Progression to Myocardial Infarction Short-Term Death Based on Interval Sequential Pattern Mining

Abstract Background Cardiovascular disease (CVD) is the leading cause of death globally, contributing to 32% of all global deaths. Moreover, myocardial infarction (MI) causes 11.9% of deaths among CVD patients. According to our Taiwan health insurance database analysis, the hazard rate reaches a peak in the initial year after diagnosis, drops to a relatively low value, and maintains stability for the following years. Therefore, identifying suspicious comorbidities before the diagnosis that may lead MI patients to short-term death is paramount. Methods Interval sequential pattern mining was applied with odds ratio to the hospitalization records from the Taiwan health insurance research database to evaluate the disease progression and identify potential subjects at the earliest stage possible. Results Our analysis resulted in five disease pathways, including “diabetes mellitus,” “other disorders of the urethra and urinary tract,” “essential hypertension,” “hypertensive heart disease,” and “other forms of chronic ischemic heart disease” that led to short-term death after MI diagnosis, and these pathways covered half of the cohort. Conclusion We explored the possibility of establishing trajectory patterns to identify the high-risk population of early mortality after MI.