Population Data Science in Cardiovascular Medicine

Big data in medicine is becoming very popular these days because clinical observations can be investigated and defined further using real-world data, multidisciplinary medicine between subspecialities (i.e. cardiology and nephrology or hepatology) which are difficult to study traditionally can be conducted using national healthcare and insurance data. For example, the study of safety of efficacy of non-vitamin K antagonist oral anticoagulant (NOAC) in patients who are excluded in the clinical trials such as patients with end-stage renal disease, abnormal liver function, anemia, etc. On the other hand, rare adverse event of specific drug or drug-drug interaction can be analyzed in large population, such as association between use of NOAC with concurrent medications in the risk of major bleeding in patients with non-valvular atrial fibrillation.

Potential rare drug side effect, drug-drug interaction (risk of hypoglycemia in patients taking certain anti-diabetic drugs such as dipeptidyl peptidase-4 inhibitor [DPP4i] or sodium glucose co-transporters 2 inhibitor [SGLT2i] and concurrent medications), multidisciplinary medicine between subspecialities (risk of cardiovascular events in patients with hepatitis), potential efficacy and safety of drugs in specific patient population (such as safety and efficacy of non-vitamin K antagonist oral anticoagulant [NOAC] in patients with atrial fibrillation and dilated left atrium), and patients who are excluded in clinical trials can be studied and the observations can be defined using big data (i.e., efficacy and safety of oral anticoagulants in patients with atrial fibrillation and chronic kidney disease).

The Research Topic is looking for papers on interdisciplinary medicine, medication safety and efficacy, treatment outcomes, drug-drug interactions, big data study, population data science, national insurance data study, national cohort study, real word data.