Background/Objectives: Nonacute subdural hematomas (naSDHs) are a prevalent intracranial pathology, particularly in older people, due to increased brain atrophy, fall risk, and anticoagulant use. This study examines the impact of frailty on the surgical outcomes of craniotomy for naSDH over 20 years. Methods: Data from the Nationwide Inpatient Sample (NIS) from 2000 to 2021 were analyzed, including 251,597 patients who underwent cranial decompression for naSDH. Patients were selected using specific ICD codes. Frailty was calculated using the modified frailty index (mFI-5 and mFI-11) and the Charlson Comorbidity Index (CCI). Outcomes were compared using descriptive statistics and multivariable regression models. Results: 251,597 patients underwent craniotomy, with a mean age of 69.2 years. The cohort exhibited significant comorbid conditions, reflected in a mean Charlson Comorbidity Index (CCI) of 3.8, and a high frailty prevalence, with 23.49% of patients classified as frail and 20.14% as severely frail. The CCI demonstrated the highest predictive value for adverse outcomes, with an area under the curve (AUC) of 0.6346 for mortality and 0.6804 for complications. Frailty indices (mFI-5 and mFI-11) were also strongly associated with increased mortality (p < 0.001), complications (p < 0.001), and extended length of stay (p < 0.001). Age was not a significant predictor of outcomes. Conclusions: This study highlights the moderate impact of frailty on surgical outcomes for naSDH.

Background: Cerebral sinus thrombosis as presentation of acute promyelocytic leukemia (AMPL) is exceptional, with only three cases registered in the literature. Case Description: A 24-year-old female patient was transferred to our center after a car accident. The patient had a witnessed generalized seizure while driving. Computerized tomography (CT) demonstrated a temporal intraparenchymal hemorrhage and CT venogram diagnosed a cerebral sinus thrombosis on the left transverse and sigmoid sinus. The patient underwent surgical evacuation of the hematoma and was treated with anticoagulation 48 h after surgery. Pancytopenia alerted of a possible hematological disorder. The patient was subsequently diagnosed with AMPL and treated with arsenic trioxide. The patient had a complete neurological recovery with no postoperative complications. Conclusion: The management of cerebral sinus thrombosis in patients with AMPL remains controversial. The previous reported cases of cerebral sinus thrombosis preceding the diagnosis of AMPL are reviewed and treatment of cerebral sinus thrombosis with anticoagulation in the setting of intraparenchymal hemorrhage and bleeding disorders is also discussed.

Introduction: Large vessel occlusion (LVO) strokes are a significant cause of disability and death globally. Endovascular mechanical thrombectomy (EVT) has improved outcomes for LVO patients. However, the impact of pre-existing frailty on outcomes for patients undergoing EVT remains understudied. Methods: This study utilized the National Inpatient Sample (NIS) database to identify patients aged 18 and older who underwent EVT for LVO of the internal carotid artery (ICA) or middle cerebral artery (MCA) from 2016 to 2021. Frailty was assessed using the modified Frailty Index-5 (mFI-5), mFI-11, Charlson Comorbidity Index (CCI), and Risk Analysis Index (RAI). Outcomes analyzed included mortality, complications, length of stay (LOS), discharge disposition, and cost of care. Results: We analyzed 57,260 EVT admissions. Patients classified as highly frail by the CCI had the highest in-hospital mortality (odds ratio (OR) 1.13, p < 0.0001). High frailty identified by mFI-5 was associated with a higher likelihood of postoperative complications (OR 1.41, p < 0.0001) and extended LOS (OR 1.23, p < 0.0001). High frailty by the administrative RAI was linked to nonhome discharge (OR 1.31, p < 0.0001), with mFI-5 showing similar predictive value (OR 1.30, p < 0.0001). High frailty, as determined by the CCI, incurred the highest estimated cost of care (p < 0.0001). Conclusions: Frailty assessment using mFI-5 is a practical predictor of patient outcomes in LVO thrombectomy, including complication rates, extended LOS, and discharge disposition. The CCI remains the most predictive for mortality, though it is more complex to calculate. We emphasize that frailty, as measured by the mFI-5, is one of many factors to consider when deciding on EVT for LVO.

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.