Zohdy, Youssef M.

Introduction Infectious intracranial aneurysms (IIAs) are rare but serious complications of systemic infections, particularly infective endocarditis. These aneurysms are prone to rupture, leading to significant morbidity and mortality. Management strategies lack consensus due to the rarity of the condition and reliance on small case series. This study examines the clinical management of IIAs using data from a large multicenter cohort. Methods A retrospective registry-based cohort study was conducted across 11 tertiary care centers in the USA between 2018 and 2023. Patients with IIAs were identified based on clinical and radiographic criteria. The primary outcome was treatment failure defined as persistence, growth, or rupture of the aneurysm. Secondary outcomes were mortality and the modified Rankin Scale (mRS) score at 90 days and 1 year. Multivariate logistic regressions were used to identify outcome predictors. Results A total of 104 patients with 166 aneurysms were included, with a median age of 43 years. Medical management was successful in 56% of cases, with failure often within 18 days of initiation. Predictors of failure included younger age, larger aneurysm size, and rupture at presentation. Surgical and endovascular interventions achieved higher success rates with better outcomes. At 90 days, 57% of patients achieved functional independence (mRS 0–2), while the mortality rate was 24%. Conclusion This study highlights the limitations of medical management for IIAs and underscores the need for early surgical or endovascular intervention in high-risk patients. Outcome predictors aid clinical decision-making, optimizing patient management. Further research is needed to standardize management guidelines for IIAs.

Introduction: Up to 50% of acute ischemic stroke (AIS) patients who undergo successful mechanical thrombectomy (MT) fail to achieve favorable outcomes (futile recanalization). In this study we aim to develop a machine learning (ML) model to predict futile recanalization (FR) in AIS patients who undergo MT. Methods: We used data from an ongoing large, multicenter database from 2013 to 2023. We included AIS patients treated with MT for ICA, M1, or M2 occlusion with successful recanalization (modified Thrombolysis in Cerebral Infarction [mTICI] score ≥ 2C) and procedure durations under 60 minutes. FR was defined as successful recanalization with 90-day modified Rankin Scale (mRS) 3-6. The dataset was divided into 75% for training and 25% for external validation. Using the Caret Package in R, multiple models were tested, and their performances were evaluated by the area under the curve (AUC) of receiver operating. Both baseline and pre-interventional characteristics were incorporated into the model. The selected model was then externally validated on a 25% validation dataset. Results: Among 2,546 qualified patients, FR occurred in 1,342 (52.7%). In univariate analysis, baseline characteristics were significantly different between FR and non-FR groups. The M5P model demonstrated the highest performance (AUC: 0.833; 95% CI: 0.7989-0.852; PPV: 0.8101) in comparison to other tested models such as logistic regression (AUC: 0.74), RF (AUC: 0.78), J48 (AUC: 0.78), SVM (AUC: 0.79), and GB (AUC: 0.79). The external validation of the model showed satisfactory results (AUC: 75.25; 95% CI: 70-80; PPV: 76.87). Conclusion: Utilizing clinical, pre-procedural, and imaging parameters, the M5P model can efficiently predict F) in AIS patients before attempting MT. This tool can assist neurointerventionalists in adequately choosing their MT candidates.