Cost-effectiveness Analysis

This trial will prospectively evaluate the impact of integrating AI-ECG within the pay-for-performance program on improving the diagnosis, treatment, and clinical outcomes of occlusion myocardial infarction patients by promoting accurate and timely diagnoses through financial incentives.

The research project titled "A Comparative Study on the Clinical Efficacy, Quality of Life, and Cost of Use of Peripheral Defocus Spectacles, Frame Glasses, and Orthokeratology Lenses in Myopic Children and Adolescents" aims to evaluate different non-surgical myopia correction methods in children. It focuses on assessing the impact of peripheral defocus spectacles, frame glasses, and orthokeratology lenses on the quality of life, clinical effectiveness, and costs associated with each method. The study is a prospective cohort study involving 90 children aged 8-17 years with myopia ranging from -1.00D to -6.00D. It aims to compare the psychological, social, and educational aspects of these correction methods, alongside their costs and clinical outcomes over a period of one year.

Cardiovascular diseases are the leading cause of mortality from treatable conditions in the European Union and the second from preventable causes, with a standardized mortality rate of 257.8 deaths per 100,000 inhabitants. In 2022, more than 1.11 million deaths in individuals under 75 years could have been avoided. Atrial fibrillation (AF) and major adverse cardiovascular events (MACE) are highly prevalent in the elderly and generate substantial healthcare costs. AF significantly increases the risk of MACE and is projected to rise markedly in the coming decades. In Europe, AF prevalence is expected to increase 2.5-fold over the next 50 years, with a lifetime risk of 1 in 3-5 individuals after age 55. AF-related strokes are projected to increase by 34%, and ischemic strokes in individuals over 80 are expected to triple between 2016 and 2060. Additionally, a 27% increase is anticipated among stroke survivors who subsequently develop AF or related conditions. AF substantially impacts morbidity, mortality, and disease progression, and early detection and treatment are crucial to prevent severe outcomes. European action plans (2018-2030) and the 2024 ESC/ESO guidelines emphasize early detection and management of AF in primary care. Although several AF prediction models exist, their integration into clinical practice remains challenging. AF represents a clinical continuum, with thrombotic risk present even before arrhythmia onset. High-risk patients for AF also show a high incidence of MACE, defined as a composite of myocardial infarction, stroke, systemic embolic events, and cardiovascular death. The proposed strategy involves developing and clinically validating an Artificial Intelligence (AI) model to improve early thrombotic risk prediction in patients at high risk of AF, using MACE as the primary outcome. This model aims to outperform the traditional CHA₂DS₂-VASc score by incorporating both classical and emerging clinical factors. The estimated timeline from clinical validation to commercialization is approximately 48 months. AI-based prediction is expected to enable personalized treatment, reduce the incidence of MACE, hospitalizations, and disability, and improve cost-effectiveness, ultimately decreasing the social and economic burden of AF and stroke in Europe.