The value of multiphase CT angiography in predicting hemorrhagic transformation after endovascular treatment: the arterial collaterals and venous drainage

Background: Hemorrhagic transformation (HT) is a common and severe complication after endovascular treatment (EVT), especially the symptomatic intracerebral hemorrhage (sICH). This study aimed to explore the quantitative diagnostic value of multiphase computed tomography (CT) angiography (mCTA) in predicting HT and sICH in patients with anterior circulation acute ischemic stroke (AIS) after EVT. Methods: We retrospectively collected data of patients with anterior circulation AIS from Tianjin Huanhu Hospital from April 2020 to December 2023. We assessed the arterial collateral circulation (ACC), superficial venous drainage scores (SVS), and deep venous drainage scores (DVS) based on mCTA. SVS1, SVS2, and SVS3, as well as DVS1, DVS2, and DVS3, represented SVS and DVS in the arterial, venous, and late venous phases, respectively. Patients were divided into HT and non-HT groups based on the presence of an intracranial hemorrhage on a follow-up non-contrast CT. We conducted a subgroup analysis of HT patients, dividing them into sICH and asymptomatic intracerebral hemorrhage (aICH) subgroups. We analyzed and compared the clinical variables, ACC, SVS, and DVS in the non-HT and HT groups, as well as in the sICH and aICH subgroups. Multiparameter predictive models of HT and sICH were established using ACC (Model-HT1 and Model-sICH1), SVS and DVS (Model-HT2 and Model-sICH2), and comprehensive parameters (Model-HT3 and Model-sICH3). The performance of predictive models was evaluated and compared using the receiver operating characteristic (ROC) curve and the Delong test. Results: Finally, 127 patients were included, and 46 developed HT. The HT group had a higher ratio of poor ACC than the non-HT group (97.83% vs. 81.48%, P=0.010). A lower SVS1, SVS2, and SVS3 were observed in the HT group compared with the non-HT group (all P<0.05). For the subgroup analysis of HT patients, 14 had sICH, all these patients had poor ACC. A lower SVS1, SVS3, DVS1, and DVS2 were observed in the sICH subgroup than in the aICH subgroup {2 [1, 3] vs. 3 [2, 4], P=0.018; 8 [7, 8] vs. 8 [8, 8], P=0.014; 1 [0.75, 1] vs. 1 [1, 2], P=0.025; and 2 [1, 2] vs. 2 [2, 2], P=0.047}. In Model-HT1 and Model-HT3, ACC was the independent predictor for HT [odds ratio (OR), 13.924; 95% confidence interval (CI): 1.671–115.991; P<0.05 and OR, 9.141; 95% CI: 1.149–72.723; P<0.05]. In Model-sICH2 and Model-sICH3, DVS2 was the independent predictor for sICH (OR, 0.1; 95% CI: 0.018–0.567; P<0.05 and OR, 0.1; 95% CI: 0.018–0.567; P<0.05). The ROC showed that Model-HT3 and Model-sICH2 improved predictive efficacy [area under curve (AUC): 0.789; 95% CI: 0.707–0.856; and 0.828; 95% CI: 0.688–0.893, respectively]. Conclusions: The ACC, SVS, and DVS based on mCTA were valuable for predicting the risk of HT and sICH after EVT, the combination of multiple parameters can improve the predictive efficacy.