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Predictive Modeling of Short-Term Poor Prognosis of Successful Reperfusion after Endovascular Treatment in Patients with Anterior Circulation Acute Ischemic Stroke
This study aimed to propose and internally validate a prediction model of short-term poor prognosis in patients with acute ischemic stroke (AIS). In the retrospective study, 356 eligible AIS patients receiving endovascular treatment (EVT) were included and divided into the good prognosis group and t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9391146/ https://www.ncbi.nlm.nih.gov/pubmed/35991294 http://dx.doi.org/10.1155/2022/3185211 |
Sumario: | This study aimed to propose and internally validate a prediction model of short-term poor prognosis in patients with acute ischemic stroke (AIS). In the retrospective study, 356 eligible AIS patients receiving endovascular treatment (EVT) were included and divided into the good prognosis group and the poor prognosis group. Data from 70% of patients were collected as training set and the 30% as validation set. Univariate analysis and multivariate logistic regression were used for identifying independent predictors. The performance of the model was evaluated by receiver operating characteristic (ROC) curve and the paired Chi-square test was used for internal validation. A model for the prediction of short-term poor prognosis in atherosclerotic AIS patients who successfully underwent endovascular reperfusion was developed: log (Pr/1 − Pr) = 3.500 + Blood glucose ∗ 0.174 + Infarct volume ∗ 0.128 + the National Institutes of Health Stroke Scale score × Onset-to-reperfusion time (NIHSS-ORT) ∗ 0.014 + Intraoperative hypotension (Yes) ∗ 1.037 + Mean arterial pressure (MAP) decrease from baseline (>40%) ∗ 2.061 (Pr represented the probability of short-term poor prognosis). The area under the curve (AUC) was 0.806 (0.748 − 0.864) in the training set and 0.850 (0.779 − 0.920) in the testing set, which suggested the good performance of the model. We proposed and validated a combined prediction model to predict the short-term poor prognosis of AIS patients after EVT, which could provide reference for clinicians to identify AIS patients with a higher risk of poor outcomes and thus improving the prognosis of EVT. |
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