Development and validation of a penumbra-based predictive model for thrombolysis outcome in acute ischemic stroke patientsResearch in context
The use of thrombolysis in acute ischemic stroke is restricted to a small proportion of patients because of the rigid 4·5-h window. With advanced imaging-based patient selection strategy, rescuing penumbra is critical to improving clinical outcomes. In this study, we included 155 acute ischemic stro...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2018-09-01
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| Series: | EBioMedicine |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396418302718 |
| _version_ | 1828887709605691392 |
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| author | Tian-Yu Tang Yun Jiao Ying Cui Chu-Hui Zeng Deng-Ling Zhao Yi Zhang Cheng-Yu Peng Xin-Dao Yin Pei-Yi Gao Yun-Jun Yang Sheng-Hong Ju Gao-Jun Teng |
| author_facet | Tian-Yu Tang Yun Jiao Ying Cui Chu-Hui Zeng Deng-Ling Zhao Yi Zhang Cheng-Yu Peng Xin-Dao Yin Pei-Yi Gao Yun-Jun Yang Sheng-Hong Ju Gao-Jun Teng |
| author_sort | Tian-Yu Tang |
| collection | DOAJ |
| description | The use of thrombolysis in acute ischemic stroke is restricted to a small proportion of patients because of the rigid 4·5-h window. With advanced imaging-based patient selection strategy, rescuing penumbra is critical to improving clinical outcomes. In this study, we included 155 acute ischemic stroke patients (84 patients in training dataset, age from 43 to 80, 59 males; 71 patients in validation dataset, age from 36 to 80, 45 males) who underwent MR scan within the first 9-h after onset, from 7 independent centers. Based on the mismatch concept, penumbra and core area were identified and quantitatively analyzed. Moreover, predictive models were developed and validated to provide an approach for identifying patients who may benefit from thrombolytic therapy. Predictive models were constructed, and corresponding areas under the curve (AUC) were calculated to explore their performances in predicting clinical outcomes. Additionally, the models were validated using an independent dataset both on Day-7 and Day-90. Significant correlations were detected between the mismatch ratio and clinical assessments in both the training and validation datasets. Treatment option, baseline systolic blood pressure, National Institutes of Health Stroke Scale score, mismatch ratio, and three regional radiological parameters were selected as biomarkers in the combined model to predict clinical outcomes of acute ischemic stroke patients. With the external validation, this predictive model reached AUCs of 0·863 as short-term validation and 0·778 as long-term validation. This model has the potential to provide quantitative biomarkers that aid patient selection for thrombolysis either within or beyond the current time window. Keywords: Penumbra, Predictive model, Clinical outcome, Acute ischemic stroke, Thrombolysis |
| first_indexed | 2024-12-13T12:09:29Z |
| format | Article |
| id | doaj.art-5b499e5eed934b31931741443bfdb564 |
| institution | Directory Open Access Journal |
| issn | 2352-3964 |
| language | English |
| last_indexed | 2024-12-13T12:09:29Z |
| publishDate | 2018-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj.art-5b499e5eed934b31931741443bfdb5642022-12-21T23:46:53ZengElsevierEBioMedicine2352-39642018-09-0135251259Development and validation of a penumbra-based predictive model for thrombolysis outcome in acute ischemic stroke patientsResearch in contextTian-Yu Tang0Yun Jiao1Ying Cui2Chu-Hui Zeng3Deng-Ling Zhao4Yi Zhang5Cheng-Yu Peng6Xin-Dao Yin7Pei-Yi Gao8Yun-Jun Yang9Sheng-Hong Ju10Gao-Jun Teng11Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, Jiangsu 210009, ChinaJiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, Jiangsu 210009, ChinaJiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, Jiangsu 210009, ChinaJiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, Jiangsu 210009, ChinaJiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, Jiangsu 210009, ChinaJiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, Jiangsu 210009, ChinaJiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, Jiangsu 210009, ChinaDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing, Jiangsu 210006, ChinaDepartment of Radiology, Beijing Tiantan Hospital, Capital Medical University, 6 Tiantanxili, Beijing 100050, ChinaDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, 2 Fuxuexiang, Wenzhou, Zhejiang 325000, ChinaJiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, Jiangsu 210009, China; Corresponding authors at: Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing 210009, China.Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, Jiangsu 210009, China; Corresponding authors at: Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, 87 Dingjiaqiao Road, Nanjing 210009, China.The use of thrombolysis in acute ischemic stroke is restricted to a small proportion of patients because of the rigid 4·5-h window. With advanced imaging-based patient selection strategy, rescuing penumbra is critical to improving clinical outcomes. In this study, we included 155 acute ischemic stroke patients (84 patients in training dataset, age from 43 to 80, 59 males; 71 patients in validation dataset, age from 36 to 80, 45 males) who underwent MR scan within the first 9-h after onset, from 7 independent centers. Based on the mismatch concept, penumbra and core area were identified and quantitatively analyzed. Moreover, predictive models were developed and validated to provide an approach for identifying patients who may benefit from thrombolytic therapy. Predictive models were constructed, and corresponding areas under the curve (AUC) were calculated to explore their performances in predicting clinical outcomes. Additionally, the models were validated using an independent dataset both on Day-7 and Day-90. Significant correlations were detected between the mismatch ratio and clinical assessments in both the training and validation datasets. Treatment option, baseline systolic blood pressure, National Institutes of Health Stroke Scale score, mismatch ratio, and three regional radiological parameters were selected as biomarkers in the combined model to predict clinical outcomes of acute ischemic stroke patients. With the external validation, this predictive model reached AUCs of 0·863 as short-term validation and 0·778 as long-term validation. This model has the potential to provide quantitative biomarkers that aid patient selection for thrombolysis either within or beyond the current time window. Keywords: Penumbra, Predictive model, Clinical outcome, Acute ischemic stroke, Thrombolysishttp://www.sciencedirect.com/science/article/pii/S2352396418302718 |
| spellingShingle | Tian-Yu Tang Yun Jiao Ying Cui Chu-Hui Zeng Deng-Ling Zhao Yi Zhang Cheng-Yu Peng Xin-Dao Yin Pei-Yi Gao Yun-Jun Yang Sheng-Hong Ju Gao-Jun Teng Development and validation of a penumbra-based predictive model for thrombolysis outcome in acute ischemic stroke patientsResearch in context EBioMedicine |
| title | Development and validation of a penumbra-based predictive model for thrombolysis outcome in acute ischemic stroke patientsResearch in context |
| title_full | Development and validation of a penumbra-based predictive model for thrombolysis outcome in acute ischemic stroke patientsResearch in context |
| title_fullStr | Development and validation of a penumbra-based predictive model for thrombolysis outcome in acute ischemic stroke patientsResearch in context |
| title_full_unstemmed | Development and validation of a penumbra-based predictive model for thrombolysis outcome in acute ischemic stroke patientsResearch in context |
| title_short | Development and validation of a penumbra-based predictive model for thrombolysis outcome in acute ischemic stroke patientsResearch in context |
| title_sort | development and validation of a penumbra based predictive model for thrombolysis outcome in acute ischemic stroke patientsresearch in context |
| url | http://www.sciencedirect.com/science/article/pii/S2352396418302718 |
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