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Identification of acute myocardial infarction and stroke events using the National Health Insurance Service database
Minsung Cho1, Hyeok-Hee Lee2,3,4, Jang-Hyun Baek5
1Department of Public Health, Yonsei University Graduate School, Seoul, Korea.
Insights
Algorithms were developed to accurately identify acute myocardial infarction (AMI) and stroke events using Korean National Health Insurance Service data. These tools show high precision, aiding reliable national cardiovascular disease (CVD) statistics.
Area of Science:
- Public Health
- Epidemiology
- Health Informatics
Background:
- Cardiovascular disease (CVD), including acute myocardial infarction (AMI) and stroke, is a major cause of death and disability globally and in Korea.
- Accurate identification of CVD events is crucial for public health surveillance and policy development.
Purpose of the Study:
- To develop and validate algorithms for identifying AMI and stroke events using the National Health Insurance Service (NHIS) database in Korea.
- To assess the accuracy of these algorithms through medical record review.
Main Methods:
- Defined "hospitalization episode" considering the characteristics of the NHIS claims database.
- Developed algorithms for first and recurrent AMI and stroke event identification.
- Validated algorithm performance by calculating positive predictive values (PPVs) against medical records.
Main Results:
- Algorithms for both AMI and stroke event identification were successfully developed.
- Validation involved reviewing 3,140 algorithm-identified events (1,399 AMI, 1,741 stroke) across 24 hospitals.
- Overall PPVs were approximately 92% for first AMI, 78% for recurrent AMI, 88% for first stroke, and 81% for recurrent stroke.
Conclusions:
- Developed algorithms accurately identify AMI and stroke events from the NHIS database.
- The algorithms demonstrate high PPVs, around 90% for initial events and 80% for recurrent events.
- These validated algorithms can support consistent and reliable national CVD statistics in Korea.
Objectives:
The escalating burden of cardiovascular disease (CVD) is a critical public health issue worldwide. CVD, especially acute myocardial infarction (AMI) and stroke, is the leading contributor to morbidity and mortality in Korea. We aimed to develop algorithms for identifying AMI and stroke events from the National Health Insurance Service (NHIS) database and validate these algorithms through medical record review.
Methods:
We first established a concept and definition of "hospitalization episode," taking into account the unique features of health claims-based NHIS database. We then developed first and recurrent event identification algorithms, separately for AMI and stroke, to determine whether each hospitalization episode represents a true incident case of AMI or stroke. Finally, we assessed our algorithms' accuracy by calculating their positive predictive values (PPVs) based on medical records of algorithm- identified events.
Results:
We developed identification algorithms for both AMI and stroke. To validate them, we conducted retrospective review of medical records for 3,140 algorithm-identified events (1,399 AMI and 1,741 stroke events) across 24 hospitals throughout Korea. The overall PPVs for the first and recurrent AMI events were around 92% and 78%, respectively, while those for the first and recurrent stroke events were around 88% and 81%, respectively.
Conclusions:
We successfully developed algorithms for identifying AMI and stroke events. The algorithms demonstrated high accuracy, with PPVs of approximately 90% for first events and 80% for recurrent events. These findings indicate that our algorithms hold promise as an instrumental tool for the consistent and reliable production of national CVD statistics in Korea.
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