Development and validation of a dynamic nomogram for predicting poor outcomes in patients with internal carotid artery occlusion undergoing endovascular therapy.

OBJECTIVE

Patients with internal carotid artery occlusion (ICAO) present with a heavy thrombosis burden and bad lateral circulation, which are associated with unfavorable outcomes following endovascular therapy (EVT). In this study, authors explored the risk factors associated with poor outcomes in patients with ICAO undergoing EVT and developed and validated a dynamic nomogram for predicting poor outcomes.

METHODS

Five hundred seventy-seven patients from the multicenter, randomized, double-blind, placebo-controlled MARVEL (Methylprednisolone as Adjunctive to Endovascular Treatment for Acute Large Vessel Occlusion) trial were included in the current retrospective study. The patients, all of whom had ICAO and received EVT between February 2022 and June 2023, were split into training (60%) and internal validation (40%) cohorts. Additionally, 281 patients from the Endovascular Treatment for Acute Anterior Circulation Ischemic Stroke registry (ACTUAL registry) served as the external validation cohort. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analyses were applied to identify risk factors to establish a dynamic nomogram prediction model.

RESULTS

Five risk factors were independently associated with poor outcome, including age (OR 0.951, 95% CI 0.935-0.968, p < 0.001), baseline Alberta Stroke Programme Early CT Score (OR 1.176, 95% CI 1.075-1.286, p < 0.001), baseline National Institutes of Health Stroke Scale score (OR 0.850, 95% CI 0.801-0.901, p < 0.001), baseline American Society of Interventional and Therapeutic Neuroradiology and Society of Interventional Radiology grade (OR 1.646, 95% CI 1.388-1.951, p < 0.001), and baseline glucose levels (OR 0.891, 95% CI 0.827-0.959, p = 0.002). The prediction model, based on these five factors, showed moderate performance with an area under the curve of 0.786 (95% CI 0.728-0.844) in the internal validation and 0.795 (95% CI 0.743-0.847) in the external validation, with the calibration curve closely aligning with the ideal diagonal line.

CONCLUSIONS

This predictive model can accurately forecast poor outcomes for patients with ICAO undergoing EVT, serving as a useful adjunct in operative decision-making for both physicians and patient families.