INTERVENTIONAL RADIOLOGY / ORIGINAL PAPER
Figure from article: Prognostic value of...
 
KEYWORDS
TOPICS
ABSTRACT
Purpose:
Hemorrhagic transformation (HT) represents a serious complication in the management of acute ischemic stroke (AIS), particularly following endovascular treatment (EVT). While clinical predictors of HT have been widely studied, the prognostic value of imaging-based collateral scoring systems remains less well defined. The main purpose of the study is to evaluate the prognostic accuracy of four commonly used collateral scoring systems – Miteff, Maas, modified Tan, the Alberta Stroke Program Early CT Score – and combinations of them, with established variables for predicting HT in AIS patients treated with EVT beyond the 4.5-hour window.

Material and methods:
This retrospective single-center study included 162 AIS patients who underwent EVT between 2017 and 2023. Patients with baseline computed tomography angiography and follow-up computed tomography were included; those receiving thrombolysis were excluded. Collateral status was assessed using four scoring systems. HT was confirmed by imaging. Patients were divided into HT and non-HT groups. Statistical analysis included c2 tests, binary logistic regression, and ROC curve analysis and evaluation of predictive models (Collateral Model and Integrated Clinical-Collateral Model). Significance was set at p < 0.05.

Results:
None of the individual collateral scoring systems reached statistical significance as independent predictors of HT. The Collateral Model achieved an area under the curve (AUC) of 0.837 (95% confidence interval [CI]: 0.769-0.906), indicating acceptable discrimination. The Integrated Clinical-Collateral Model further improved predictive performance, with an AUC of 0.933 (95% CI: 0.883-0.983), reflecting excellent accuracy.

Conclusions:
While individual collateral scoring systems showed limited value in predicting HT risk, their combined use improved prognostic accuracy. The predictive accuracy was even better when collateral scores were integrated with established clinical and imaging predictors.
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