A Systematic Review and Meta-Analysis of Outcomes Following Mitral Valve Surgery in Patients with Significant Functional Mitral Regurgitation and Left Ventricular Dysfunction.

BACKGROUND AND AIM OF THE STUDY: The surgical correction of functional mitral regurgitation (MR) remains challenging and controversial. The study aim was to systematically review the outcomes of surgical mitral valve repair (MVRpr) and mitral valve replacement (MVR) in patients with significant functional MR and left ventricular (LV) dysfunction. METHODS: A meta-analysis was performed of published data acquired from patients with moderate to severe functional MR and LV ejection fraction (LVEF) <40% who underwent surgical MVRpr or MVR. The data were meta-analyzed across studies using Bayesian hierarchical models when feasible. RESULTS: The search yielded 36 observational studies. The pooled proportion of operative mortality following MVRpr was 5% (33 studies; 2,231 patients; 95% credible interval (CrI) 4-7%), while that following MVR was 10% (10 studies; 389 patients; 95% CrI 5-18%). For patients undergoing MVRpr, pooled proportions of postoperative cerebrovascular accidents and renal failure were 2% (11 studies; 750 patients; 95% CrI 1-3%) and 9% (11 studies; 756 patients; 95% CrI 5-16%), respectively. The five-year actuarial survival rates following MVRpr across 12 studies ranged from 47% to 78% (median 66%). CONCLUSIONS: In selected patients with significant functional MR and LV dysfunction, surgical MVRpr and MVR can be performed with acceptable intermediate operative mortality risks.

Transcatheter heart valve failure: a systematic review.

AIMS: A comprehensive description of transcatheter heart valve (THV) failure has not been performed. We undertook a systematic review to investigate the aetiology, diagnosis, management, and outcomes of THV failure. METHODS AND RESULTS: The systematic review was performed in accordance with the PRISMA guidelines using EMBASE, MEDLINE, and Scopus. Between December 2002 and March 2014, 70 publications reported 87 individual cases of transcatheter aortic valve implantation (TAVI) failure. Similar to surgical bioprosthetic heart valve failure, we observed cases of prosthetic valve endocarditis (PVE) (n = 34), structural valve failure (n = 13), and THV thrombosis (n = 15). The microbiological profile of THV PVE was similar to surgical PVE, though one-quarter had satellite mitral valve endocarditis, and surgical intervention was required in 40% (75% survival). Structural valve failure occurred most frequently due to leaflet calcification and was predominantly treated by redo-THV (60%). Transcatheter heart valve thrombosis occurred at a mean 9 ± 7 months post-implantation and was successfully treated by prolonged anticoagulation in three-quarters of cases. Two novel causes of THV failure were identified: late THV embolization (n = 18); and THV compression (n = 7) following cardiopulmonary resuscitation (CPR). These failure modes have not been reported in the surgical literature. Potential risk factors for late THV embolization include low prosthesis implantation, THV undersizing/underexpansion, bicuspid, and non-calcified anatomy. Transcatheter heart valve embolization mandated surgery in 80% of patients. Transcatheter heart valve compression was noted at post-mortem in most cases. CONCLUSION: Transcatheter heart valves are susceptible to failure modes typical to those of surgical bioprostheses and unique to their specific design. Transcatheter heart valve compression and late embolization represent complications previously unreported in the surgical literature.

Revolutionizing Acute Cardiac Care with Artificial Intelligence: Opportunities and Challenges.

This manuscript reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The paper examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac CT, and MRI and discusses the regulatory landscape for AI in healthcare, categorizes AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalizability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.

A responsible framework for applying artificial intelligence on medical images and signals at the point-of-care: the PACS-AI platform.

The potential of artificial intelligence (AI) in medicine lies in its ability to enhance clinicians' capacity to analyze medical images, thereby improving diagnostic precision and accuracy, thus enhancing current tests. However, the integration of AI within healthcare is fraught with difficulties. Heterogeneity among healthcare system applications, reliance on proprietary closed-source software, and rising cyber-security threats pose significant challenges. Moreover, prior to their deployment in clinical settings, AI models must demonstrate their effectiveness across a wide range of scenarios and must be validated by prospective studies, but doing so requires testing in an environment mirroring the clinical workflow which is difficult to achieve without dedicated software. Finally, the use of AI techniques in healthcare raises significant legal and ethical issues, such as the protection of patient privacy, the prevention of bias, and the monitoring of the device's safety and effectiveness for regulatory compliance. This review describes challenges to AI integration in healthcare and provides guidelines on how to move forward. We describe an open-source solution that we developed which integrates AI models into the Picture Archives Communication System (PACS), called PACS-AI. This approach aims to increase the evaluation of AI models by facilitating their integration and validation with existing medical imaging databases. PACS-AI may overcome many current barriers to AI deployment and offers a pathway towards responsible, fair, and effective deployment of AI models in healthcare. Additionally, we propose a list of criteria and guidelines that AI researchers should adopt when publishing a medical AI model, to enhance standardization and reproducibility.

Maximizing Large Language Model Utility in Cardiovascular Care: A Practical Guide.

Large language models (LLMs) have emerged as powerful tools in artificial intelligence, demonstrating remarkable capabilities in natural language processing and generation. In this article, we explore the potential applications of LLMs in enhancing cardiovascular care and research. We discuss how LLMs can be utilized to simplify complex medical information, improve patient-physician communication, and automate tasks such as summarizing medical articles and extracting key information. Additionally, we highlight the role of LLMs in categorizing and analyzing unstructured data, such as medical notes and test results, which could revolutionize data handling and interpretation in cardiovascular research. However, we also emphasize the limitations and challenges associated with LLMs, including potential biases, reasoning opacity, and the need for rigorous validation in medical contexts. This article provides a practical guide for cardiovascular professionals to understand and harness the power of LLMs while navigating their limitations. We conclude by discussing the future directions and implications of LLMs in transforming cardiovascular care and research.

Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications.

Cardiovascular diseases are the leading cause of death globally and contribute significantly to the cost of healthcare. Artificial intelligence (AI) is poised to reshape cardiology. Using supervised and unsupervised learning, the two main branches of AI, several applications have been developed in recent years to improve risk prediction, allow large-scale analysis of medical data, and phenotype patients for personalized medicine. In this review, we examine the key advances in AI in cardiology and its limitations regarding bias in the data, standardization in reporting, data access, and model trust and accountability in cases of error. Finally, we discuss implementation methods to unleash AI's potential in making healthcare more accurate and efficient. Several steps need to be followed and challenges overcome in order to successfully integrate AI in clinical practice and ensure its longevity.

Should they stay, or should they go: Do we need to remove the old cardiac implantable electronic device if a new system is required on the contralateral side?

Background: Ipsilateral approach in patients requiring cardiac implantable electronic device (CIED) revision or upgrade may not be feasible, primarily due to vascular occlusion. If a new CIED is implanted on the contralateral side, a common practice is to explant the old CIED to avoid device interaction. Objective: The purpose of this study was to assess a conservative approach of abandoning the old CIED after implanting a new contralateral device. Methods: We used an artificial intelligence algorithm to analyze postimplant chest radiographs to identify those with multiple CIEDs. Outcomes of interest included device interaction, abandoned CIED elective replacement indicator (ERI) behavior, subsequent programming changes, and explant of abandoned CIED. Theoretical risk of infection with removal of abandoned CIED was estimated using a validated scoring system. Results: Among 12,045 patients, we identified 40 patients with multiple CIEDs. Occluded veins were the most common indication for contralateral implantation (n = 27 [67.5%]). Fifteen abandoned CIEDs reached ERI, with 4 reverting to VVI 65. One patient underwent explant due to device interaction, and 2 required device reprogramming. Of 32 patients with an implantable cardioverter-defibrillator, 8 (25%) had treated ventricular arrhythmia. There were no failed or inappropriate therapies due to interaction. Eighteen patients (45%) had hypothetical >1% annual risk of hospitalization for device infection if the abandoned CIED had been explanted. Conclusion: In patients requiring new CIED implant on the contralateral side, abandoning the old device is feasible. This approach may reduce the risk of infection and concerns regarding abandoned leads and magnetic resonance imaging scans. Knowledge of ERI behavior is essential to avoid device interactions.

Aortic Annulus S-Curve: Implications for Transcatheter Aortic Valve Replacement and Related Procedures, Part 1.

Most transcatheter aortic valve replacement-related procedures (eg, transcatheter aortic valve replacement implantation depth, commissural alignment, coronary access, bioprosthetic or native aortic scallop intentional laceration to prevent iatrogenic coronary artery obstruction, paravalvular leak closure) require an optimal fluoroscopic viewing angle located somewhere along the aortic annulus S-curve. Chamber views, coronary cusp and coronary anatomy, can be understood along the aortic annulus S-curve. A better understanding of the optimal fluoroscopic viewing angles along the S-curve may translate into increased operator confidence and improved safety and efficacy while reducing procedural time, radiation dose, contrast volume, and complication rates.

Percutaneous Closure of a Giant Aortic Pseudoaneurysm Using Multimodality Imaging Guidance.

Ascending aortic pseudoaneurysm is a rare, life-threatening complication of cardiac surgery. Surgical management is recommended, however, transcatheter techniques offer a less invasive alternative. We describe successful percutaneous closure, guided by using multimodality imaging, in a patient with high surgical risk.

Implications of Myocardial Infarction on Management and Outcome in Cardiogenic Shock.

Background The randomized DOREMI (Dobutamine Compared to Milrinone) clinical trial evaluated the efficacy and safety of milrinone and dobutamine in patients with cardiogenic shock. Whether the results remain consistent when stratified by acute myocardial infarction remains unknown. In this substudy, we sought to evaluate differences in clinical management and outcomes of acute myocardial infarction complicated by cardiogenic shock (AMICS) versus non-AMICS. Methods and Results Patients in cardiogenic shock (n=192) were randomized 1:1 to dobutamine or milrinone. The primary composite end point in this subgroup analysis was all-cause in-hospital mortality, cardiac arrest, non-fatal myocardial infarction, cerebrovascular accident, the need for mechanical circulatory support, or initiation of renal replacement therapy (RRT) at 30-days. Outcomes were evaluated in patients with (n=65) and without (n=127) AMICS. The primary composite end point was significantly higher in AMICS versus non-AMICS (hazard ratio [HR], 2.21; 95% CI, 1.47-3.30; P=0.0001). The primary end point was driven by increased rates of all-cause mortality, mechanical circulatory support, and RRT. No differences in other secondary outcomes including cardiac arrest or cerebrovascular accident were observed. AMICS remained associated with the primary composite outcome, 30-day mortality, and RRT after adjustment for age, sex, procedural contrast use, multivessel disease, and inotrope type. Conclusions AMI was associated with increased rates of adverse clinical outcomes in cardiogenic shock along with increased rates of mortality and initiation of mechanical circulatory support and RRT. Contrast administration during revascularization likely contributes to increased rates of RRT. Heterogeneity of outcomes in AMICS versus non-AMICS highlights the need to study interventions in specific subgroups of cardiogenic shock. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03207165.

Recursive multiresolution convolutional neural networks for 3D aortic valve annulus planimetry.

PURPOSE: Transcatheter aortic valve replacement (TAVR) is the standard of care in a large population of patients with severe symptomatic aortic valve stenosis. The sizing of TAVR devices is done from ECG-gated CT angiographic image volumes. The most crucial step of the analysis is the determination of the aortic valve annular plane. In this paper, we present a fully tridimensional recursive multiresolution convolutional neural network (CNN) to infer the location and orientation of the aortic valve annular plane. METHODS: We manually labeled 1007 ECG-gated CT volumes from 94 patients with severe degenerative aortic valve stenosis. The algorithm was implemented and trained using the TensorFlow framework (Google LLC, USA). We performed K-fold cross-validation with K = 9 groups such that CT volumes from a given patient are assigned to only one group. RESULTS: We achieved an average out-of-plane localization error of (0.7 ± 0.6) mm for the training dataset and of (0.9 ± 0.8) mm for the evaluation dataset, which is on par with other published methods and clinically insignificant. The angular orientation error was (3.9 ± 2.3)° for the training dataset and (6.4 ± 4.0)° for the evaluation dataset. For the evaluation dataset, 84.6% of evaluation image volumes had a better than 10° angular error, which is similar to expert-level accuracy. When measured in the inferred annular plane, the relative measurement error was (4.73 ± 5.32)% for the annular area and (2.46 ± 2.94)% for the annular perimeter. CONCLUSIONS: The proposed algorithm is the first application of CNN to aortic valve planimetry and achieves an accuracy on par with proposed automated methods for localization and approaches an expert-level accuracy for orientation. The method relies on no heuristic specific to the aortic valve and may be generalizable to other anatomical features.

Optimal Fluoroscopic Projections of Coronary Ostia and Bifurcations Defined by Computed Tomographic Coronary Angiography.

OBJECTIVES: The aim of this study was to define the optimal fluoroscopic viewing angles of both coronary ostia and important coronary bifurcations by using 3-dimensional multislice computed tomographic data. BACKGROUND: Optimal fluoroscopic projections are crucial for coronary imaging and interventions. Historically, coronary fluoroscopic viewing angles were derived empirically from experienced operators. METHODS: In this analysis, 100 consecutive patients who underwent computed tomographic coronary angiography (CTCA) for suspected coronary artery disease were studied. A CTCA-based method is described to define optimal viewing angles of both coronary ostia and important coronary bifurcations to guide percutaneous coronary interventions. RESULTS: The average optimal viewing angle for ostial left main stenting was left anterior oblique (LAO) 37°, cranial (CRA) 22° (95% confidence interval [CI]: LAO 33° to 40°, CRA 19° to 25°) and for ostial right coronary stenting was LAO 79°, CRA 41° (95% CI: LAO 74° to 84°, CRA 37° to 45°). Estimated mean optimal viewing angles for bifurcation stenting were as follows: left main: LAO 0°, caudal (CAU) 49° (95% CI: right anterior oblique [RAO] 8° to LAO 8°, CAU 43° to 54°); left anterior descending with first diagonal branch: LAO 11°, CRA 71° (95% CI: RAO 6° to LAO 27°, CRA 66° to 77°); left circumflex bifurcation with first marginal branch: LAO 24°, CAU 33° (95% CI: LAO 15° to 33°, CAU 25° to 41°); and posterior descending artery and posterolateral branch: LAO 44°, CRA 34° (95% CI: LAO 35° to 52°, CRA 27° to 41°). CONCLUSIONS: CTCA can suggest optimal fluoroscopic viewing angles of coronary artery ostia and bifurcations. As the frequency of use of diagnostic CTCA increases in the future, it has the potential to provide additional information for planning and guiding percutaneous coronary intervention procedures.

Optimal fluoroscopic viewing angles of right-sided heart structures in patients with tricuspid regurgitation based on multislice computed tomography.

AIMS: This study sought to analyse multislice computed tomography (MSCT) data of patients with tricuspid regurgitation and to report the variability of fluoroscopic viewing angles for several right-sided heart structures, as well as chamber views of the right heart in order to determine the optimal fluoroscopic viewing angles of six right-sided heart structures and right-heart chamber views. METHODS AND RESULTS: The MSCT data of 44 patients with mild to severe tricuspid regurgitation (TR) were retrospectively analysed. For each patient, we determined the optimal fluoroscopic viewing angles of the annulus/orifice en face view of the tricuspid valve, atrial septum, superior vena cava (SVC), inferior vena cava (IVC), coronary sinus (CS) and pulmonary valve. In this TR patient cohort, the average fluoroscopic viewing angle for the en face view of the tricuspid valve annulus was LAO 54-CAUD 15; RAO 10-CAUD 66 for the SVC orifice; LAO 27-CRA 59 for the IVC orifice; RAO 28-CRA 19 for the CS orifice; RAO 33-CAUD 33 for the atrial septum and LAO 13-CAUD 52 for the pulmonary valve annulus. The average viewing angle for right-heart chamber views was LAO 55-CAUD 15 for the one-chamber view; RAO 59-CAUD 54 for the two-chamber view; RAO 27-CRA 19 for the three-chamber view and LAO 5-CRA 60 for the four-chamber view. CONCLUSIONS: MSCT can provide patient-specific fluoroscopic viewing angles of right-sided heart structures. This information may facilitate transcatheter right-heart interventions.

Multimodality imaging for interventional cardiologists.

Performing transcatheter valve interventions requires a thorough knowledge of right heart imaging. Integration of chamber views across the spectrum of imaging modalities (i.e., multislice computed tomography, fluoroscopy, and echocardiography) can facilitate complex transcatheter interventions. Optimal fluoroscopic viewing angles for guiding interventional procedures can be obtained using preprocedural multislice computed tomography scans. The present manuscript describes the fluoroscopic viewing angles necessary, when using multislice computed tomography, to appreciate heart chamber anatomy and their relationship to echocardiography.

Fluoroscopic Anatomy of Right-Sided Heart Structures for Transcatheter Interventions.

Performing transcatheter tricuspid valve interventions requires a thorough knowledge of right-heart imaging. Integration of chamber views across the spectrum of imaging modalities (i.e., multislice computed tomography, fluoroscopy, and echocardiography) can facilitate transcatheter interventions on the right heart. Optimal fluoroscopic viewing angles for guiding interventional procedures can be obtained using pre-procedural multislice computed tomography scans. The present paper describes fluoroscopic viewing angles necessary to appreciate right-heart chamber anatomy and their relationship to echocardiography using multislice computed tomography.

Transcatheter Mitral Paravalvular Leak Closure Facilitated by Preprocedural Cardiac CT for Simulation of Fluoroscopic Anatomy and Paravalvular Defect Localization.

Paravalvular leakage (PVL) occurs in 6%-15% of cases after surgical heart valve replacement. A percutaneous approach is increasingly used to close PVLs as an alternative to repeat surgery. Computed tomography (CT) can be used for simulation of fluoroscopic cardiac anatomy. This technique allows preprocedural definition of optimal C-arm angulations and PVL localization in reference to fluoroscopic views. It is very helpful for guidewire crossing of the PVL and positioning of the closure device. We report a case with the first use of dedicated software for fluoroscopic simulation (FluoroCT) in transcatheter mitral PVL closure.

Transcatheter aortic valve implantation versus redo surgery for failing surgical aortic bioprostheses: a multicentre propensity score analysis.

AIMS: Transcatheter aortic valve implantation for a failing surgical bioprosthesis (TAV-in-SAV) has become an alternative for patients at high risk for redo surgical aortic valve replacement (redo-SAVR). Comparisons between these approaches are non-existent. This study aimed to compare clinical and echocardiographic outcomes of patients undergoing TAV-in-SAV versus redo-SAVR after accounting for baseline differences by propensity score matching. METHODS AND RESULTS: Patients from seven centres in Europe and Canada who had undergone either TAV-in-SAV (n=79) or redo-SAVR (n=126) were identified. Significant independent predictors used for propensity scoring were age, NYHA functional class, number of prior cardiac surgeries, urgent procedure, pulmonary hypertension, and COPD grade. Using a calliper range of ±0.05, a total of 78 well-matched patient pairs were found. All-cause mortality was similar between groups at 30 days (6.4% redo-SAVR vs. 3.9% TAV-in-SAV; p=0.49) and one year (13.1% redo-SAVR vs. 12.3% TAV-in-SAV; p=0.80). Both groups also showed similar incidences of stroke (0% redo-SAVR vs. 1.3% TAV-in-SAV; p=1.0) and new pacemaker implantation (10.3% redo-SAVR vs. 10.3% TAV-in-SAV; p=1.0). The incidence of acute kidney injury requiring dialysis was numerically lower in the TAV-in-SAV group (11.5% redo-SAVR vs. 3.8% TAV-in-SAV; p=0.13). The TAV-in-SAV group had a significantly shorter median total hospital stay (12 days redo-SAVR vs. 9 days TAV-in-SAV; p=0.001). CONCLUSIONS: Patients with aortic bioprosthesis failure treated with either redo-SAVR or TAV-in-SAV have similar 30-day and one-year clinical outcomes.

Fluoroscopic "heart chamber" anatomy - the case for imaging modality-independent terminology.

Interventional cardiologists have traditionally relied upon fluoro-scopic imaging for percutaneous coronary interventions. Transcatheter structural heart interventions, however, require additional imaging modalities such as echocardiography and multislice computed tomography (MSCT) for pre-, intra- and post-procedural assistance. MSCT has emerged as the critical imaging modality for patient and device selection prior to transcatheter structural heart interventions. MSCT is unique as it provides a complete 3-dimensional (3D) dataset of the heart and vasculature that is amenable to multiplanar reconstruction for 2-dimensional (2D) or volume-rendered interpretations. Herein, we present a modality-independent terminology for understanding volumetric images in the context of transcatheter heart valve therapies. The goal of this system is to allow physicians to readily interpret the orientation of fluoroscopic, MSCT, echocardiographic and MRI images, thus generalising their understanding of cardiac anatomy to all imaging modalities.