Cardiovascular disease (CVD) accounts for the majority of death and hospitalization, health care expenditures and loss of productivity in developed country. CVD research, thus, plays a key role for improving patients' outcomes as well as for the sustainability of health systems. The increasing costs and complexity of modern medicine along with the fragmentation in healthcare organizations interfere with improving quality care and represent a missed opportunity for research. The advancement in diagnosis, therapy and prognostic evaluation of patients with CVD, indeed, is frustrated by limited data access to selected small patient populations, not standardized nor computable definition of disease and lack of approved relevant patient-centered outcomes. These critical issues results in a deep mismatch between randomized controlled trials and real-world setting, heterogeneity in treatment response and wide inter-individual variation in prognosis. Big data approach combines millions of people's electronic health records (EHR) from different resources and provides a new methodology expanding data collection in three direction: high volume, wide variety and extreme acquisition speed. Large population studies based on EHR holds much promise due to low costs, diminished study participant burden, and reduced selection bias, thus offering an alternative to traditional ascertainment through biomedical screening and tracing processes. By merging and harmonizing large data sets, the researchers aspire to build algorithms that allow targeted and personalized CVD treatments. In current paper, we provide a critical review of big health data for cardiovascular research, focusing on the opportunities of this largely free data analytics and the challenges in its realization.

Acute coronary syndromes (ACS) are frequently reported in patients with coronavirus disease 2019 (COVID‐19) and may impact patient clinical course and mortality. Although the underlying pathogenesis remains unclear, several potential mechanisms have been hypothesized, including oxygen supply/demand imbalance, direct viral cellular damage, systemic inflammatory response with cytokine‐mediated injury, microvascular thrombosis, and endothelial dysfunction. The severe hypoxic state, combined with other conditions frequently reported in COVID‐19, namely sepsis, tachyarrhythmias, anemia, hypotension, and shock, can induce a myocardial damage due to the mismatch between oxygen supply and demand and results in type 2 myocardial infarction (MI). In addition, COVID‐19 promotes atherosclerotic plaque instability and thrombus formation and may precipitate type 1 MI. Patients with severe disease often show decrease in platelets count, higher levels of d‐dimer, ultralarge von Willebrand factor multimers, tissue factor, and prolongation of prothrombin time, which reflects a prothrombotic state. An endothelial dysfunction has been described as a consequence of the direct viral effects and of the hyperinflammatory environment. The expression of tissue factor, von Willebrand factor, thromboxane, and plasminogen activator inhibitor‐1 promotes the prothrombotic status. In addition, endothelial cells generate superoxide anions, with enhanced local oxidative stress, and endothelin‐1, which affects the vasodilator/vasoconstrictor balance and platelet aggregation. The optimal management of COVID‐19 patients is a challenge both for logistic and clinical reasons. A deeper understanding of ACS pathophysiology may yield novel research insights and therapeutic perspectives in higher cardiovascular risk subjects with COVID‐19.