Nomogram to predict symptomatic intracranial hemorrhage after mechanical thrombectomy in patients with intracranial atherosclerosis-related large-vessel occlusion

Background Despite significant advancements in endovascular treatment (EVT) over the past decade, symptomatic intracerebral hemorrhage (sICH) persists as a serious complication, especially in patients with acute ischemic stroke (AIS) related to intracranial atherosclerotic stenosis (ICAS), which is characterized by complex and calcified plaques. The present study aimed to develop a predictive nomogram for assessing the risk of sICH in AIS patients with ICAS and large-vessel occlusion (LVO) who undergo EVT. Methods We analyzed data from 183 patients with acute ischemic stroke (AIS) due to intracranial atherosclerotic stenosis-related large vessel occlusion (ICAS-LVO) who underwent endovascular treatment (EVT) between January 2022 and December 2023. The primary safety outcome was the incidence of symptomatic intracranial hemorrhage (sICH). A nomogram was developed based on a multivariable logistic regression model, and the calibration of the model was evaluated using Spiegelhalter’s Z -test. Results In our developed nomogram, elevated D-dimer levels (odds ratio [OR] 2.87; 95% confidence interval [CI], 1.53–5.39; p = 0.001), longer onset-to-recanalization time (OR 1.00; 95% CI, 1.00–1.01; p = 0.004), and larger core infarct volume (OR 1.04; 95% CI, 1.01–1.06; p = 0.002) were identified as independent predictors of symptomatic intracranial hemorrhage (sICH) in stroke patients undergoing endovascular treatment (EVT). The goodness-of-fit of the model was confirmed by a non-significant Spiegelhalter’s Z -test ( p = 0.941). Conclusion A nomogram integrating D-dimer levels, onset-to-recanalization time, and core infarct volume offers a reliable and practical method for estimating the risk of symptomatic intracranial hemorrhage in acute ischemic stroke patients with intracranial atherosclerotic stenosis-related large vessel occlusion receiving endovascular therapy.