MACE

The aim of this study is to evaluate the risk of serious adverse cardiovascular events in adolescent and adult patients with severe asthma taking tezepelumab compared to a population receiving standard of care treatment for severe asthma.

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.

There are 28 non-cardiology medications from multiple families costing more than $13 billion annually in Canada, categorized as 'Known' QT-prolonging medications (QTPmeds) based on very low levels of evidence. The association between many commonly used medications listed as known QTPmeds and actual major adverse cardiac events (MACE) is weak. Meanwhile, QTPmeds-related warnings are ubiquitous in every healthcare setting, triggering 'hard stop' disruption millions of times per day to front line clinicians. Poor quality medication safety alerts are increasingly recognized as a source of inferior patient care and provider burnout which detracts from healthcare sustainability. In this study, anonymized hospital electronic medical record data from more than 990,000 adult patients across Ontario will be used to compare patients who experience MACE with those who do not, measuring their real-time exposure to QT-prolonging drugs. Additionally, machine-learning techniques will also be used to find which patient or treatment factors best predict risk. The objectives of this study are to 1) Investigate whether exposure to one or more 'Known' QTPmed is associated with an increased risk of MACE after adjusting for confounders; and 2) Identify predictors and their relative importance for QTPmeds-associated MACE. In summary, QT-prolonging medications have the potential to cause very serious adverse events, including death. However, it is not sufficiently clear which patients under which circumstances suffer events, or when is QT prolongation a useful surrogate marker for harm. Meanwhile, ubiquitous medication alerts related to QT-prolonging medications are at best imprecise and at worst, misleading, costly and potentially dangerous. Now that data resources are available with the data elements, structure and sample size required to rigorously assess this association, this study will address this question to improve patient safety, provider satisfaction and the cost-effectiveness of care.

Postimplantation syndrome (PIS) is a common and clinically important complication following thoracic endovascular aortic repair (TEVAR). PIS is characterized by a strong systemic inflammatory response to the stent-graft implantation and is manifested by flu-like symptoms, which include fever, increased white blood count, increased levels of acute phase proteins, and fatigue, but without a clear inflammatory and infective cause. Besides, it has been demonstrated that PIS is associated with prolonged hospital stay and increased risk for postoperative complications, including acute kidney injury, postoperative delirium, and increased postoperative pain scores. Recently, there has been increasing evidence that PIS is associated with an increased risk of major adverse cardiac events (MACE) and perioperative myocardial injury. Observational studies suggest that preoperative administration of glucocorticoids may decrease the incidence of PIS after TEVAR and EVAR procedures. However, to date, there are no randomised trials that have investigated whether preoperative administration of glucocorticoids can reduce the incidence of PIS and its associated poorer treatment outcomes following TEVAR. This randomized controlled trial was designed to investigate the effect of glucocorticoid administration on reducing the incidence and improving the outcome of patients who develop PIS after TEVAR.

we aim : * To evaluate how frailty influences acute management decisions (invasive vs conservative strategy) in ACS patients and whether it independently affects short-term outcomes. * To determine whether adding frailty assessment to existing ACS risk prediction models improves the prediction of 30-day mortality and major adverse cardiovascular events (MACE) in elderly ACS patients.

this study aims to assess the short-term (3-month) clinical and echocardiographic outcomes of primary PCI among STEMI patients from rural versus urban areas in Upper Egypt. A special focus will be placed on identifying predictors of MACE. The findings may offer valuable insight into optimizing STEMI care pathways and support the development of regional STEMI networks across Egypt.