Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employing machine learning methods
Background: Acute ischemic stroke (AIS) is a leading cause of mortality and disability worldwide. Intravenous thrombolysis (IVT) improves recovery, but predicting outcomes remains challenging. Machine learning (ML) and biomarkers like ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1), S100 calcium-b...
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SAGE Publishing
2025-06-01
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| Series: | Therapeutic Advances in Neurological Disorders |
| Online Access: | https://doi.org/10.1177/17562864251342429 |
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| author | Ming-Ya Luo Yang Qu Peng Zhang Reziya Abuduxukuer Li-Juan Wang Li-Chong Yang Zhi-Guo Li Xiao-Dong Liu Ce Han Dan Li Wei-Jia Wang Dian-Ping Lv Ming Liu Jian Gao Jing Xu Yongfei Jiang Hai-Nan Chen Fu-Jin Li Li-Ming Sun Qi-Dong Sun Yingbin Qi Si-Yin Sun Yu Zhang Zhen-Ni Guo Yi Yang |
| author_facet | Ming-Ya Luo Yang Qu Peng Zhang Reziya Abuduxukuer Li-Juan Wang Li-Chong Yang Zhi-Guo Li Xiao-Dong Liu Ce Han Dan Li Wei-Jia Wang Dian-Ping Lv Ming Liu Jian Gao Jing Xu Yongfei Jiang Hai-Nan Chen Fu-Jin Li Li-Ming Sun Qi-Dong Sun Yingbin Qi Si-Yin Sun Yu Zhang Zhen-Ni Guo Yi Yang |
| author_sort | Ming-Ya Luo |
| collection | DOAJ |
| description | Background: Acute ischemic stroke (AIS) is a leading cause of mortality and disability worldwide. Intravenous thrombolysis (IVT) improves recovery, but predicting outcomes remains challenging. Machine learning (ML) and biomarkers like ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1), S100 calcium-binding protein β (S100β), and neuron-specific enolase (NSE) may enhance prognostic accuracy. Objectives: We aimed to assess the predictive value of serum brain injury biomarkers for 3-month outcomes in AIS patients treated with IVT, using an ML-based model. Design: A multicenter prospective cohort study was conducted, enrolling AIS patients treated with recombinant tissue plasminogen activator from 16 hospitals. Methods: Of 1580 patients, 1028 were included and divided into training ( n = 571), testing ( n = 243), and external validation ( n = 214) cohorts. Thirty-three variables, including demographics, clinical data, and biomarkers (UCH-L1, S100β, NSE), were analyzed. Least Absolute Shrinkage and Selection Operator regression was used for feature selection, and six ML algorithms were tested. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F 1-score, calibration curve, and decision curve analysis. Results: The light gradient boosting machines (LightGBM) model achieved the best performance in the training dataset (AUC: 0.846; F 1-score: 0.789) and external validation dataset (AUC: 0.714). Eight critical predictors, including age, admission National Institutes of Health Stroke Scale (NIHSS) score, Trial of Org 10172 in Acute Stroke Treatment, white blood cell, finger blood glucose, UCH-L1, S100β, and NSE, were identified and incorporated into an ML model for clinical application. Shapley additive interpretation analysis enhances the interpretability of the model, with NIHSS score and NSE as top contributors. External validation confirmed good calibration and consistent net benefit across threshold probabilities (0.1–0.8). Conclusion: Integrating serum biomarkers (UCH-L1, S100β, NSE) with ML significantly improves 3-month outcome prediction in AIS patients. The LightGBM model offers robust performance and clinical interpretability for individualized treatment planning. |
| format | Article |
| id | doaj-art-d25e49de2e694f7b95104d66ed900e1a |
| institution | OA Journals |
| issn | 1756-2864 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Therapeutic Advances in Neurological Disorders |
| spelling | doaj-art-d25e49de2e694f7b95104d66ed900e1a2025-08-20T02:09:22ZengSAGE PublishingTherapeutic Advances in Neurological Disorders1756-28642025-06-011810.1177/17562864251342429Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employing machine learning methodsMing-Ya LuoYang QuPeng ZhangReziya AbuduxukuerLi-Juan WangLi-Chong YangZhi-Guo LiXiao-Dong LiuCe HanDan LiWei-Jia WangDian-Ping LvMing LiuJian GaoJing XuYongfei JiangHai-Nan ChenFu-Jin LiLi-Ming SunQi-Dong SunYingbin QiSi-Yin SunYu ZhangZhen-Ni GuoYi YangBackground: Acute ischemic stroke (AIS) is a leading cause of mortality and disability worldwide. Intravenous thrombolysis (IVT) improves recovery, but predicting outcomes remains challenging. Machine learning (ML) and biomarkers like ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1), S100 calcium-binding protein β (S100β), and neuron-specific enolase (NSE) may enhance prognostic accuracy. Objectives: We aimed to assess the predictive value of serum brain injury biomarkers for 3-month outcomes in AIS patients treated with IVT, using an ML-based model. Design: A multicenter prospective cohort study was conducted, enrolling AIS patients treated with recombinant tissue plasminogen activator from 16 hospitals. Methods: Of 1580 patients, 1028 were included and divided into training ( n = 571), testing ( n = 243), and external validation ( n = 214) cohorts. Thirty-three variables, including demographics, clinical data, and biomarkers (UCH-L1, S100β, NSE), were analyzed. Least Absolute Shrinkage and Selection Operator regression was used for feature selection, and six ML algorithms were tested. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F 1-score, calibration curve, and decision curve analysis. Results: The light gradient boosting machines (LightGBM) model achieved the best performance in the training dataset (AUC: 0.846; F 1-score: 0.789) and external validation dataset (AUC: 0.714). Eight critical predictors, including age, admission National Institutes of Health Stroke Scale (NIHSS) score, Trial of Org 10172 in Acute Stroke Treatment, white blood cell, finger blood glucose, UCH-L1, S100β, and NSE, were identified and incorporated into an ML model for clinical application. Shapley additive interpretation analysis enhances the interpretability of the model, with NIHSS score and NSE as top contributors. External validation confirmed good calibration and consistent net benefit across threshold probabilities (0.1–0.8). Conclusion: Integrating serum biomarkers (UCH-L1, S100β, NSE) with ML significantly improves 3-month outcome prediction in AIS patients. The LightGBM model offers robust performance and clinical interpretability for individualized treatment planning.https://doi.org/10.1177/17562864251342429 |
| spellingShingle | Ming-Ya Luo Yang Qu Peng Zhang Reziya Abuduxukuer Li-Juan Wang Li-Chong Yang Zhi-Guo Li Xiao-Dong Liu Ce Han Dan Li Wei-Jia Wang Dian-Ping Lv Ming Liu Jian Gao Jing Xu Yongfei Jiang Hai-Nan Chen Fu-Jin Li Li-Ming Sun Qi-Dong Sun Yingbin Qi Si-Yin Sun Yu Zhang Zhen-Ni Guo Yi Yang Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employing machine learning methods Therapeutic Advances in Neurological Disorders |
| title | Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employing machine learning methods |
| title_full | Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employing machine learning methods |
| title_fullStr | Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employing machine learning methods |
| title_full_unstemmed | Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employing machine learning methods |
| title_short | Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employing machine learning methods |
| title_sort | prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum uch l1 s100β and nse a multicenter prospective cohort study employing machine learning methods |
| url | https://doi.org/10.1177/17562864251342429 |
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