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.

No abstract

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. [1] 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. In this study, interval sequential pattern mining was applied to the hospitalization records to evaluate the disease progression and identify potential subjects at the earliest stage possible. 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. We hope that our findings will assist in the early identification of patients at risk of short-term death.

Background Myocardial infarction (MI) is one of the significant cardiovascular diseases (CVDs). According to Taiwanese health record analysis, the hazard rate reaches a peak in the initial year after diagnosis of MI, drops to a relatively low value, and maintains stable for the following years. Therefore, identifying suspicious comorbidity patterns of short-term death before the diagnosis may help achieve prolonged survival for MI patients. Methods Interval sequential pattern mining was applied with odds ratio to the hospitalization records from the Taiwan National Health Insurance Research Database to evaluate the disease progression and identify potential subjects at the earliest possible stage. 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.