The role of automated computed topography perfusion in prediction of hemorrhagic transformation after acute ischemic stroke
Nada Elsaid, Guido Bigliardi, Maria Luisa Dell’Acqua, Laura Vandelli, Ludovico Ciolli, Livio Picchetto, Giuseppe Borzì, R Ricceri, Roberta Pentore, Stefano Vallone, Stefano Meletti, Ahmed Saied
Abstract
Introduction The role of computed tomography perfusion (CTP) in prediction of hemorrhagic transformation (HT) has been evolving. We aimed to study the role of automated perfusion post-processing software in prediction of HT using the commercially available RAPID software. Methods Two hundred eighty-two patients with anterior circulation ischemic stroke, who underwent CTP with RAPID automated post-processing, were retrospectively enrolled and divided into HT ( n = 91) and non-HT groups ( n = 191). The automated RAPID-generated perfusion maps were reviewed. Mismatch volume and ratio, time to maximum (Tmax) > 4‐10s volumes, hypoperfusion index, cerebral blood flow (CBF) < 20–38% volumes, cerebral blood volume (CBV) < 34%–42% volumes, and CBV index were recorded and analyzed. Results The volumes of brain tissues suffering from reduction of cerebral blood flow (CBF < 20%–38%), reduction in cerebral blood volumes (CBV < 34–42%), and delayed contrast arrival times (Tmax > 4–10s) were significantly higher in the HT group. The mismatch volumes were also higher in the HT group ( p = .001). Among these parameters, the Tmax > 6s volume was the most reliable and sensitive predictor of HT ( p = .001, AUC = 0.667). However, the combination of the perfusion parameters can slightly improve the diagnostic efficiency (AUC = 0.703). There was no statistically significant difference between the non-HT group and either the parenchymal or the symptomatic subtypes. Conclusion The RAPID automated CTP parameters can provide a reliable predictor of HT overall but not the parenchymal or the symptomatic subtypes. The infarct area involving the penumbra and core represented by the Tmax > 6s threshold is the most sensitive predictor; however, the combination of the perfusion parameters can slightly improve the diagnostic efficiency.