New insight in massive cerebral infarction predictions after anterior circulation occlusion

• Published in: Scientific reports Vol. 13; no. 1; p. 23021
• Main Authors: Chen, Jingshu, Zou, Mingyu, Zhang, Nan, Qi, Shouliang, Yang, Benqiang, Zhang, Libo, Shi, Lin, Duan, Yang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.12.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378; 692/308; 692/499; 692/699; Angiography; Brain Ischemia; Cerebral Angiography - methods; Cerebral blood flow; Cerebral infarction; Cerebral Infarction - diagnostic imaging; Cerebral Infarction - etiology; Cerebrovascular Circulation; Computed tomography; Computed Tomography Angiography - methods; Humanities and Social Sciences; Humans; Ischemia; Medical imaging; multidisciplinary; Reclassification; Retrospective Studies; Science; Science (multidisciplinary); Stroke
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-50175-4

To predict massive cerebral infarction (MCI) occurrence after anterior circulation occlusion (ACO) by cASPECTS-CTA-CS (combined ASPECTS and CTA-CS). Of 185 cerebral infarction patients with the ACO, their collateral circulation scores from CT angiography (CTA) images in two groups (MCI and non-MCI) were evaluated using Alberta Stroke Program Early CT Score (ASPECTS) and CT angiography collateral score (CTA-CS) approaches. The cASPECTS-CTA-CS was validated internally using the bootstrap sampling method with 1000 bootstrap repetitions and compared to CTA-CS. Receiver-operating characteristic curve (ROC), clinical impact curve (CIC), and decision curve analysis (DCA) strategies were used to assess the clinical practicality and predictability of both approaches (cASPECTS-CTA-CS and CTA-CS). Using net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analyses, discrimination levels of the cASPECTS-CTA-CS were compared with CTA-CS. Classification and regression tree (CART) analyses was conducted to identify the best predictive values and identify subgroup of MCI. The discrimination ability of collateral circulation evaluation score using the cASPECTS-CTA-CS [AUC: 0.918, 95% confidence interval (CI): 0.869–0.967, P  < 0.01; NRI: 0.200, 95% CI: −0.104 to 0.505, P  = 0.197; and IDI: 0.107, 95% CI: 0.035–0.178, P  = 0.004] was better than CTA-CS alone (AUC: 0.885, 95% CI: 0.833–0.937, P  < 0.01). DCA indicated the net benefits of the cASPECTS-CTA-CS approach was higher than CTA-CS alone when the threshold probability range over 20%. CIC analyses showed that the number of high risks and true positives were in agreement when the threshold probability > 80%. Less than 23 of cASPECTS-CTA-CS by CART was important factor in determining MCI occurrence, and ASPECTS < 7 was followed factor. The cASPECTS-CTA-CS approach cumulatively predicted MCI after ACO.