Evidence map›Paper›PMID 42272212›Full record

ArticleAcute and critical care2026

Clinical variable-based decision-support model for rapid differentiation of hemorrhagic and ischemic stroke at emergency department presentation in South Korea.

Jae-Woo Kim, Jin-Heon Jeong, Moon-Ku Han, Sang-Hoon Han, Ka Hyun Kim, Seung Park, Dong-Ick Shin, Kyu Sun Yum

Abstract read
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Article in Acute and critical care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Jae-Woo KimChungbuk National University College of Medicine, Medical Artificial Intelligence Center, Chungbuk National University Hospital, Cheongju, Korea.
Jin-Heon JeongDepartment of Intensive Care Medicine and Neurology, Dong-A University Hospital, Dong-A University College of Medicine, Busan, Korea.
Moon-Ku HanDepartment of Neurology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
Sang-Hoon HanDivision of Intensive Care Medicine, Departments of Neurosurgery and Neurology, Seoul National University Bundang Hospital, Seongnam, Korea.
Ka Hyun KimChungbuk National University College of Medicine, Medical Artificial Intelligence Center, Chungbuk National University Hospital, Cheongju, Korea.
Seung ParkDepartment of Biomedical Engineering, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju, Korea.
Dong-Ick ShinDepartment of Neurology, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju, Korea.
Kyu Sun YumDepartment of Neurology, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju, Korea.

Funding

Chungbuk National UniversityMinistry of Science ICT 2710006818
6 · The paper itself

Abstract

backgroundPrompt differentiation between ischemic stroke (IS) and hemorrhagic stroke (HS) is critical because their treatment strategies fundamentally differ. While neuroimaging is essential, clinical decision-making often begins before imaging is completed, and conventional clinical scores have shown inconsistent performance. The objective of this study was therefore to develop and externally validate a machine-learning model that supports HS vs. IS subtype suspicion at emergency department (ED) presentation using only clinical variables.

methodsWe conducted a retrospective multicenter cohort study of 2,998 adult patients with a final diagnosis of acute IS or HS treated at three comprehensive stroke centers (July 2020-January 2024). Patients from hospitals A and B comprised the development/internal validation cohort (n=2,418), while patients from hospital C served as an independent external validation cohort (n=580). An extreme gradient boosting (XGBoost) algorithm was trained using four-fold cross-validation, and feature contributions were assessed using Shapley additive explanation (SHAP) values.

resultsInternal validation showed an area under the receiver operating characteristic curve (AUROC) of 0.937 (95% CI, 0.922-0.950) with a sensitivity 0.828, specificity of 0.932, and accuracy of 0.905. Independent external validation yielded an AUROC of 0.841 (95% CI, 0.792-0.883) with a sensitivity 0.758, specificity of 0.789, and accuracy of 0.783. SHAP analysis identified headache and higher National Institutes of Health Stroke Scale item 1a (level of consciousness) as factors increasing the model output toward HS, whereas atrial fibrillation shifted predictions toward IS.

conclusionsA clinical variable-only model can support early HS vs. IS subtype suspicion at ED presentation among patients managed in an acute-stroke pathway without requiring laboratory tests. Performance decreased on independent external validation, suggesting potential site-related differences and the need for prospective evaluation and calibration. Stroke mimics were not included and should be addressed in future studies.

Indexed as

classificationclinical decision support systemshemorrhagic strokeischemic strokemachine learning

Identifiers

PMID42272212
PMCPMC13268717

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.