Developing a national surveillance system for stroke and acute myocardial infarction using claims data in the Republic of Korea: a retrospective study

Figures

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  Figure 1.. (A) Organization of the study teams. (B) Overview of the study process. AMI, acute myocardial infarction; ICD, International Classification of Diseases; NHIS, National Health Insurance Service; PPV, positive predictive value; NPV, negative predictive value.

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  Figure 2.. Identification algorithms for stroke and acute myocardial infarction (AMI). (A) Any stroke identification algorithm (A-SIA). The stroke identification algorithm used in our study was a 2-stage process based on International Classification of Diseases (ICD) codes. In the first stage, the algorithm identified ischemic stroke cases, while the second stage focused on identifying hemorrhagic stroke cases. Each stage utilized a set of key identifiers associated with clinical practices during acute stroke management. A total of 23 key identifiers were employed in this algorithm. These identifiers included variables related to various aspects of stroke care, such as the admission route, stroke unit care procedures, brain imaging techniques (such as computed tomography [CT], CT angiography, brain magnetic resonance imaging [MRI], and transfemoral cerebral angiography [TFCA]) used for stroke diagnosis. It also included recanalization therapy (comprising intravenous thrombolysis and endovascular therapy) administered as hyperacute management post-ischemic stroke, antithrombotic therapy (including antiplatelet agents and anticoagulants), and interventional therapies (such as carotid…

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  Figure 3.. Two-stage stratified sampling method for the algorithm-positive and algorithm-negative groups. Initially, 6 major administrative divisions and 18 hospitals were selected based on the feasibility of the survey. In the second stage, a specific number of cases from the 18 hospitals were determined for the survey. Ultimately, 6 strata were selected for the survey. The ratio of the numbers of tertiary hospitals to general hospitals was 8:10, based on the 8th Acute Stroke Quality Assessment Program (ASQAP) data, in which the number of tertiary hospitals and general hospitals was 42 and 356, respectively, with a sampling fraction of 20% in tertiary hospitals and 3% in general hospitals. The ratio of the sampled case volume was 1:1 between capital and non-capital regions and 6.5:2.5:1 between tertiary hospitals, general hospitals, and other hospitals, drawing from the 8th ASQAP data. For sampling cases for the hospital survey, stroke and acute myocardial infarction (AMI) algorithms were applied to cases with and without the International Classification of Diseases (ICD) codes. Cases with ICD codes were divided into 2 groups: the algorithm-positive group and the algorithm-negative group.…

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  Figure 4.. Calculation of total sampling rates in each stratum. In the intial stage of our sampling process, total cases were partitioned into 3 groups: (1) algorithm-positive cases with the International Classification of Diseases (ICD) codes, (2) algorithm-negative cases with the ICD codes, and (3) algoritm-negative cases without the ICD codes. Sampling rate 1 was determined by dividing the total number of cases in selected hospitals, after applying the algorithms and ICD codes, by the total number of cases derived from applying the algorithm and ICD codes to the 2018 National Health Insurance Service (NHIS) claims data. This calculation was made for each stratum. Sampling rate 2 was calculated by dividing the number of sampled cases for the hospital survey by the total number of cases in selected hospitals after implementing the algorithms in each stratum. The total sampling rate was calculated by multiplying sampling rate 1 by sampling rate 2. This provided a comprehensive measure of the sampling efficiency for the hospital survey, accounting for both the algorithm-based selection and the actual case sampling.

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  Figure 5.. Estimation of the number of stroke and acute myocardial infarction (AMI) cases in each stratum. Initially, we applied the algorithms to cases both with and without the International Classification of Diseases (ICD) codes to estimate the number of algorithm-positive and algorithm-negative cases. Subsequently, we computed the weighted values for each category. For algorithm-positive cases with the ICD codes, we multiplied the weighted PPV by the number of cases. For algorithm-negative cases with the ICD codes, we multiplied 1 minus the weighted NPV1 by the number of cases. Similarly, for algorithm-negative cases without the ICD codes, we multiplied 1 minus the weighted NPV2 by the number of cases. Finally, we derived the incident cases of stroke or AMI by adding these values (A, B, and C) calculated for each category. This approach allowed us to estimate the overall number of incident cases, taking into account the performance of the algorithm and the presence or absence of ICD codes in the data. NHIS, National Health Insurance Service; PPV, positive predictive value; NPV, negative predictive value.

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  Figure 6.. Study milestones. AMI, acute myocardial infarction; IRB, Institutional Review Board; NHIS, National Health Insurance Service; HIRA, Health Insurance Review and Assessment Service; ASQAP, Acute Stroke Quality Assessment Program. Initially planned study schedule Proceeded study schedule

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