RESUMEN
This systematic review aims to assess the prognostic implications of supranormal left ventricular ejection fraction (snLVEF) in cardiovascular disease, particularly heart failure (HF), and explore its association with major adverse cardiovascular events (MACE). A comprehensive search of electronic databases was conducted to identify relevant studies examining the relationship between snLVEF and cardiovascular outcomes. Studies utilizing various imaging modalities, including echocardiography, cardiac positron emission tomography, computed tomography, and cardiac magnetic resonance imaging, were included. Data extraction and quality assessment were performed according to predefined criteria. The review identified several studies investigating the association between snLVEF and cardiovascular outcomes. Findings revealed an increased risk of MACE, including HF hospitalization and stroke, in patients with snLVEF, particularly in women. Coronary microvascular dysfunction and autonomic dysregulation were proposed mechanisms underlying these associations. However, conflicting results were observed when focusing exclusively on snLVEF, with some studies reporting similar outcomes between snLVEF and other HF subgroups. snLVEF (>65%) appears to be associated with an elevated risk of MACE, particularly in women, suggesting a U-shaped mortality curve. However, the prognostic implications may vary among HF patients, necessitating further research to elucidate the specific contributions of HF phenotypes and comorbidities. These findings underscore the importance of tailored risk assessment and management strategies for patients with snLVEF, particularly in the context of HF.
RESUMEN
We present the case of a 63-year-old female diagnosed with atypical SSc in the setting of acute SRC. She was undergoing work-up for progressive dyspnoea in the outpatient setting when she was found to have newly diagnosed restrictive lung pathology and worsening renal function, thus prompting acute hospital admission. Given multisystem involvement of the pulmonary and renal systems, the differential diagnosis included autoimmune and connective tissue disorders. Although serologies were non-specific, renal biopsy confirmed scleroderma renal disease, and she was started on treatment with captopril. This case highlights the importance of clinical judgment and timely diagnosis, even when laboratory data might indicate otherwise. LEARNING POINTS: Scleroderma renal crisis (SRC) remains an important cause of morbidity and mortality in systemic sclerosis (SSc), and clinicians should have a high index of suspicion to diagnose it.The absence of specific serologic markers makes SSc diagnosis challenging and necessitates reliance on clinical findings and additional diagnostic tools such as imaging studies and tissue sampling.
RESUMEN
Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.
