Background and purposePatients with ischemic stroke frequently develop hemorrhagic transformation (HT), which could potentially worsen the prognosis. The objectives of the current study were to determine the incidence and predictors of HT, to evaluate predictor interaction, and to identify the optimal predicting models.MethodsA prospective study included 360 patients with ischemic stroke, of whom 354 successfully continued the study. Patients were subjected to thorough general and neurological examination and T2 diffusion-weighted MRI, at admission and 1 week later to determine the incidence of HT. HT predictors were selected by a filter-based minimum redundancy maximum relevance (mRMR) algorithm independent of model performance. Several machine learning algorithms including multivariable logistic regression classifier (LRC), support vector classifier (SVC), random forest classifier (RFC), gradient boosting classifier (GBC), and multilayer perceptron classifier (MLPC) were optimized for HT prediction in a randomly selected half of the sample (training set) and tested in the other half of the sample (testing set). The model predictive performance was evaluated using receiver operator characteristic (ROC) and visualized by observing case distribution relative to the models' predicted three-dimensional (3D) hypothesis spaces within the testing dataset true feature space. The interaction between predictors was investigated using generalized additive modeling (GAM).ResultsThe incidence of HT in patients with ischemic stroke was 19.8%. Infarction size, cerebral microbleeds (CMB), and the National Institute of Health stroke scale (NIHSS) were identified as the best HT predictors. RFC (AUC: 0.91, 95% CI: 0.85–0.95) and GBC (AUC: 0.91, 95% CI: 0.86–0.95) demonstrated significantly superior performance compared to LRC (AUC: 0.85, 95% CI: 0.79–0.91) and MLPC (AUC: 0.85, 95% CI: 0.78–0.92). SVC (AUC: 0.90, 95% CI: 0.85–0.94) outperformed LRC and MLPC but did not reach statistical significance. LRC and MLPC did not show significant differences. The best models' 3D hypothesis spaces demonstrated non-linear decision boundaries suggesting an interaction between predictor variables. GAM analysis demonstrated a linear and non-linear significant interaction between NIHSS and CMB and between NIHSS and infarction size, respectively.ConclusionCerebral microbleeds, NIHSS, and infarction size were identified as HT predictors. The best predicting models were RFC and GBC capable of capturing nonlinear interaction between predictors. Predictor interaction suggests a dynamic, rather than, fixed cutoff risk value for any of these predictors.
Background: Post stroke delirium is a multifactorial life-threatening process, still poorly understood. The aim of the study was to identify the risk factors associated with the development of delirium in acute stroke patients and detection of the effect of delirium on the short-term prognosis of acute stroke patients.Patients and methods: This study was carried on 74 acute stroke patients, 40 males (54.1%) and 34 females (45.9%). Full general and neurological examination was performed to all patients. Full routine laboratory investigation and computed tomography scan and/or magnetic resonance imaging of the brain were done. Results: The patients were divided into two groups: 15 patients with delirium (group I) and 59 patients without delirium (group II). The incidence of delirium was higher among patients with older age (P = 0.002). There was no statistically significant relationship between incidence of delirium and sex of patients (P = 0.52). The delirium patients had significantly higher National Institutes of Health Stroke Scale (NIHSS) (P = 0.001) and lower Glasgow Coma Scale (GSC) (P = 0.001) at admission. They also had high mortality (P = 0.017) and lower Barthel Index (BI), and these results were statistically (P = 0.001) significantly. Conclusion: Post stroke delirium was associated with old age, higher NIHSS at admission, intracerebral hemorrhage, and higher long-term mortality.
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