Cardiac Resynchronization Therapy in Non-Ischemic Cardiomyopathy: Role of Multimodality Imaging

Understanding Arrhythmia-Induced Cardiomyopathy: Symptoms and Treatments

Arrhythmia-induced cardiomyopathy is a complex condition that causes a decline in heart function as a result of irregular heart rhythms. This disorder highlights the link between irregular heart rhythm and heart failure, necessitating prompt identification and intervention. It often occurs due to ongoing fast heart rhythms like atrial fibrillation or tachycardia. Understanding the mechanisms, symptoms, and available treatments is essential for enhancing patient outcomes given the complicated nature of the condition. This article delves into various aspects of arrhythmia-induced cardiomyopathy, including pathogenesis, clinical presentation, diagnostic methods, epidemiology, typical arrhythmias associated with the condition, and management options. It assesses patients' future outlook and necessary follow-up, aiming to provide healthcare providers with a comprehensive understanding of how to handle this intricate condition. The article emphasizes the important effect an integrative approach can have on both patients' lives and the clinical consequences of diagnosing and treating this condition. This extensive understanding enhances the resources at the disposal of physicians, enabling targeted treatments that enhance cardiomyopathy by targeting arrhythmia regulation. More research and development are needed in the field of cardiomyopathy and arrhythmia relationship. The presentation urges the medical field to delve deeper into the complexities of illness by emphasizing the need for continuous research and a multifaceted treatment plan. By combining these understandings, our goal is to enhance patient outcomes and create opportunities for further studies on cardiovascular wellness.

The Prognostic Role of Global Longitudinal Strain and NT-proBNP in Heart Failure Patients Receiving Cardiac Resynchronization Therapy

BACKGROUND

We aimed to evaluate whether baseline GLS (global longitudinal strain), NT-proBNP, and changes in these after cardiac resynchronization therapy (CRT) can predict long-term clinical outcomes and the echocardiographic-based response to CRT (defined by 15% relative reduction in left ventricular end-systolic volume).

METHODS

We enrolled 143 patients with stable ischemic heart failure (HF) undergoing CRT-D implantation. NT-proBNP and echocardiography were obtained before and 6 months after. The patients were followed up (median: 58 months) for HF-related deaths and/or HF hospitalizations (primary endpoint) or HF-related deaths (secondary endpoint).

RESULTS

A total of 84 patients achieved the primary and 53 the secondary endpoint, while 104 patients were considered CRT responders and 39 non-responders. At baseline, event-free patients had higher absolute GLS values (p < 0.001) and lower NT-proBNP serum levels (p < 0001) than those achieving the primary endpoint. A similar pattern was observed in favor of CRT responders vs. non-responders. On Cox regression analysis, baseline absolute GLS value (HR = 0.77; 95% CI, 0.51-1.91; p = 0.002) was beneficially associated with lower primary endpoint incidence, while baseline NT-proBNP levels (HR = 1.55; 95% CI, 1.43-2.01; p = 0.002) and diabetes presence (HR = 1.27; 95% CI, 1.12-1.98; p = 0.003) were related to higher primary endpoint incidence.

CONCLUSIONS

In HF patients undergoing CRT-D, baseline GLS and NT-proBNP concentrations may serve as prognostic factors, while they may predict the echocardiographic-based response to CRT.

The Role of AI in Characterizing the DCM Phenotype

Dilated Cardiomyopathy is conventionally defined by left ventricular dilatation and dysfunction in the absence of coronary disease. Emerging evidence suggests many patients remain vulnerable to major adverse outcomes despite clear therapeutic success of modern evidence-based heart failure therapy. In this era of personalized medical care, the conventional assessment of left ventricular ejection fraction falls short in fully predicting evolution and risk of outcomes in this heterogenous group of heart muscle disease, as such, a more refined means of phenotyping this disease appears essential. Cardiac MRI (CMR) is well-placed in this respect, not only for its diagnostic utility, but the wealth of information captured in global and regional function assessment with the addition of unique tissue characterization across different disease states and patient cohorts. Advanced tools are needed to leverage these sensitive metrics and integrate with clinical, genetic and biochemical information for personalized, and more clinically useful characterization of the dilated cardiomyopathy phenotype. Recent advances in artificial intelligence offers the unique opportunity to impact clinical decision making through enhanced precision image-analysis tasks, multi-source extraction of relevant features and seamless integration to enhance understanding, improve diagnosis, and subsequently clinical outcomes. Focusing particularly on deep learning, a subfield of artificial intelligence, that has garnered significant interest in the imaging community, this paper reviews the main developments that could offer more robust disease characterization and risk stratification in the Dilated Cardiomyopathy phenotype. Given its promising utility in the non-invasive assessment of cardiac diseases, we firstly highlight the key applications in CMR, set to enable comprehensive quantitative measures of function beyond the standard of care assessment. Concurrently, we revisit the added value of tissue characterization techniques for risk stratification, showcasing the deep learning platforms that overcome limitations in current clinical workflows and discuss how they could be utilized to better differentiate at-risk subgroups of this phenotype. The final section of this paper is dedicated to the allied clinical applications to imaging, that incorporate artificial intelligence and have harnessed the comprehensive abundance of data from genetics and relevant clinical variables to facilitate better classification and enable enhanced risk prediction for relevant outcomes.