Progression to myocardial infarction short-term death based on interval sequential pattern mining.

Area of Science:

• Cardiology
• Medical Informatics
• Public Health

Background:

• Myocardial infarction (MI) is a major cardiovascular disease (CVD).
• Taiwanese health data shows a high early mortality risk after MI diagnosis, stabilizing over time.
• Early identification of risk factors is crucial for improving MI patient survival.

Purpose of the Study:

• To identify comorbidity patterns preceding MI diagnosis that predict short-term mortality.
• To explore trajectory patterns for early identification of high-risk MI patients.

Main Methods:

• Utilized interval sequential pattern mining and odds ratio analysis.
• Analyzed hospitalization records from the Taiwan National Health Insurance Research Database.
• Evaluated disease progression to identify at-risk individuals early.

Main Results:

• Identified five key disease pathways associated with short-term death post-MI.
• These pathways include diabetes mellitus, urinary tract disorders, essential hypertension, hypertensive heart disease, and chronic ischemic heart disease.
• These identified pathways accounted for half of the study cohort.

Conclusions:

• Established disease trajectory patterns can identify MI patients at high risk of early mortality.
• Early detection of specific comorbidity patterns can guide interventions to improve long-term outcomes for MI survivors.