Integrated Clinical–Metabolic Predictive Model for Long-Term Functional Outcome in Anterior Communicating Artery Aneurysms

Abstract Introduction: Anterior communicating artery (ACoA) aneurysms remain a significant surgical challenge due to their proximity to hypothalamic structures and the associated risk of neurocognitive impairment. Although clinical grading scales continue to be essential for initial prognostic assessment, the incorporation of metabolic biomarkers may enhance the predictive accuracy for long-term neurological recovery. Objective The aim of this study was to develop and validate a bimodal model capable of predicting functional independence at 12 months (mRS ≤ 2), integrating neurological severity scales with systemic metabolic variables obtained at admission. Methods A retrospective analysis was conducted on 100 patients with ACoA aneurysms treated by microsurgical clipping between 2018 and 2024. Hunt–Hess (HH) and Fisher scores were recorded, along with serum sodium and glucose levels. Functional outcome at one year was assessed using the mRS scale. Predictive performance was evaluated through AUC-ROC analysis and multivariate logistic regression. Results A total of 90% of patients achieved a favorable outcome. Unfavorable outcomes were associated with higher HH scores (p < 0.001) and vasospasm (71.4% vs. 27.8%; p = 0.047). In the multivariate model, HH grade (OR 0.24; 95% CI 0.09–0.62; p = 0.003) and initial serum sodium (OR 1.29; 95% CI 1.01–1.66; p = 0.046) emerged as independent predictors. The integrated model demonstrated strong discriminative ability (AUC-ROC 0.885; 95% CI 0.76–0.99). Conclusions Initial clinical severity and sodium homeostasis play key roles in determining long-term functional outcome. The bimodal model enhances discriminative capacity and underscores the value of incorporating metabolic parameters into early risk stratification.