Intracranial aneurysms (IAs) pose a significant public health threat due to their potential to rupture, causing subarachnoid hemorrhage (SAH) and high rates of morbidity and mortality. In Ecuador, limited access to endovascular treatment heightens the need for early risk identification. To analyze the morphological and demographic characteristics associated with aneurysm rupture in Ecuadorian patients and to develop a predictive model for rupture risk. A retrospective, cross-sectional study was conducted involving 236 patients with 302 intracranial aneurysms diagnosed at a tertiary center in Quito, Ecuador. Data collected included demographics, comorbidities, aneurysm morphology (size, angulation, location), and treatment approach. Logistic regression was used to identify rupture predictors, and a predictive model was developed. The mean age was 48.42 years; most patients were female (69.49%) and mestizo (98.3%). Hypertension was the most common comorbidity (41.1%). Rupture was observed in 72.45% of aneurysms, with SAH being the most frequent presentation. Aneurysms in the PComm (OR: 2.71, p < 0.001) and AComA (OR: 3.63, p = 0.001), with a length > 4.43 mm (OR: 3.68, p = 0.0001) or Type 2 angle (45–90°, OR: 1.81, p = 0.01), had significantly higher rupture risk. The final model showed 64.6% accuracy (AUC: 0.692). Morphological features are strong predictors of rupture and may guide early intervention. The proposed model is simple and practical, with potential for integration into triage protocols, especially in resource-limited settings. These findings highlight the importance of morphology-based risk assessment in improving aneurysm outcomes.
