Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning
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 p...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2025.1506771/full |
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| author | Xiaoqing Liu Miaoran Wang Rui Wen Haoyue Zhu Ying Xiao Qian He Yangdi Shi Zhe Hong Bing Xu |
| author_facet | Xiaoqing Liu Miaoran Wang Rui Wen Haoyue Zhu Ying Xiao Qian He Yangdi Shi Zhe Hong Bing Xu |
| author_sort | Xiaoqing Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ce52969f9a534d23b3d345c856695d4a |
| institution | Kabale University |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-ce52969f9a534d23b3d345c856695d4a2025-01-27T09:32:33ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-01-011610.3389/fphar.2025.15067711506771Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learningXiaoqing Liu0Miaoran Wang1Rui Wen2Haoyue Zhu3Ying Xiao4Qian He5Yangdi Shi6Zhe Hong7Bing Xu8Shenyang Tenth People’s Hospital, Shenyang Medical College, Shenyang, ChinaThe First Hospital of China Medical University, Shenyang, ChinaShenyang Tenth People’s Hospital, Shenyang Medical College, Shenyang, ChinaShenyang Tenth People’s Hospital, Shenyang Medical College, Shenyang, ChinaShenyang First People’s Hospital, Shenyang Medical College, Shenyang, ChinaShenyang Tenth People’s Hospital, Shenyang Medical College, Shenyang, ChinaShenyang Tenth People’s Hospital, Shenyang Medical College, Shenyang, ChinaShenyang First People’s Hospital, Shenyang Medical College, Shenyang, ChinaShenyang Tenth People’s Hospital, Shenyang Medical College, Shenyang, ChinaThis 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.https://www.frontiersin.org/articles/10.3389/fphar.2025.1506771/fullacute ischemic stroke (AIS)diabetesthrombolyticXGBSHAP90-day MRS |
| spellingShingle | Xiaoqing Liu Miaoran Wang Rui Wen Haoyue Zhu Ying Xiao Qian He Yangdi Shi Zhe Hong Bing Xu Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning Frontiers in Pharmacology acute ischemic stroke (AIS) diabetes thrombolytic XGB SHAP 90-day MRS |
| title | Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning |
| title_full | Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning |
| title_fullStr | Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning |
| title_full_unstemmed | Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning |
| title_short | Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning |
| title_sort | following intravenous thrombolysis the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning |
| topic | acute ischemic stroke (AIS) diabetes thrombolytic XGB SHAP 90-day MRS |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2025.1506771/full |
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