{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:59:24Z","timestamp":1790657964639,"version":"4.1.0"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate early prediction of 3-month functional outcome after acute ischemic stroke is essential for prognostic communication, early management planning, and resource allocation. However, clinically applicable and interpretable prediction tools based on routinely collected data remain limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this single-center retrospective prediction-model study, we analyzed 770 adults with acute ischemic stroke admitted within 24\u202fh of symptom onset. Baseline clinical and early laboratory variables were collected. After an 80\/20 train\u2013test split, key predictors were selected using cross-validated least absolute shrinkage and selection operator (LASSO) in the training set. Several supervised learning models were developed and compared. Model discrimination, calibration, and clinical utility were evaluated. The final gradient boosting machine (GBM) model was interpreted using TreeSHAP, and an online Shiny-based calculator was developed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LASSO identified 12 predictors. Test-set discrimination was similar across models, with area under the receiver operating characteristic curve (AUC) values ranging from 0.854 to 0.867. The final GBM achieved AUCs of 0.910 in the training set and 0.872 in the test set, with corresponding areas under the precision\u2013recall curve of 0.814 and 0.741. Calibration was satisfactory in both sets. SHAP analyses identified NIHSS, age, and C-reactive protein as dominant contributors, showing predominantly non-linear effects with limited interaction strength.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An explainable model based on routinely collected clinical data showed good discrimination and calibration in internal validation for predicting 3-month functional outcome after AIS. Independent external validation is required before the model can be considered for clinical application.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2026.1852875","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:37:54Z","timestamp":1790656674000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Early prediction of 3-month functional outcome after acute ischemic stroke: an explainable model based on routine clinical data"],"prefix":"10.3389","volume":"17","author":[{"given":"Zichen","family":"Rao","sequence":"first","affiliation":[{"name":"Department of Endocrinology, Quzhou People\u2019s Hospital, The Quzhou Affiliated Hospital, Wenzhou Medical University","place":["Quzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyi","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Endocrinology and Metabolism, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanmei","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Quzhou People\u2019s Hospital, The Quzhou Affiliated Hospital, Wenzhou Medical University","place":["Quzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Quzhou People\u2019s Hospital, The Quzhou Affiliated Hospital, Wenzhou Medical University","place":["Quzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1654","DOI":"10.1097\/CCM.0000000000004597","article-title":"Management of acute ischemic stroke","volume":"48","author":"Herpich","year":"2020","journal-title":"Crit Care Med"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"16983","DOI":"10.1136\/bmj.l6983","article-title":"Management of acute ischemic stroke","volume":"368","author":"Phipps","year":"2020","journal-title":"BMJ"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"106018","DOI":"10.1016\/j.jstrokecerebrovasdis.2021.106018","article-title":"Machine-learning-derived model for the stratification of cardiovascular risk in patients with ischemic stroke","volume":"30","author":"Ntaios","year":"2021","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1487248","DOI":"10.3389\/fneur.2024.1487248","article-title":"Nomogram to predict 3 month prognosis of acute ischemic stroke among young adults","volume":"15","author":"He","year":"2025","journal-title":"Front Neurol"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"e000177","DOI":"10.1136\/bmjno-2021-000177","article-title":"Predicting 90-day modified Rankin scale score with discharge information in acute ischaemic stroke patients following treatment","volume":"3","author":"ElHabr","year":"2021","journal-title":"BMJ Neurol Open"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"3314","DOI":"10.1161\/STROKEAHA.119.025696","article-title":"Prediction tools for stroke rehabilitation","volume":"50","author":"Stinear","year":"2019","journal-title":"Stroke"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"e07","DOI":"10.15420\/aer.2022.34","article-title":"Machine learning and the conundrum of stroke risk prediction","volume":"12","author":"Chahine","year":"2023","journal-title":"Arrhythm Electrophysiol Rev"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1161\/strokeaha.118.024293","article-title":"Machine learning\u2013based model for prediction of outcomes in acute stroke","volume":"50","author":"Heo","year":"2019","journal-title":"Stroke"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"122997","DOI":"10.1016\/j.jns.2024.122997","article-title":"Stroke risk prediction models: a systematic review and meta-analysis","volume":"460","author":"Asowata","year":"2024","journal-title":"J Neurol Sci"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"106041","DOI":"10.1016\/j.ijmedinf.2025.106041","article-title":"Machine learning techniques for stroke prediction: a systematic review of algorithms, datasets, and regional gaps","volume":"203","author":"Soladoye","year":"2025","journal-title":"Int J Med Inform"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"63","DOI":"10.34172\/hpp.025.43635","article-title":"Machine learning predictive models for survival in patients with brain stroke","volume":"15","author":"Norouzi","year":"2025","journal-title":"Health Promot Perspect"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"4670","DOI":"10.3390\/s22134670","article-title":"Stroke risk prediction with machine learning techniques","volume":"22","author":"Dritsas","year":"2022","journal-title":"Sensors"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"e0234722","DOI":"10.1371\/journal.pone.0234722","article-title":"A systematic review of machine learning models for predicting outcomes of stroke with structured data","volume":"15","author":"Wang","year":"2020","journal-title":"PLoS One"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"9859","DOI":"10.3390\/s22249859","article-title":"Explainable artificial intelligence model for stroke prediction using EEG signal","volume":"22","author":"Islam","year":"2022","journal-title":"Sensors (Basel, Switzerland)"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"52288","DOI":"10.1109\/access.2023.3278273","article-title":"Automated stroke prediction using machine learning: an explainable and exploratory study with a web application for early intervention","volume":"11","author":"Mridha","year":"2023","journal-title":"IEEE Access"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"1286","DOI":"10.3390\/jcm10061286","article-title":"Prediction of long-term stroke recurrence using machine learning models","volume":"10","author":"Abedi","year":"2021","journal-title":"J Clin Med"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3892\/br.2023.1599","article-title":"CRP as a potential predictor of outcome in acute ischemic stroke","volume":"18","author":"Bian","year":"2023","journal-title":"Biomed Rep"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"e0323995","DOI":"10.1371\/journal.pone.0323995","article-title":"Three-month functional outcomes of acute ischemic stroke in patients with chronic renal function impairment","volume":"20","author":"Hemmati","year":"2025","journal-title":"PLoS One"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"821336","DOI":"10.3389\/fnagi.2021.821336","article-title":"Elevated glycated hemoglobin levels are associated with poor outcome in acute ischemic stroke","volume":"13","author":"Dong","year":"2022","journal-title":"Front Aging Neurosci"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/j.jstrokecerebrovasdis.2018.10.026","article-title":"Impact of elevated hemoglobin A1c levels on functional outcome in patients with acute ischemic stroke","volume":"28","author":"Wang","year":"2019","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"207","DOI":"10.3390\/jcdd11070207","article-title":"Machine learning approaches for stroke risk prediction: findings from the Suita study","volume":"11","author":"Vu","year":"2024","journal-title":"J Cardiovasc Dev Dis"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.3390\/s25061739","article-title":"A novel explainable attention-based meta-learning framework for imbalanced brain stroke prediction","volume":"25","author":"Abousaber","year":"2025","journal-title":"Sensors (Basel)"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1177\/15741699251351844","article-title":"SHAP-assisted machine learning model to predict stroke location in atrial fibrillation-related ischemia","volume":"20","author":"Lin","year":"2025","journal-title":"Model Assist Stat Appl"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"1234046","DOI":"10.3389\/fneur.2023.1234046","article-title":"Interpretable machine learning for prediction of clinical outcomes in acute ischemic stroke","volume":"14","author":"Lee","year":"2023","journal-title":"Front Neurol"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"e0303287","DOI":"10.1371\/journal.pone.0303287","article-title":"Developing machine learning models to predict multi-class functional outcomes and death three months after stroke in Sweden","volume":"19","author":"Otieno","year":"2024","journal-title":"PLoS One"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"1678815","DOI":"10.3389\/fneur.2025.1678815","article-title":"Development and validation of explainable machine learning models for predicting 3-month functional outcomes in acute ischemic stroke: a SHAP-based approach","volume":"16","author":"Chen","year":"2025","journal-title":"Front Neurol"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1212\/WNL.0b013e31824367ab","article-title":"Functional recovery after ischemic stroke\u2014a matter of age","volume":"78","author":"Knoflach","year":"2012","journal-title":"Neurology"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"2827","DOI":"10.1212\/WNL.0000000000204764","article-title":"The National Institute of health stroke scale: a teaching tool (P8-5.008)","volume":"102","author":"Jumah","year":"2024","journal-title":"Neurology"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"105494","DOI":"10.1016\/j.ebiom.2024.105494","article-title":"Accelerated biological aging increases the risk of short- and long-term stroke prognosis in patients with ischemic stroke or TIA","volume":"111","author":"Wang","year":"2025","journal-title":"EBioMedicine"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"3120","DOI":"10.1210\/en.2018-00465","article-title":"Age and sex are critical factors in ischemic stroke pathology","volume":"159","author":"Roy-O\u2019Reilly","year":"2018","journal-title":"Endocrinology"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/s00415-011-6006-0","article-title":"Serum levels of cytokines and C-reactive protein in acute ischemic stroke patients, and their relationship to stroke lateralization, type, and infarct volume","volume":"258","author":"Ormstad","year":"2011","journal-title":"J Neurol"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"e0156790","DOI":"10.1371\/journal.pone.0156790","article-title":"Plasma C-reactive protein and clinical outcomes after acute ischemic stroke: a prospective observational study","volume":"11","author":"Matsuo","year":"2016","journal-title":"PLoS One"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"e1738","DOI":"10.1212\/WNL.0000000000200153","article-title":"Etiology and outcome of ischemic stroke in patients with renal impairment including chronic kidney disease","volume":"98","author":"Miwa","year":"2022","journal-title":"Neurology"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1093\/ndt\/gfn471","article-title":"Renal dysfunction in acute stroke: an independent predictor of long-term all combined vascular events and overall mortality","volume":"24","author":"Tsagalis","year":"2009","journal-title":"Nephrol Dial Transplant"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1186\/s12916-021-02014-4","article-title":"The risk of ischemic stroke and hemorrhagic stroke in Chinese adults with low-density lipoprotein cholesterol concentrations <\u202f70\u202fmg\/dL","volume":"19","author":"Wu","year":"2021","journal-title":"BMC Med"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"e021855","DOI":"10.1161\/JAHA.121.021855","article-title":"Low-density lipoprotein cholesterol and risk of recurrent vascular events in Chinese patients with ischemic stroke with and without significant atherosclerosis","volume":"10","author":"Lau","year":"2021","journal-title":"J Am Heart Assoc"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"e0221911","DOI":"10.1371\/journal.pone.0221911","article-title":"Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis","volume":"14","author":"Suzuki","year":"2019","journal-title":"PLoS One"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","article-title":"A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models","volume":"110","author":"Christodoulou","year":"2019","journal-title":"J Clin Epidemiol"}],"container-title":["Frontiers in Neurology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2026.1852875\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:38:07Z","timestamp":1790656687000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2026.1852875\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,29]]},"references-count":38,"alternative-id":["10.3389\/fneur.2026.1852875"],"URL":"https:\/\/doi.org\/10.3389\/fneur.2026.1852875","relation":{},"ISSN":["1664-2295"],"issn-type":[{"value":"1664-2295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,29]]},"article-number":"1852875"}}