Successful treatment of severe primary mitral regurgitation due to rheumatic aetiology using a novel-designed transcatheter edge-to-edge repair system.

The transcatheter edge-to-edge mitral valve repair (TEER) has been recommended as a reliable treatment option for selected patients with severe degenerative and functional mitral regurgitation (MR). Although MR patients with rheumatic etiology were excluded from two significant trials (EVEREST II and COAPT) that established a role for the TEER in degenerative and functional MR. However, it has been reported that the TEER procedure could be safely and effectively performed in carefully selected rheumatic MR patients. Therefore, we share a case report of successfully treating severe rheumatic MR using a novel-designed TEER system (JensClipTM).

Transcatheter Mitral Valve Repair Using a Novel Device for a High-Surgical-Risk Patient With DMR.

Transcatheter edge-to-edge repair (TEER) has been established as a safe and effective option for treating patients with severe symptomatic degenerative mitral regurgitation (MR) who are at prohibitive surgical risk. However, the significant cost presents a considerable disease burden in low-income countries. This case details the treatment of a high-surgical-risk patient with severe degenerative MR by using the GeminiOne (Peijia Medical) system-a novel Chinese TEER device.

Biodegradable Cardiac Occluder with Surface Modification by Gelatin-Peptide Conjugate to Promote Endogenous Tissue Regeneration.

Transcatheter intervention has been the preferred treatment for congenital structural heart diseases by implanting occluders into the heart defect site through minimally invasive access. Biodegradable polymers provide a promising alternative for cardiovascular implants by conferring therapeutic function and eliminating long-term complications, but inducing in situ cardiac tissue regeneration remains a substantial clinical challenge. PGAG (polydioxanone/poly (l-lactic acid)-gelatin-A5G81) occluders are prepared by covalently conjugating biomolecules composed of gelatin and layer adhesive protein-derived peptides (A5G81) to the surface of polydioxanone and poly (l-lactic acid) fibers. The polymer microfiber-biomacromolecule-peptide frame with biophysical and biochemical cues could orchestrate the biomaterial-host cell interactions, by recruiting endogenous endothelial cells, promoting their adhesion and proliferation, and polarizing immune cells into anti-inflammatory phenotypes and augmenting the release of reparative cytokines. In a porcine atrial septal defect (ASD) model, PGAG occluders promote in situ tissue regeneration by accelerating surface endothelialization and regulating immune response, which mitigate inflammation and fibrosis formation, and facilitate the fusion of occluder with surrounding heart tissue. Collectively, this work highlights the modulation of cell-biomaterial interactions for tissue regeneration in cardiac defect models, ensuring endothelialization and extracellular matrix remodeling on polymeric scaffolds. Bioinspired cell-material interface offers a highly efficient and generalized approach for constructing bioactive coatings on medical devices.

Study design and rationale of COMPETE: Comparison of the effect of medication therapy in alleviating migraine with patent foramen ovale.

BACKGROUND: Previous studies have examined the impact of antithrombotic agents on Patent Foramen Ovale (PFO) in relation to migraine. However, differences in effectiveness of different antithrombotic agents and traditional migraine medications are not known. METHODS/DESIGN: This study is an investigator-initiated, randomized, multicenter, single-masked (outcomes assessor), and active-controlled parallel-group trial (ClinicalTrials.gov Identifier: NCT05546320), with the objective of evaluating the prevention efficacy of antithrombotic agents compared to first-line migraine medication in PFO patients. The trial involves 1,000 migraine patients with a right-to-left shunt at the atrial level, randomized in a 1:1:1:1 fashion to receive either aspirin 300 mg QD, clopidogrel 75 mg QD, rivaroxaban 20 mg QD, or the active-control metoprolol 25 mg BID. The primary efficacy end point is the response rate, defined as a 50% or greater reduction in the average migraine attack days per month or in the average number of migraine attacks per month at 12-week visit compared to baseline. CONCLUSIONS: The COMPETE trial aims to provide valuable insights into the comparative effectiveness of antithrombotic agents and standard migraine therapies in patients with PFO. This study holds the promise of advancing treatment approaches for individuals having migraines associated with PFO, thus addressing an important gap in current migraine management strategies.

Alerting trends in epidemiology for non-rheumatic degenerative mitral valve disease, 1990-2019: An age-period-cohort analysis for the Global Burden of Disease Study 2019.

BACKGROUND: The global and national burden of rheumatic mitral valve disease (MVD) has been well studied and estimated before. However, little is known about non-rheumatic degenerative MVD. Therefore, this study aimed to assess the trends in non-rheumatic degenerative MVD (NRDMVD) epidemiology, with an emphasis on NRDMVD mortality, leading risk factors, and their associations with age, period, and birth cohort. METHODS: Using the data derived from the Global Burden of Disease Study 2019, including prevalence, mortality, and disability-adjusted life years, we analyzed the burden of NRDMVD and the detailed trends of NRDMVD mortality over the past 30 years in 204 countries and territories by implementing the age-period-cohort framework. RESULTS: Globally, the number of deaths due to NRDMVD increased from 5695.89 (95% uncertainty interval [UI]: 5405.19 to 5895.4) × 1000 in 1990 to 9137.79 (95% UI: 8395.68 to 9743.55) × 1000 in 2019. The all-age mortality rate increased from 106.47 (95% UI: 101.03 to 110.2) per 100,000 to 118.1 (95% UI: 108.51 to 125.93) per 100,000, whereas the age-standardized mortality rate decreased from 170.45 (95% UI: 159.61 to 176.94) per 100,000 to 117.95 (95% UI: 107.83 to 125.92) per 100,000. The estimated net drift of mortality per year was -1.1% (95% confidence interval: -1.17 to -1.04). The risk of death due to NRDMVD increased with age, reaching its peak after 85 years old globally. Despite female patients being associated with lower local drift than male patients, no significant gender differences were observed in the age effect across countries and regions for all sociodemographic index (SDI) levels, except low-SDI regions. CONCLUSIONS: We estimated the global disease prevalence of and mortality due to NRDMVD over approximately a 30-year period. The health-related burden of NRDMVD has declined worldwide; however, the condition persisted in low-SDI regions. Moreover, higher attention should be paid to female patients.

Noncontact remote sensing of abnormal blood pressure using a deep neural network: a novel approach for hypertension screening.

Background: As the global burden of hypertension continues to increase, early diagnosis and treatment play an increasingly important role in improving the prognosis of patients. In this study, we developed and evaluated a method for predicting abnormally high blood pressure (HBP) from infrared (upper body) remote thermograms using a deep learning (DL) model. Methods: The data used in this cross-sectional study were drawn from a coronavirus disease 2019 (COVID-19) pilot cohort study comprising data from 252 volunteers recruited from 22 July to 4 September 2020. Original video files were cropped at 5 frame intervals to 3,800 frames per slice. Blood pressure (BP) information was measured using a Welch Allyn 71WT monitor prior to infrared imaging, and an abnormal increase in BP was defined as a systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg. The PanycNet DL model was developed using a deep neural network to predict abnormal BP based on infrared thermograms. Results: A total of 252 participants were included, of which 62.70% were male and 37.30% were female. The rate of abnormally high HBP was 29.20% of the total number. In the validation group (upper body), precision, recall, and area under the receiver operating characteristic curve (AUC) values were 0.930, 0.930, and 0.983 [95% confidence interval (CI): 0.904-1.000], respectively, and the head showed the strongest predictive ability with an AUC of 0.868 (95% CI: 0.603-0.994). Conclusions: This is the first technique that can perform screening for hypertension without contact using existing equipment and data. It is anticipated that this technique will be suitable for mass screening of the population for abnormal BP in public places and home BP monitoring.

Genetic overlap for ten cardiovascular diseases: A comprehensive gene-centric pleiotropic association analysis and Mendelian randomization study.

Recent studies suggest that pleiotropic effects may explain the genetic architecture of cardiovascular diseases (CVDs). We conducted a comprehensive gene-centric pleiotropic association analysis for ten CVDs using genome-wide association study (GWAS) summary statistics to identify pleiotropic genes and pathways that may underlie multiple CVDs. We found shared genetic mechanisms underlying the pathophysiology of CVDs, with over two-thirds of the diseases exhibiting common genes and single-nucleotide polymorphisms (SNPs). Significant positive genetic correlations were observed in more than half of paired CVDs. Additionally, we investigated the pleiotropic genes shared between different CVDs, as well as their functional pathways and distribution in different tissues. Moreover, six hub genes, including ALDH2, XPO1, HSPA1L, ESR2, WDR12, and RAB1A, as well as 26 targeted potential drugs, were identified. Our study provides further evidence for the pleiotropic effects of genetic variants on CVDs and highlights the importance of considering pleiotropy in genetic association studies.

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.

The Cardiovascular Disease Burden Attributable to Low Physical Activity in the Western Pacific Region, 1990-2019: an Age-Period-Cohort Analysis of the Global Burden of Disease Study.

AIM: To increase the comprehensive understanding of trends in the burden of cardiovascular disease (CVD) attributable to low physical activity in the Western Pacific Region. METHODS: Based on data from the Global Burden of Disease (GBD) study for the years 1990-2019, an age-period-cohort (APC) analysis was conducted to investigate trends in CVD-related mortality attributable to low physical activity in the Western Pacific Region and associations with age, period, and birth cohort. We also used joinpoint regression analysis to identify the periods with the most substantial changes. RESULTS: The Western Pacific Region witnessed a substantial increase in CVD deaths attributable to low physical activity, accompanied by a rise in all-age CVD-related mortality. However, the age-standardized death rate was lower in the region than the global level, highlighting the importance of considering the age composition of CVD burden in the region. Countries with higher SDI levels exhibited lower mortality than those with lower SDI levels. The longitudinal analysis using the APC model indicated an overall improvement in CVD-related mortality attributable to low physical activity in the region, but with differences between sexes and CVD subtypes. Specific period in which CVD-related mortality decreased significantly were 2011-2016, for the average annual percentage change for the period was -0.69%. CONCLUSION: The study highlights the significance of addressing low physical activity as a modifiable risk factor for CVD burden in the Western Pacific Region. Further research is essential to understand the factors contributing to inter-country variations, sex disparities, and CVD subtypes distinctions.

Low TP53 variant allele frequency as a biomarker for anti-programmed death (ligand) 1 monotherapy in lung adenocarcinoma.

BACKGROUND: TP53 mutation heterogeneity should be considered when using TP53 as a predictive biomarker for anti-programmed death (ligand) 1 (PD-(L)1) monotherapy in lung adenocarcinoma (LUAD). However, whether TP53 variant allele frequency (VAF) should also be considered remains unknown. METHODS: Patients with LUAD from both published research and the local cohort were included to discover and validate the relationship between TP53 VAF and the efficacy of PD-(L)1 inhibitors. The Cancer Genome Atlas (TCGA) LUAD data were included for genomic, transcriptomic, and tumor microenvironment analysis. RESULTS: Among 159 patients in the discovery cohort, low TP53 VAF patients (VAF ≤ 25%) experienced significantly longer progression-free survival (PFS) than both high TP53 VAF (5.4 vs. 3.3 months; p = .021) and TP53-wild-type patients (5.4 vs. 2.5 months; p = .011). Multivariate Cox regression revealed low TP53 VAF as an independent biomarker of better efficacy. Among 50 patients in the combined validation cohort, median PFS of low TP53 VAF patients was also significantly longer than that of high TP53 VAF patients (12.0 vs. 2.1 months; p = .037). Analyzed with 469 TCGA LUAD samples, low TP53 VAF is associated with significantly higher PD-L1 expression, enrichment of gene sets related to T-cell activation, T cell-mediated immunity, and interferon-γ signaling pathways, and independently associated with more tumor-infiltrating CD8+ T cells compared with both high TP53 VAF and TP53-wild type. CONCLUSIONS: TP53 VAF should also be considered when using TP53 as a predictive biomarker. Only low TP53 VAF is independently associated with better efficacy of anti-PD-(L)1 monotherapy, which may result from higher PD-L1 expression and more tumor-infiltrating CD8+ T cells.

Development of an Expert-Level Right Ventricular Abnormality Detection Algorithm Based on Deep Learning.

PURPOSE: Studies relating to the right ventricle (RV) are inadequate, and specific diagnostic algorithms still need to be improved. This essay is designed to make exploration and verification on an algorithm of deep learning based on imaging and clinical data to detect RV abnormalities. METHODS: The Automated Cardiac Diagnosis Challenge dataset includes 20 subjects with RV abnormalities (an RV cavity volume which is higher than 110 mL/m2 or RV ejection fraction which is lower than 40%) and 20 normal subjects who suffered from both cardiac MRI. The subjects were separated into training and validation sets in a ratio of 7:3 and were modeled by utilizing a nerve net of deep-learning and six machine-learning algorithms. Eight MRI specialists from multiple centers independently determined whether each subject in the validation group had RV abnormalities. Model performance was evaluated based on the AUC, accuracy, recall, sensitivity and specificity. Furthermore, a preliminary assessment of patient disease risk was performed based on clinical information using a nomogram. RESULTS: The deep-learning neural network outperformed the other six machine-learning algorithms, with an AUC value of 1 (95% confidence interval: 1-1) on both training group and validation group. This algorithm surpassed most human experts (87.5%). In addition, the nomogram model could evaluate a population with a disease risk of 0.2-0.8. CONCLUSIONS: A deep-learning algorithm could effectively identify patients with RV abnormalities. This AI algorithm developed specifically for right ventricular abnormalities will improve the detection of right ventricular abnormalities at all levels of care units and facilitate the timely diagnosis and treatment of related diseases. In addition, this study is the first to validate the algorithm's ability to classify RV abnormalities by comparing it with human experts.

Extracellular Matrix/Glycopeptide Hybrid Hydrogel as an Immunomodulatory Niche for Endogenous Cardiac Repair after Myocardial Infarction.

The treatment of myocardial infarction (MI) remains a substantial challenge due to excessive inflammation, massive cell death, and restricted regenerative potential, leading to maladaptive healing process and eventually heart failure. Current strategies of regulating inflammation or improving cardiac tissue regeneration have limited success. Herein, a hybrid hydrogel coassembled by acellular cardiac extracellular matrix (ECM) and immunomodulatory glycopeptide is developed for endogenous tissue regeneration after MI. The hydrogel constructs a niche recapitulating the architecture of native ECM for attracting host cell homing, controlling macrophage differentiation via glycopeptide unit, and promoting endotheliocyte proliferation by enhancing the macrophage-endotheliocyte crosstalk, which coordinate the innate healing mechanism for cardiac tissue regeneration. In a rodent MI model, the hybrid hydrogel successfully orchestrates a proreparative response indicated by enhanced M2 macrophage polarization, increased angiogenesis, and improved cardiomyocyte survival, which alleviates infarct size, improves wall thicknesses, and enhances cardiac contractility. Furthermore, the safety and effectiveness of the hydrogel are demonstrated in a porcine MI model, wherein proteomics verifies the regulation of immune response, proangiogenesis, and accelerated healing process. Collectively, the injectable composite hydrogel serving as an immunomodulatory niche for promoting cell homing and proliferation, inflammation modulation, tissue remodeling, and function restoration provides an effective strategy for endogenous cardiac repair.

Global, regional, and national time trends in disability-adjusted life years, mortality, and variable risk factors of non-rheumatic calcified aortic valve disease, 1990-2019: an age-period-cohort analysis of the Global Burden of Disease 2019 study.

Background: Non-rheumatic heart valve disease (NRVD) is a common cardiovascular disease, whereas calcific aortic valve disease (CAVD) is a type of disease with the fastest-growing mortality and disability-adjusted life years (DALYs). This study presents an overview of the trends noted in the DALY, CAVD mortality, and the modifiable risk factors in the last 30 years, across 204 countries and territories, and their relationship with the period, age, and birth cohort. Methods: Data were obtained from the Global Burden of Disease (GBD) 2019 database. An age-period-cohort (APC) model was used to assess general annual percentage changes in DALYs and mortality over the past 30 years in 204 countries and territories. Results: In 2019, the age-standardized mortality rate for the entire population in areas with a high socio-demographic index (SDI) was more than 4 times higher than that in low-SDI areas. From 1990 to 2019, the net drift in mortality for the whole population was from -2.1% [95% confidence interval (CI): -2.39% to -1.82%] per year in high-SDI regions to 0.05% (95% CI: -0.13% to 0.23%) per year in low- to medium-SDI regions. The trend of DALYs was similar to that of mortality. The age-wise distribution of deaths exhibited a shift toward older populations in high-SDI regions globally, except for Qatar, Saudi Arabia, and the United Arab Emirates. Over time, in most medium, medium-low, and low SDI regions, there was no significant improvement in the period and birth cohort or even an unfavorable or worsening risk. The main variable risk factors of CAVD death and DALYs lost were high sodium diet, high systolic blood pressure, and lead exposure. Those risk factors only showed a significant downward trend in middle- and high-SDI regions. Conclusions: Health disparities between regions for CAVD are widening and could lead to a heavy disease burden in the future. Health authorities and policymakers in low SDI areas, in particular, need to consider improving resource allocation, increasing access to medical resources, and controlling variable risk factors to stem the growth of the disease burden.

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.

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 fully biodegradable polydioxanone occluder for ventricle septal defect closure.

Ventricular septal defect (VSD) is one of the commonest congenital heart diseases (CHDs). Current occluders for VSD treatment are mainly made of nitinol, which has the risk of nickel allergy, persistent myocardial abrasion and fatal arrythmia. Herein, a fully biodegradable polydioxanone (PDO) occluder equipped with a shape line and poly-l-lactic acid PLLA membranes is developed for VSD closure without the addition of metal marker. PDO occluder showed great mechanical strength, fatigue resistance, geometry fitness, biocompatibility and degradability. In a rat subcutaneous implantation model, PDO filaments significantly alleviated inflammation response, mitigated fibrosis and promoted endothelialization compared with nitinol. The safety and efficacy of PDO occluder were confirmed in a canine VSD model with 3-year follow-up, demonstrating the biodegradable PDO occluder could not only effectively repair VSD, induce cardiac remodeling but also address the complications associated with metal occluders. Furthermore, a pilot clinical trial with five VSD patients indicated that all the occluders were successfully implanted under the guidance of echocardiography and no adverse events occurred during the 3-month follow-up. Collectively, the fully bioresorbable PDO occluder is safe and effective for clinical VSD closure and holds great promise for the treatment of structural CHDs.