolution model was constructed to identify patients with HF with reduced EF accurately (< 40%) using dynamic and static cardiac ultrasound images. The model outperformed the diagnostic performance of most senior specialists. This may be the first HF-related AI diagnostic model compatible with multi-dimensional cardiac ultrasound data, and may thereby contribute to the improvement of HF diagnosis. Additionally, the model enables patients to carry "on-the-go" static ultrasound reports for referral and reexamination, thus saving healthcare resources.

Cardiac function evaluated by two-dimensional speckle tracking imaging in fetuses with congenital heart disease of ventricular afterload increase.

AIMS:To study myocardial deformation in fetuses with ventricular afterload increase compared with gestational age-matched controls using speckle tracking echocardiography.METHODS AND RESULTS:Eighty-nine fetuses were retrospectively selected from the pregnancy screen by echocardiography. There are 41 fetuses with gestational age-matched normal heart served as the control group, 25 fetuses with congenital heart disease (CHD) leading to left ventricular (LV) afterload increase as group LVA and 23 fetuses with CHD leading to right ventricular(RV) afterload increases as group RVA. LV and RV fractional shortening (FS) were measured by conventional methods. The longitudinal strain (LS) and strain rate (LSr) were analyzed by EchoPac software. Group LVA and RVA compared with control group, the LV FS was no significant difference, but LS and LSr values of LV were lower in fetuses with LVA compared to the control group (LS:-15.97(-12.50,-22.52)vs -27.53(-24.33,-29.16) %, p < .01; systolic strain rate (SRs):-1.34(-1.12,-2.16) vs -2.55(-2.28,-2.92) 1/sec, p < .01; early diastolic strain rate (SRe):1.70 ± 0.57 vs 2.46 ± 0.61 1/sec, p < 0.01; late diastolic strain rate (SRa):1.62 ± 0.82 vs 2.39 ± 0.81 1/sec, p < .01). LS and LSr values of LV or RV were lower in fetuses with RVA compared to the control group (LV: LS:-21.52 ± 6.68 vs -26.79 ± 3.22%, p < .01; SRs:-2.11 ± 0.78 vs -2.56 ± 0.43 1/sec; p = .02; RV: LS:-17.64 ± 7.58 vs -26.38 ± 3.97%, p < .01; SRs:-1.62 ± 0.67 vs -2.37 ± 0.44 1/sec; p < .01).CONCLUSION:The results of this study showed that the ventricular LS, LSr, SRs, SRe, SRa values were lower in fetuses with LV or RV afterload increasing CHD estimated by speckle tracking imaging but LV and RV FS were normal,which indicated the strain imaging is feasible in evaluating cardiac function of fetus, and may be more sensitive.

Automated deep neural network-based identification, localization, and tracking of cardiac structures for ultrasound-guided interventional surgery.

Background:The increase in the use of ultrasound-guided interventional therapy for cardiovascular diseases has increased the importance of intraoperative real-time cardiac ultrasound image interpretation. We thus aimed to develop a deep learning-based model to accurately identify, localize, and track the critical cardiac structures and lesions (9 kinds in total) and to validate the algorithm's performance using independent data sets.Methods:This diagnostic study developed a deep learning-based model using data collected from Fuwai Hospital between January 2018 and June 2019. The model was validated with independent French and American data sets. In total, 17,114 cardiac structures and lesions were used to develop the algorithm. The model findings were compared with those of 15 specialized physicians in multiple centers. For external validation, 516,805 tags and 27,938 tags were used from 2 different data sets.Results:Regarding structure identification, the area under the receiver operating characteristic curve (AUC) of each structure in the training data set, optimal performance in the test data set, and median AUC of each structure identification were 1 (95% CI: 1-1), 1 (95% CI: 1-1), and 1 (95% CI: 1-1), respectively. Regarding structure localization, the optimal average accuracy was 0.83. As for structure identification, the accuracy of the model significantly outperformed the median performance of the experts (P<0.01). The optimal identification accuracies of the model in 2 independent external data sets were 89.5% and 90%, respectively (P=0.626).Conclusions:The model outperformed most human experts and was comparable to the optimal performance of all human experts in cardiac structure identification and localization, and could be used in the external data sets.

Successful management of the leaflet perforation after the transcatheter edge-to-edge repair procedure using transcatheter occlusion.

Transcatheter closure of perimembranous ventricular septal defect using a novel fully bioabsorbable occluder: multicenter randomized controlled trial.

Although the use of bioabsorbable occluder is expected to reduce the risk of metal occluder-related complications, it has not been approved due to incomplete degradation and new complications. Novel fully bioabsorbable occluders were designed to overcome such limitations. The aim of this study was to investigate the efficacy and safety of a fully biodegradable occluder in patients with ventricular septal defects. 125 patients with perimembranous ventricular septal defect (VSD) larger than 3 mm were screened from April 2019 to January 2020 in seven centers. 108 patients were enrolled and randomized into the bioabsorbable occluder group (n = 54 patients) and nitinol occluder group (n = 54). A non-inferiority design was utilized and all patients underwent transcatheter device occlusion. Outcomes were analyzed with a 24-month follow-up. All patients were successfully implanted and completed the trial. No residual shunt >2 mm was observed during follow-up. Transthoracic echocardiography showed a hyperechoic area corresponding to the bioabsorbable occluder which decreased primarily during the first year after implantation and disappeared within 24 months. Postprocedural arrhythmia was the only occluder-related complication with an incidence of 5.56% and 14.81% for the bioabsorbable and nitinol groups, respectively (P = 0.112). The incidence of sustained conduction block was lower in the bioabsorbable occluder group (0/54 vs. 6/54, P = 0.036) at 24-month follow-up. In conclusion, the novel fully bioabsorbable occluder can be successfully and safely implanted under echocardiography guidance and reduce the incidence of sustained postprocedural arrythmia. The efficacy and safety of this fully biodegradable occluder are non-inferior to that of a traditional nitinol one.

A Detailed Study to Discover the Trade between Left Atrial Blood Flow, Expression of Calcium-Activated Potassium Channels and Valvular Atrial Fibrillation.

Background: The present study aimed to explore the correlation between calcium-activated potassium channels, left atrial flow field mechanics, valvular atrial fibrillation (VAF), and thrombosis. The process of transforming mechanical signals into biological signals has been revealed, which offers new insights into the study of VAF. Methods: Computational fluid dynamics simulations use numeric analysis and algorithms to compute flow parameters, including turbulent shear stress (TSS) and wall pressure in the left atrium (LA). Real-time PCR and western blotting were used to detect the mRNA and protein expression of IKCa2.3/3.1, ATK1, and P300 in the left atrial tissue of 90 patients. Results: In the valvular disease group, the TSS and wall ressure in the LA increased, the wall pressure increased in turn in all disease groups, mainly near the mitral valve and the posterior portion of the LA, the increase in TSS was the most significant in each group near the mitral valve, and the middle and lower part of the back of the LA and the mRNA expression and protein expression levels of IKCa2.3/3.1, AKT1, and P300 increased (p < 0.05) (n = 15). The present study was preliminarily conducted to elucidate whether there might be a certain correlation between IKCa2.3 and LA hemodynamic changes. Conclusions: The TSS and wall pressure changes in the LA are correlated with the upregulation of mRNA and protein expression of IKCa2.3/3.1, AKT1, and P300.

Are There Any Differences in the Prognostic Value of Left Ventricular Ejection Fraction in Coronary Artery Disease Patients With or Without Moderate and Severe Mitral Regurgitation?

Background:Left ventricular ejection fraction (LVEF) is a vital variable to describe left ventricle systolic function and contractility of left ventricle. However, the association between LVEF and the prognostic effect in patients with moderate or severe mitral regurgitation (MR) is still controversial.Methods:This study comprised 30,775 coronary artery disease (CAD) patients who underwent coronary arteriography (CAG) in the Cardiorenal ImprovemeNt (CIN) registry from January 2007 to December 2018. Patients were divided into none or mild MR group and moderate or severe MR group, and 3 levels of LVEF ≥50, 40-50%, and <40% were further distinguished according to hospital baseline. Univariate and multivariate Cox proportional analyses were used to investigate the association between LVEF levels and long-term all-cause mortality in patients with different MR severities.Results:Of 30,775 CAD patients (62.9 ± 10.6 years, females 23.8%), 26,474 (86.0%) patients had none or mild MR. Compared with none or mild MR patients, patients with moderate or severe MR were older and had worse cardio-renal function. In multivariable Cox proportional analysis, LVEF <40% was independently associated with higher mortality compared with LVEF ≥ 50% in all kinds of MR severity {none or mild MR [adjusted hazard ratio (HR): 1.79; 95% CI: 1.56-2.05, p < 0.001], moderate or severe MR [adjusted HR: 1.57; 95% CI: 1.29-1.91, p < 0.001]}.Conclusions:LVEF is a reliable prognostic index in CAD patients, even in those with moderate or severe MR. LVEF monitoring would still be clinically useful in CAD patients with moderate or severe MR. Clinical trials are needed to prospectively evaluate the optimal threshold for LVEF in patients with moderate or severe MR.

Initial experiences of transapical beating-heart mitral valve repair with a novel artificial chordal implantation device.

BACKGROUND:Severe mitral regurgitation (MR) is associated with progressive heart failure and impairment of survival. Degenerative MR accounts for most MV repair surgeries. Conventional mitral valve repair surgery requires cardiopulmonary bypass and is associated with significant morbidity and risks. Transapical beating-heart mitral valve repair by artificial chordae implantation with transesophageal echocardiography (TEE) guidance has the potential to significantly reduce surgical morbidity. We report the first-in-human experience of degenerative MR repair using a novel artificial chordae implantation device (MitralstitchTM system).METHODS:Ten patients with severe MR underwent transapical artificial chordae implantation using MitralstitchTM system. The procedure was performed through a small left thoracotomy under general anesthesia and TEE guidance. Patients underwent transthoracic echocardiography and other assessments during the follow-up.RESULTS:All 10 patients with an average age of 63.7 ± 9.6 years successfully received transapical artificial chordae implantation. Their MR reduced from severe to none or trace in five patients, mild in five patients before discharge. Five patients received one artificial chordal implantation, four patients received two, and one patient received three and edge-to-edge repair by locking two of them. The safety and efficacy endpoint were achieved in all patients at 1-month follow-up. At 1-year follow-up, six patients had mild MR, three patients had moderate MR, one patient had recurrence of severe MR and underwent surgical repair.CONCLUSIONS:The results of this first-in-human study show safety and feasibility of transapical mitral valve repair using MitralStitch system. Patient selection and technical refinement are crucial to improve the outcomes.

Down-regulation of the insulin signaling pathway by SHC may correlate with congenital heart disease in Chinese populations.

BACKGROUND/AIMS:Congenital heart disease (CHD) is one of the most common and severe congenital defects. The incidence of fetal cardiac malformation is increased in the context of maternal gestational diabetes mellitus (GDM). Therefore, we wanted to determine whether abnormalities in the insulin signaling pathway are associated with the occurrence of nonsyndromic CHD (ns-CHD).METHODS:We used digital gene expression profiling (DGE) of right atrial myocardial tissue samples from eight ns-CHD patients and four controls. The genes potentially associated with CHD were validated by real-time fluorescence quantitative PCR analysis of right atrial myocardial tissues from 37 patients and 10 controls and the H9C2 cell line.RESULTS:The results showed that the insulin signaling pathway, which is mediated by the SHC gene family, was inhibited in the ns-CHD patients. The expression levels of five genes (PTPRF, SHC4, MAP2K2, MKNK2, and ELK1) in the pathway were significantly down-regulated in the patients' atrial tissues (P<0.05 for all). In vitro, the H9C2 cells cultured in high glucose (33 mmol/l) expressed less SHC4, MAP2K2, and Elk-1 than those cultured in low glucose (25 mmol/l). Furthermore, the high glucose concentration down-regulated the 25 genes associated with blood vessel development based on Gene Ontology (GO) term enrichment analyses of RNA-seq data.CONCLUSION:We considered that changes in the insulin signaling pathway mediated by SHC might be involved in the heart development process. This mechanism might account for the increase in the incidence of fetal cardiac malformations in the context of GDM.

Clinical Application of a Fully Ultrasound-Guided Transapical Transcatheter Mitral Valve Replacement Device.