The high-risk strategy for prevention of myocardial infarction is insufficient as it is applied today, especially considering the high occurrence of silent myocardial infarctions, and the fact that sudden cardiac death is the presenting feature in one out of five myocardial infarctions. Hence, population or screening strategies are necessary. Understanding the prevalence and characteristics of coronary artery stenosis and plaques in the general population is key for determining the potential usefulness of screening programs for targeting preventive efforts. The Swedish CArdioPulmonary BioImage Study (SCAPIS) was designed for this specific purpose.
The project aims at using both traditional statistical methods as well as modern machine learning alorithms to identify important predictors for different outcomes relating to the location and extent of coronary artery stenoses. While the actual data are not large we anticipate that 50GB of storage should be adequate to store the data as well as output from the statistical software.
The project uses personal health data obtained on individuals and can thus be considered sensitive.