2022
DOI: 10.3389/fneur.2022.774654
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A New Nomogram for Predicting the Risk of Intracranial Hemorrhage in Acute Ischemic Stroke Patients After Intravenous Thrombolysis

Abstract: BackgroundWe aimed to develop and validate a new nomogram for predicting the risk of intracranial hemorrhage (ICH) in patients with acute ischemic stroke (AIS) after intravenous thrombolysis (IVT).MethodsA retrospective study enrolled 553 patients with AIS treated with IVT. The patients were randomly divided into two cohorts: the training set (70%, n = 387) and the testing set (30%, n = 166). The factors in the predictive nomogram were filtered using multivariable logistic regression analysis. The performance … Show more

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Cited by 12 publications
(2 citation statements)
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“…Manuel et al reported that SICH nomogram is able to predict the symptomatic intracerebral hemorrhage after intravenous thrombolysis for stroke with a ROC at 0.739 ( Cappellari et al, 2018 ). Weng et al also reported that AUC-ROC values of the nomogram regarding the risk of intracranial hemorrhage is 0.887 and 0.776 in the training and testing sets ( Weng et al, 2022 ). Zhou et al obtained the similar results with AUC-ROC values of the nomogram are 0.828 and 0.801, respectively ( Zhou et al, 2020 ).…”
Section: Discussionmentioning
confidence: 99%
“…Manuel et al reported that SICH nomogram is able to predict the symptomatic intracerebral hemorrhage after intravenous thrombolysis for stroke with a ROC at 0.739 ( Cappellari et al, 2018 ). Weng et al also reported that AUC-ROC values of the nomogram regarding the risk of intracranial hemorrhage is 0.887 and 0.776 in the training and testing sets ( Weng et al, 2022 ). Zhou et al obtained the similar results with AUC-ROC values of the nomogram are 0.828 and 0.801, respectively ( Zhou et al, 2020 ).…”
Section: Discussionmentioning
confidence: 99%
“…The “glmnet” package was used to screen factors that may affect the risk of death during hospitalization by the least absolute shrinkage and select operator (LASSO) method; the lambda (λ) with the smallest standard error was selected. The optimal LASSO regression model was constructed, and then the factors with non-zero coefficients selected by the LASSO regression model were included for further analysis ( 23 ). LASSO regression models avoid the problems of overfitting and multicollinearity caused by ordinary least squares estimation when there are too many predictors.…”
Section: Methodsmentioning
confidence: 99%