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Progression to myocardial infarction short-term death based on interval sequential pattern mining
Yang-Sheng Wu1, David Taniar2, Kiki Adhinugraha3
1Computer Science and Information Engineering, National Taipei University of Technology, Taipei, 106344, Taiwan.
Insights
Identifying pre-diagnosis comorbidities like diabetes and hypertension can predict short-term death risk in myocardial infarction (MI) patients. This aids in early intervention for prolonged survival in cardiovascular disease (CVD) patients.
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.
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.
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