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Yang J, Liu Q, Mo S, Wang K, Li M, Wu J, Jiang P, Yang S, Guo R, Yang Y, Zhang J, Liu Y, Cao Y, Wang S. The Effect of Preoperative Antiplatelet Therapy on Early Postoperative Rehemorrhage and Outcomes in Patients With Spontaneous Intracranial Hematoma. Front Aging Neurosci 2021; 13:681998. [PMID: 34276341 PMCID: PMC8283695 DOI: 10.3389/fnagi.2021.681998] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 05/31/2021] [Indexed: 11/13/2022] Open
Abstract
Background and Purpose The effect of antiplatelet therapy (APT) on early postoperative rehemorrhage and outcomes of patients with spontaneous intracerebral hemorrhage (ICH) is still unclear. This study is to evaluate the effect of preoperative APT on early postoperative rehemorrhage and outcomes in ICH patients. Methods This was a multicenter cohort study. ICH patients undergoing surgery were divided into APT group and no antiplatelet therapy (nAPT) group according to whether patients received APT or not. Chi-square test, t-test, and Mann–Whitney U test were used to compare the differences in variables, postoperative rehematoma, and outcomes between groups. Multivariate logistics regression analysis was used to correct for confounding variables, which were different in group comparison. Results One hundred fifty ICH patients undergoing surgical treatment were consecutively included in this study. Thirty five (23.33%) people were included in the APT group, while 115 (76.67%) people were included in the nAPT group. The incidence of early postoperative rehemorrhage in the APT group was significantly higher than that in the nAPT group (25.7% VS 10.4%, p = 0.047 < 0.05). After adjustment for age, ischemic stroke history, and ventricular hematoma, preoperative APT had no significant effect on early postoperative rehemorrhage (p = 0.067). There was no statistical difference between the two groups in early poorer outcomes (p = 0.222) at 14 days after surgery. After adjustment for age, ischemic stroke history, and ventricular hematoma, preoperative APT also had no significant effect on early poorer modified Rankin Scale (mRS) (p = 0.072). Conclusion In conclusion, preoperative APT appears to be safe and have no significant effect on early postoperative rehematoma and outcomes in ICH patients.
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Affiliation(s)
- Junhua Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Qingyuan Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Shaohua Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Kaiwen Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Maogui Li
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Jun Wu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Pengjun Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Shuzhe Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Rui Guo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Yi Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Jiaming Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Yang Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Yong Cao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
| | - Shuo Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Center of Stroke, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China
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Hall AN, Weaver B, Liotta E, Maas MB, Faigle R, Mroczek DK, Naidech AM. Identifying Modifiable Predictors of Patient Outcomes After Intracerebral Hemorrhage with Machine Learning. Neurocrit Care 2020; 34:73-84. [PMID: 32385834 DOI: 10.1007/s12028-020-00982-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND/OBJECTIVE Demonstrating a benefit of acute treatment to patients with intracerebral hemorrhage (ICH) requires identifying which patients have a potentially modifiable outcome, where treatment could favorably shift a patient's expected outcome. A decision rule for which patients have a modifiable outcome could improve the targeting of treatments. We sought to determine which patients with ICH have a modifiable outcome. METHODS Patients with ICH were prospectively identified at two institutions. Data on hematoma volumes, medication histories, and other variables of interest were collected. ICH outcomes were evaluated using the modified Rankin Scale (mRS), assessed at 14 days and 3 months after ICH, with "good outcome" defined as 0-3 (independence or better) and "poor outcome" defined as 4-6 (dependence or worse). Supervised machine learning models identified the best predictors of good versus poor outcomes at Institution 1. Models were validated using repeated fivefold cross-validation as well as testing on the entirely independent sample at Institution 2. Model fit was assessed with area under the ROC curve (AUC). RESULTS Model performance at Institution 1 was strong for both 14-day (AUC of 0.79 [0.77, 0.81] for decision tree, 0.85 [0.84, 0.87] for random forest) and 3 month (AUC of 0.75 [0.73, 0.77] for decision tree, 0.82 [0.80, 0.84] for random forest) outcomes. Independent predictors of functional outcome selected by the algorithms as important included hematoma volume at hospital admission, hematoma expansion, intraventricular hemorrhage, overall ICH Score, and Glasgow Coma Scale. Hematoma expansion was the only potentially modifiable independent predictor of outcome and was compatible with "good" or "poor" outcome in a subset of patients with low hematoma volumes, good Glasgow Coma scale and premorbid modified Rankin Scale scores. Models trained on harmonized data also predicted patient outcomes well at Institution 2 using decision tree (AUC 0.69 [0.63, 0.75]) and random forests (AUC 0.78 [0.72, 0.84]). CONCLUSIONS Patient outcomes are predictable to a high level in patients with ICH, and hematoma expansion is the sole-modifiable predictor of these outcomes across two outcome types and modeling approaches. According to decision tree analyses predicting outcome at 3 months, patients with a high Glasgow Coma Scale score, less than 44.5 mL hematoma volume at admission, and relatively low premorbid modified Rankin Score in particular have a modifiable outcome and appear to be candidates for future interventions to improve outcomes after ICH.
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Affiliation(s)
- Andrew N Hall
- Department of Psychology, Weinberg College of Arts and Sciences, Northwestern University, Evanston, IL, USA.
| | - Bradley Weaver
- Institute for Public Health and Medicine, Northwestern University Chicago, Chicago, IL, USA
| | - Eric Liotta
- Institute for Public Health and Medicine, Northwestern University Chicago, Chicago, IL, USA
| | - Matthew B Maas
- Institute for Public Health and Medicine, Northwestern University Chicago, Chicago, IL, USA
| | - Roland Faigle
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel K Mroczek
- Department of Psychology, Weinberg College of Arts and Sciences, Northwestern University, Evanston, IL, USA
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Andrew M Naidech
- Institute for Public Health and Medicine, Northwestern University Chicago, Chicago, IL, USA
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