Background and objectives: Middle meningeal artery embolization (MMAE) has become a pivotal intervention in managing chronic subdural hematomas (cSDHs). This systematic review synthesizes past, recent, and ongoing clinical trials to assess MMAE's role in cSDH treatment. Methods: A systematic review was conducted using PRISMA guidelines, incorporating PubMed, ClinicalTrials.gov, and reverse bibliography searches to identify clinical trials evaluating MMAE for cSDH. Inclusion criteria included randomized and nonrandomized trials reporting outcomes, such as recurrence rates and procedural safety. Case reports, retrospective reviews, and opinion pieces were excluded. Results: Seven published and 15 ongoing trials were identified. Landmark randomized controlled trials (RCTs), including EMBOLISE, STEM, and MAGIC-MT, demonstrated reductions in hematoma recurrence and surgical rescues with MMAE, establishing its role as both an adjunctive and standalone therapy. Ongoing trials, such as EMPROTECT and CHESS, investigate diverse embolic agents, procedural strategies, and patient populations to optimize MMAE outcomes. However, challenges remain, including variability in patient selection criteria, embolic materials, and endpoints. Conclusion: MMAE is an innovative and minimally invasive approach that has reshaped cSDH management. Evidence supports its efficacy and safety as an adjunct to surgery and a potential standalone therapy for select patients. Future research should focus on long-term outcomes, subgroup analyses, and standardization of protocols to further refine its application and integration into clinical practice.

Introduction: Ruptured intracranial aneurysms (RIAs) are a significant cause of morbidity and mortality. While individual-level risk factors for aneurysmal subarachnoid hemorrhage (aSAH) are well established, the influence of meteorological variables has been widely debated but remains unclear. Methods: We retrospectively analyzed 1504 endovascularly treated intracranial aneurysm cases from two stroke centers (2018 to 2024). We matched daily weather data to presentation dates. We used variance inflation factor (VIF) analysis to remove collinear features. Multivariable logistic regression models were adjusted for age and sex. We developed extreme gradient boosting (XGBoost) models using weather variables alone and in combination with demographic covariates (age, sex, race, smoking status, and family history). Results: Of 1504 cases, 377 (25.1%) presented with rupture. In univariate logistic regression, greater humidity (odds ratio [OR] 0.988 per 1% relative humidity, 95% confidence interval [CI] 0.979 to 0.998, p = 0.0135) and greater ultraviolet (UV) index (OR 0.951 per index unit, 95% CI 0.914 to 0.990, p = 0.0146) were associated with reduced odds of rupture, whereas greater snow depth (OR 1.360 per inch, 95% CI 1.068 to 1.731, p = 0.0126) and advanced moon phase (OR 1.589 per full lunar cycle, 95% CI 1.064 to 2.373, p = 0.0237) were associated with increased odds. On multivariable analysis, only female sex remained protective (OR 0.605, 95% CI 0.386 to 0.949, p = 0.0285), and greater sea level pressure trended toward lower odds of rupture without reaching significance (OR 0.962, p = 0.0523). Findings were consistent in a full-cohort, center-adjusted sensitivity analysis. The weather-only XGBoost model yielded an area under the receiver operating characteristic curve (AUC) of 0.560 and a recall of 78%, which improved to an AUC of 0.590 and a recall of 83% after adding demographic variables. SHapley Additive exPlanations (SHAP) analysis identified precipitation and cloud cover as key meteorological features. Conclusions: Several weather variables correlated with rupture risk in univariate analysis, but overall predictive value was limited. Machine learning improved sensitivity while confirming patient features as the dominant contributors. Weather on the day of presentation was not significantly associated with rupture.