Bhanushali, Aditi Kishor

Abstract Background Inflammation plays a potential role in heart failure (HF) etiology and prognosis. Neutrophil-to-Lymphocyte ratio (NLR) is a simple, routinely available lab criteria. In this meta-analysis, we sought to investigate the role of NLR on HF outcomes which remains unexplored. Methods PubMed, Embase and Scopus were systematically screened to find studies through August 2022, reporting data on association of HF related outcomes [all-cause mortality (ACM), cardiovascular (CV) death, in-hospital, HF readmissions and composite events] and NLR. Random effects models and I2 statistics were used to estimate pooled odds of outcomes and heterogeneity. Leave one out sensitivity analysis was performed. Results A total of 11 studies (6 retrospective and 5 prospective, 4 studies from Asia and Europe each, 2 from the US and 1 multicenter study) including 12954 patients (mean age: 69 years, Males 57.2%) from 2011 to 2022 with an average follow up duration of 54.8 months were included. Elevated NLR was significantly associated with ACM (aOR 1.80, 95%CI 1.46-2.22, p<0.01, Table 1), HF readmissions (aOR 2.53, 95% CI 1.60-3.99), in-hospital mortality (aOR 3.26, 95% CI 1.76- 6.02), CV mortality (aOR 1.84, 95%CI 1.03-3.31) and composite CV outcome including heart transplant (OR 2.07, 95% CI 1.32-3.25), p<0.001. Conclusion Elevated NLR was significantly associated with worse outcomes in HF patients. The NLR test might potentially be useful in identifying high-risk HF patients and curtail worse outcomes.

The interplay between neurology and cardiology has gained significant attention in recent years, particularly regarding the shared pathophysiological mechanisms and clinical comorbidities observed in epilepsy and arrhythmias. Neuro-cardiac electrophysiology mapping involves the comprehensive assessment of both neural and cardiac electrical activity, aiming to unravel the intricate connections and potential cross-talk between the brain and the heart. The emergence of artificial intelligence (AI) has revolutionized the field by enabling the analysis of large-scale data sets, complex signal processing, and predictive modeling. AI algorithms have been applied to neuroimaging, electroencephalography (EEG), electrocardiography (ECG), and other diagnostic modalities to identify subtle patterns, classify disease subtypes, predict outcomes, and guide personalized treatment strategies. In this review, we highlight the potential clinical implications of neuro-cardiac mapping and AI in the management of epilepsy and arrhythmias. We address the challenges and limitations associated with these approaches, including data quality, interpretability, and ethical considerations. Further research and collaboration between neurologists, cardiologists, and AI experts are needed to fully unlock the potential of this interdisciplinary field.

Atrial fibrillation (AF) is the most persistent arrhythmia today, with its prevalence increasing exponentially with the rising age of the population. Particularly at elevated heart rates, a functional abnormality known as cardiac alternans can occur prior to the onset of lethal arrhythmias. Cardiac alternans are a beat-to-beat oscillation of electrical activity and the force of cardiac muscle contraction. Extensive evidence has demonstrated that microvolt T-wave alternans can predict ventricular fibrillation vulnerability and the risk of sudden cardiac death. The majority of our knowledge of the mechanisms of alternans stems from studies of ventricular electrophysiology, although recent studies offer promising evidence of the potential of atrial alternans in predicting the risk of AF. Exciting preclinical and clinical studies have demonstrated a link between atrial alternans and the onset of atrial tachyarrhythmias. Here, we provide a comprehensive review of the clinical utility of atrial alternans in identifying the risk and guiding treatment of AF.