[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123204-en":3,"doc-seo-123204-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123204,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Following intravenous thrombolysis - the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning","Following intravenous thrombolysis, this cohort study evaluates prognostic outcomes in patients with acute ischemic stroke (AIS) who have diabetes mellitus, using machine learning to support post-treatment prediction. Data were drawn from Shenyang First People’s Hospital, covering 3,478 eligible AIS patients with diabetes treated between January 2018 and December 2023, with 1,314 included after screening. The primary endpoint was the 90-day modified Rankin Scale (MRS), with an 80/20 train-test design and multiple classifiers, including XGBoost. XGBoost achieved the highest average accuracy (0.7355 ± 0.0307).","TYPE Original Research PUBLISHED 27 January 2025 DOI 10.3389/fphar.2025.1506771  \nOPEN ACCESS  \nEDITED BY  \nZongchao Han,  \nUniversity of North Carolina at Chapel Hill, United States  \nREVIEWED BY  \nSushma Jaiswal,  \nGuru Ghasidas Vishwavidyalaya, India Vadthe Narasimha,  \nCMR College Of Engineering & Technology, India  \nMadhusmita Rout,  \nOklahoma State University Oklahoma City, United States  \n*CORRESPONDENCE  \nBing Xu,  \n [xb1968131@163.com](xb1968131@163.com)  \nRECEIVED 06 October 2024  \nACCEPTED 02 January 2025  \nPUBLISHED 27 January 2025  \nCITATION  \nLiu X, Wang M, Wen R, Zhu H, Xiao Y, He Q, Shi Y, Hong Z and Xu B (2025) Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning.  \nFront. Pharmacol. 16:1506771 .  \ndoi: 10.3389/fphar.2025.1506771  \nCOPYRIGHT  \n© 2025 Liu, Wang, Wen, Zhu, Xiao, He, Shi, Hong and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nFollowing intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning  \nXiaoqing Liu 1, Miaoran Wang 2, Rui Wen 1, Haoyue Zhu 1,  \nYing Xiao 3, Qian He 1, Yangdi Shi 1, Zhe Hong 3 and Bing Xu 1*  \n1Shenyang Tenth People’s Hospital, Shenyang Medical College, Shenyang, China, 2The First Hospital of China Medical University, Shenyang, China, 3Shenyang First People ’s Hospital, Shenyang Medical College, Shenyang, China  \nThis cohort study aimed to evaluate the prognostic outcomes of patients with acute ischemic stroke (AIS) and diabetes mellitus following intravenous thrombolysis, utilizing machine learning techniques . The analysis was conducted using data from Shenyang First People ’ s Hospital, involving 3,478 AIS patients with diabetes who received thrombolytic therapy from January 2018 to December 2023, ultimately focusing on 1,314 patients after screening. The primary outcome measured was the 90-day Modified Rankin Scale (MRS) . An 80/20 train-test split was implemented for model development and validation, employing various machine learning classifiers, including artificial neural networks (ANN), random forest (RF), XGBoost (XGB), and LASSO regression . Results indicated that the average accuracy of the XGB model was 0.7355 (±0.0307), outperforming the other models. Key predictors for prognosis post-thrombolysis included the National Institutes of Health Stroke Scale (NIHSS) and blood platelet count. The findings underscore the effectiveness of machine learning algorithms, particularly XGB, in predicting functional outcomes in diabetic AIS patients, providing clinicians with a valuable tool for treatment planning and improving patient outcome predictions based on receiver operating characteristic (ROC) analysis and accuracy assessments.  \nKEYWORDS  \nacute ischemic stroke (AIS), diabetes, thrombolytic, XGB, SHAP, 90-day MRS  \nAbbreviations: AIS, Acute ischemic stroke; MRS, Modified Rankin scale; NIHSS, National Institutes of Health Stroke Scale; TOAST, Acute Stroke Treatment Classification in the ORG 10172 trial; LAA, Large artery atherosclerosis; SVO, small vessel occlusion; CE, cardioembolism; OD, Other determined; UD, Undetermined; FBG, fasting blood glucose; CHOL, Cholesterol; ALB, Albumin; GLOB, globulin; AG, Albumin-globulin ratio; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; SHR, Stress hyperglycemia; SHAP, SHapley Additive exPlanations; XGB, extreme gradient boosting; RF, random forest; ANN, artificial neural network; LASSO, LASSO regular logistic regression.  \nFrontiers in Pharmacology 01 [fr","cbCaic6PrFHHa3Ae","https://ap.wps.com/l/cbCaic6PrFHHa3Ae","pdf",1209801,1,9,"English","en",105,"# Introduction\n## Background on stroke and acute ischemic stroke\n## Diabetes mellitus as a stroke risk factor\n## Rationale for machine learning prediction","[{\"question\":\"What patient group and treatment does the study focus on?\",\"answer\":\"The study focuses on patients with acute ischemic stroke who have diabetes mellitus and received intravenous thrombolysis (rt-PA/alteplase).\"},{\"question\":\"How is prognosis measured and what is the main endpoint?\",\"answer\":\"Prognosis is measured using the 90-day modified Rankin Scale (MRS).\"},{\"question\":\"Which machine learning model performed best and which predictors were important?\",\"answer\":\"XGBoost achieved the highest average accuracy among the tested models. Key predictors included NIHSS and blood platelet count.\"}]","Following intravenous thrombolysis - the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning | PDF",1785815205,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"following-intravenous-thrombolysis-the-outcome-of-diabetes-mellitus-associated-with-acute-ischemic-stroke-was-predicted-via-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/following-intravenous-thrombolysis-the-outcome-of-diabetes-mellitus-associated-with-acute-ischemic-stroke-was-predicted-via-machine-learning/123204/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What patient group and treatment does the study focus on?","Question",{"text":76,"@type":77},"The study focuses on patients with acute ischemic stroke who have diabetes mellitus and received intravenous thrombolysis (rt-PA/alteplase).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is prognosis measured and what is the main endpoint?",{"text":81,"@type":77},"Prognosis is measured using the 90-day modified Rankin Scale (MRS).",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning model performed best and which predictors were important?",{"text":85,"@type":77},"XGBoost achieved the highest average accuracy among the tested models. 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