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Background & Methods: This paper describes a pilot application of the Epidemic Volatility Index (EVI) to data from the pulmonary clinic of the University Hospital of Thessaly, Greece, for monitoring respiratory infections, COVID-19, and flu cases. EVI, a simple and easily implemented early warning method based on the volatility of newly reported cases, exhibited consistent and stable performance in detecting new waves of epidemics. The study highlights the importance of implementing early warning tools to address the effects of epidemics, including containment of outbreaks, timely intervention strategies, and resource allocation within real-world clinical settings as part of a broader public health strategy., Results: The results presented in the figures demonstrate the association between successive early warnings and the onset of new waves, providing valuable insights for proactive decision-making. A web-based application enabling real-time monitoring and informed decision-making by healthcare professionals, public health officials, and policymakers was developed., Conclusions: This study emphasizes the significant role of early warning methods in managing epidemics and safeguarding public health. Future research may explore extensions and combinations of multiple warning systems for optimal outbreak interventions and application of the methods in the context of personalized medicine., Competing Interests: Declaration of Competing Interest The authors have no competing or any other interests that might be perceived to influence the results and/or discussion reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Background: Despite hypertrophic cardiomyopathy (HCM) being the most common inherited heart disease and conferring increased risk for heart failure (HF) and sudden cardiac death (SCD), risk assessment in HCM patients is still largely unresolved., Objectives: This study aims to synthesize and compare the prognostic impact of demographic, clinical, biochemical, and imaging findings in patients with HCM., Methods: The authors searched PubMed, Embase, and Cochrane Library for studies published from 1955 to November 2020, and the endpoints were: 1) all-cause death; 2) an arrhythmic endpoint including SCD, sustained ventricular tachycardia, ventricular fibrillation, or aborted SCD; and 3) a composite endpoint including (1) or (2) plus hospitalization for HF or cardiac transplantation. The authors performed a pairwise meta-analysis obtaining the pooled estimate separately for the association between baseline variables and study endpoints. A random-effects network meta-analysis was subsequently used to comparatively assess the prognostic value of outcome associates., Results: A total of 112 studies with 58,732 HCM patients were included. Among others, increased brain natriuretic peptide/N-terminal pro-B-type natriuretic peptide, late gadolinium enhancement (LGE), positive genotype, impaired global longitudinal strain, and presence of apical aneurysm conferred increased risk for the composite endpoint. At network meta-analysis, LGE showed the highest prognostic value for all endpoints and was superior to all other associates except New York Heart Association functional class >class II. A multiparametric imaging-based model was superior in predicting the composite endpoint compared to a prespecified model based on conventional risk factors., Conclusions: This network meta-analysis supports the development of multiparametric risk prediction algorithms, including advanced imaging markers additively to conventional risk factors, for refined risk stratification in HCM. (Long-term prognosis of hypertrophic cardiomyopathy according to genetic, clinical, biochemical and imaging findings: a systemic review and meta-analysis; CRD42020185219)., Competing Interests: Funding Support and Author Disclosures Dr Georgiopoulos was supported by a training grant from the European Association of Cardiovascular Imaging (training grant 2021). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)