Abstract Details

Title Development and Validation of an AI-based Algorithm to Identify Recurrent Stroke Using National Claims Data in Korea

Topic Cerebrovascular Disease and Interventional Neurology

Presentation(s) P6 - Poster Session 6 (5:00 PM-6:00 PM)

Poster/Presentation
Number 4-010

Objective This study aimed to develop and validate an artificial intelligence (AI)–based algorithm to identify recurrent stroke admissions using national claims data, enabling accurate epidemiologic statistics and supporting health policy planning.

Background Stroke is a major cause of death and disability worldwide. Recurrent stroke contributes substantially to the overall burden but is difficult to accurately identify using administrative claims data because current coding systems do not distinguish acute from chronic or non-acute events. Although validated algorithms for first-ever stroke exist, no nationwide claims-based algorithms are currently available to detect recurrent stroke in Korea.

Design/Methods The gold standard for recurrent stroke was defined using the Korean Acute Stroke Assessment Program (ASQAP), a nationwide government-led evaluation of acute stroke care. Initial stroke admissions were identified during the 4th–9th assessment periods (2011–2021), and patients who were re-registered in the 10th assessment (October 2022–March 2023) were classified as recurrent stroke. A rule-based algorithm was developed, followed by a Random Forest machine learning model. Hyperparameters were optimized using grid search to maximize sensitivity and PPV.

Results A total of 13,078 admission cases during the 10th assessment period (male, 54.2%; mean age, 65.7 ± 15.5 years) were analyzed, of which 485 cases were confirmed recurrent stroke. Thirty key identifiers were selected from claims data, and 58 conditions were generated through their combinations. These identifiers included brain CT, MRI, rt-PA, endovascular treatment, carotid endarterectomy or stenting, antithrombotics, and anticoagulants, etc. The rule-based algorithm achieved sensitivity 72.0%, specificity 98.5%, accuracy 97.5%, and PPV 64.7%. The AI-based model improved performance (sensitivity 82.3%, specificity 98.9%, accuracy 98.3%, PPV 74.7%).

Conclusions An AI-based algorithm accurately identified recurrent stroke admissions from national claims data. This approach may enable robust nationwide recurrent stroke statistics and inform resource allocation and health policy decisions.

Authors/Disclosures
Jun Yup Kim, MD PRESENTER	Prof. Kim has nothing to disclose.
Kyoung-Yun Kim, PhD	Mr. Kim has nothing to disclose.
Jiyoon Lee	Ms. Lee has nothing to disclose.
Seong-Eun Kim, PhD	Mrs. Kim has nothing to disclose.
Wi-Sun Ryu	Wi-Sun Ryu has received personal compensation for serving as an employee of JLK Inc..
Jihoon Kang, MD, PhD	Prof. Kang has nothing to disclose.
Yoonho Kim, PhD	Dr. Kim has received personal compensation for serving as an employee of Magnendo Corp..
Hee-Joon Bae, MD, PhD	Prof. Bae has received personal compensation in the range of $0-$499 for serving on a Speakers Bureau for Daichi Sankyo. The institution of Prof. Bae has received personal compensation in the range of $0-$499 for serving on a Speakers Bureau for Daewoong Pharmaceutical Co., Ltd. The institution of Prof. Bae has received personal compensation in the range of $0-$499 for serving on a Speakers Bureau for Esai Korea, Inc.. Prof. Bae has received personal compensation in the range of $0-$499 for serving on a Speakers Bureau for Otsuka Korea. Prof. Bae has received personal compensation in the range of $0-$499 for serving on a Speakers Bureau for Amgen Korea. Prof. Bae has stock in JLK, Inc. The institution of Prof. Bae has received research support from Amgen Korea Limited.. The institution of Prof. Bae has received research support from Celltrion Inc.. The institution of Prof. Bae has received research support from Dong-A ST. The institution of Prof. Bae has received research support from SAMJIN Pharm. The institution of Prof. Bae has received research support from Otsuka Korea.

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