Computed Tomography Perfusion Derived Blood-Brain Barrier Permeability Does Not Yet Improve Prediction of Hemorrhagic Transformation

<b><i>Introduction:</i></b> Hemorrhagic transformation (HT) in acute ischemic stroke can occur as a result of reperfusion treatment. While withholding treatment may be warranted in patients with increased risk of HT, prediction of HT remains difficult. Nonlinear regression analysis can be used to estimate blood-brain barrier permeability (BBBP). The aim of this study was to identify a combination of clinical and imaging variables, including BBBP estimations, that can predict HT. <b><i>Materials and Methods:</i></b> From the Dutch acute stroke study, 545 patients treated with intravenous recombinant tissue plasminogen activator and/or intra-arterial treatment were selected, with available admission extended computed tomography (CT) perfusion and follow-up imaging. Patient admission treatment characteristics and CT imaging parameters regarding occlusion site, stroke severity, and BBBP were recorded. HT was assessed on day 3 follow-up imaging. The association between potential predictors and HT was analyzed using univariate and multivariate logistic regression. To compare the added value of BBBP, areas under the curve (AUCs) were created from 2 models, with and without BBBP. <b><i>Results:</i></b> HT occurred in 57 patients (10%). In univariate analysis, older age (OR 1.03, 95% CI 1.006–1.05), higher admission National Institutes of Health Stroke Scale (NIHSS; OR 1.13, 95% CI 1.08–1.18), higher clot burden (OR 1.28, 95% CI 1.16–1.41), poor collateral score (OR 3.49, 95% CI 1.85–6.58), larger Alberta Stroke Program Early CT Score cerebral blood volume deficit size (OR 1.26, 95% CI 1.14–1.38), and increased BBBP (OR 2.22, 95% CI 1.46–3.37) were associated with HT. In multivariate analysis with age and admission NIHSS, the addition of BBBP did not improve the AUC compared to both independent predictors alone (AUC 0.77, 95% CI 0.71–0.83). <b><i>Conclusion:</i></b> BBBP predicts HT but does not improve prediction with age and admission NIHSS.