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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Neuropharmacology
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1506771

Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning

Provisionally accepted
Xiaoqing Liu Xiaoqing Liu 1Miaoran Wang Miaoran Wang 2*Rui Wen Rui Wen 1*Haoyue Zhu Haoyue Zhu 1*Ying Xiao Ying Xiao 3*Qian He Qian He 1*Yangdi Shi Yangdi Shi 1*Zhe Hong Zhe Hong 3*Bing Xu Bing Xu 1*
  • 1 Shenyang Tenth People's Hospital, Shenyang, China
  • 2 Shenyang Medical College, Shenyang, Liaoning Province, China
  • 3 Shenyang First People’s Hospital, Shenyang Brain Institute, Shenyang, Liaoning Province, China

The final, formatted version of the article will be published soon.

    This 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.

    Keywords: Acute ischemic stroke (AIS), diabetes, Thrombolytic, Xgb, Shap, 90-day MRS

    Received: 06 Oct 2024; Accepted: 02 Jan 2025.

    Copyright: © 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) or licensor 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.

    * Correspondence:
    Miaoran Wang, Shenyang Medical College, Shenyang, 110034, Liaoning Province, China
    Rui Wen, Shenyang Tenth People's Hospital, Shenyang, China
    Haoyue Zhu, Shenyang Tenth People's Hospital, Shenyang, China
    Ying Xiao, Shenyang First People’s Hospital, Shenyang Brain Institute, Shenyang, 110041, Liaoning Province, China
    Qian He, Shenyang Tenth People's Hospital, Shenyang, China
    Yangdi Shi, Shenyang Tenth People's Hospital, Shenyang, China
    Zhe Hong, Shenyang First People’s Hospital, Shenyang Brain Institute, Shenyang, 110041, Liaoning Province, China
    Bing Xu, Shenyang Tenth People's Hospital, Shenyang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.