Abstract
Accurately predicting the prognosis of patients with acute ischemic stroke at discharge remains highly challenging after active treatment. The aim of this retrospective nationwide registry-based study was to identify key predictors associated with favorable outcomes and to develop machine learning models for patient outcome prediction. Analysis of a comprehensive dataset of 40,586 patients revealed younger age (odds ratio [OR]: 0.975; 95% confidence interval [CI]: 0.972–0.977; p < 0.001), lower initial National Institutes of Health Stroke Scale score (OR: 0.862; 95% CI: 0.855–0.868; p < 0.001), mechanical thrombectomy (OR: 2.617; 95% CI: 2.185–3.134; p < 0.001), and rehabilitation therapy (OR: 2.765; 95% CI: 2.530–3.022; p < 0.001) as significant predictors of good functional outcome. We developed three machine learning models—random forest (RF), support vector machine (SVM), and logistic regression—to predict favorable functional outcomes (modified Rankin Scale score, ≤ 2) at discharge. Among these, the RF model revealed superior predictive performance, achieving an area under the curve (AUC) of 0.87, compared to the SVM and logistic regression, each achieving an AUC of 0.80. This study underscores the transformative potential of machine learning in stroke management, predicting and improving patient outcomes and streamlining healthcare delivery.
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Data availability
Data that support the findings of this study are available from the Korean Stroke Registry (M20230323002) but are not publicly available due to licensing restrictions. Access to the data is restricted and was granted specifically for the study. Data may be made available from the corresponding author upon reasonable request and with permission of the Korean Stroke Registry.
Code availability
The Stroke Registry dataset was only available in a closed analytical environment operated by the government, and while research outputs necessary for manuscript preparation could be exported after analysis, the written code for data processing could not be exported due to potential exposure of metadata information. Code for training and validating ML algorithms is available. We can supply the machine learning code in a supplementary material. If there are any questions about the code, please contact the corresponding author for answers. Analysis was conducted using R 4.1.2 in the closed analytical environment.
References
Nayak, N., Mahendran, N., Kuys, S. & Brauer, S. G. What factors at discharge predict physical activity and walking outcomes 6 months after stroke? A systematic review. Clin. Rehabil. 38, 1393–1403 (2024).
Michel, P. et al. The acute stroke registry and analysis of Lausanne (ASTRAL): design and baseline analysis of an ischemic stroke registry including acute multimodal imaging. Stroke 41, 2491–2498 (2010).
Kim, J., Park, J. E., Nahrendorf, M. & Kim, D. E. Direct thrombus imaging in stroke. J. Stroke. 18, 286–296 (2016).
Cai, W. et al. Association between triglyceride-glucose index and all-cause mortality in critically ill patients with ischemic stroke: analysis of the MIMIC-IV database. Cardiovasc. Diabetol. 22, 138. https://doi.org/10.1186/s12933-023-01864-x (2023).
Kazi, S. A., Siddiqui, M. & Majid, S. Stroke outcome prediction using admission Nihss in anterior and posterior circulation stroke. J. Ayub Med. Coll. Abbottabad. 33, 274–278 (2021).
Lee, J. Y. et al. Short and long-term outcomes of subarachnoid hemorrhage treatment according to hospital volume in korea: a nationwide multicenter registry. J. Korean Med. Sci. 36, e146. https://doi.org/10.3346/jkms.2021.36.e146 (2021).
Park, S. W. et al. High-volume hospital had lower mortality of severe intracerebral hemorrhage patients. J. Korean Neurosurg. Soc. 67, 622–636 (2024).
Zhao, X., Liu, T. & Wang, Z. CNN-based predictive modeling for ischemic stroke outcomes: a multimodal approach. Artif. Intell. Med. 133, 102404 (2023).
Zhang, Y., Wang, H. & Chen, X. Deep learning for predicting 90-day outcomes in ischemic stroke: a multicenter study. Stroke Res. Treat. 2022, 9856723 (2022).
Liu, Z. et al. Predicting functional outcome in acute ischemic stroke patients after endovascular treatment by machine learning. Transl Neurosci. 14, 20220324. https://doi.org/10.1515/tnsci-2022-0324 (2023).
Kwon, H. S., Lee, J. W. & Kim, Y. H. NIHSS score as a predictor of stroke outcomes: a meta-analysis. Stroke 52, 821–829 (2021).
Yeh, S. H., Cheng, H. L. & Lin, C. C. The impact of age on recovery after ischemic stroke: a retrospective cohort study. J. Neurol. 269, 3400–3412 (2022).
Saver, J. L., Smith, E. E. & Fonarow, G. C. The importance of rapid imaging in acute ischemic stroke care. Circulation 140, 1477–1490 (2019).
Khatri, P., Yeatts, S. D. & Mazighi, M. Onset-to-door time and outcomes in ischemic stroke. Lancet Neurol. 19, 318–325 (2020).
de Jong, G., van der Worp, H. B. & van Gijn, J. Charlson comorbidity index and its relation to stroke outcomes: a systematic review. Cerebrovasc. Dis. 45, 11–19 (2018).
Hacke, W. et al. Thrombolysis with Alteplase 3 to 4.5 hours after acute ischemic stroke. N Engl. J. Med. 359, 1317–1329 (2008).
Heo, J. et al. Machine learning-based model for prediction of outcomes in acute stroke. Stroke 50 (5), 1263–1265. (2019).
Ovbiagele, B. & Saver, J. L. The smoking-thrombolysis paradox and acute ischemic stroke. Neurology 65 (2), 293–295 (2005).
Acknowledgements
Thanks for JY Lee , SW Park.
Funding
This research was supported by the Patient-Centered Clinical Research Coordinating Center (PACEN) funded by the Ministry of Health & Welfare, Republic of Korea (RS-2022-KH131668 (HC22C0043)), by the Korean Neuroendovascular Society, the Minister of Health & Welfare of Republic of Korea (RS-2024-00439915) and Uijeongbu St. Mary’s Hospital of the Catholic University of KoreaThe funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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Taehoon Ko & Kanghyuk Lee & Jae Sang Oh were analyzed the data and participitated on AI modeling .Youg Uk Kwon, Yu Ra Lee & So Young Han were supported the data under the national project. Jae Sang Oh was designed the study and wrote the document.All authors reviewed the manuscript.
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Ko, T., Lee, K., Kwon, Y.U. et al. Machine learning for predicting functional outcomes in acute ischemic stroke: insights from a nationwide stroke registry. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37159-w
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DOI: https://doi.org/10.1038/s41598-026-37159-w
