Background/Objectives: India has experienced a sharp increase in stroke burden over the last half-century. The diverse geographical conditions and developing health infrastructure warrant an investigation into changes in mortality and morbidity due to ischemic stroke. This research aims to estimate the impact of attributable risk factors, providing comprehensive insights into the temporal trends of ischemic stroke in India. Methods: Data regarding ischemic stroke in India were queried from the 2019 Global Burden of Disease (GBD) study. Age-standardized deaths, disability-adjusted life years (DALYs), years lived with disability (YLDs), years of life lost (YLLs), prevalence, and incidence were collected and analyzed. Descriptive statistics and 95% uncertainty intervals (UI) were utilized to ensure reliability. Results: In 2019, there were 535,700 incident stroke cases in India (95% CI 453,200–631,800), marking a 118.8% increase from 1990. Females saw a higher incidence rise (131.1%) than males (107.5%). The incidence rate reached 38.5 per 100,000 (95% CI 32.6–45.4), a 34.6% increase, while the age-standardized incidence rate declined by 4.2%. Stroke deaths rose by 148.5%, totaling 271,200 (95% CI 227,800–320,700). Females experienced a higher increase (183.5%) than males (123.0%). Prevalence increased by 130.4%, reaching 6,465,700 cases (95% CI 5,541,000–7,378,000), while age-standardized prevalence rose by 5.7%. Disability-adjusted life years (DALYs) increased by 121.0% to 5,689,300 (95% CI 4,821,100–6,649,500), but the age-standardized DALY rate fell by 23.1%. Years lived with disability (YLD) increased by 135.5% to 907,000 (95% CI 640,300–1,172,900). Years of life lost (YLL) rose by 118.4% to 4,782,300 (95% CI 3,945,500–5,743,700), but the age-standardized YLL rate fell by 26.3%. Metabolic risks, ambient particulate matter pollution, and tobacco smoking were significant risk factors contributing to stroke burden. Conclusions: This study highlights contrasting trends in ischemic stroke indicators in India from 1990 to 2019. While prevalence and YLDs have increased, the overall decline in mortality, YLLs, DALYs, and stroke incidence points to improvements in healthcare access and treatment. There is a need for targeted public health interventions to address the growing stroke burden, mainly focusing on preventive measures and addressing sex-based disparities.

Introduction: The ARUBA trial has influenced a shift towards more conservative management of small, unruptured cerebral arteriovenous malformations (AVMs), leading to less aggressive treatment approaches among neurointerventionalists. However, with evolving endovascular techniques, it is important to assess whether national practice changes have impacted rupture rates and outcomes. Methods: Data from the Nationwide Inpatient Sample (NIS) for 2016–2022 regarding clinical characteristics, cost, morbidity, and mortality endovascularly treated AVMs was analyzed. Trends were evaluated using multivariable regression modeling, controlling for patient and hospital characteristics. Results: A total of 8,935 patients underwent endovascular treatment for cerebral AVMs (6,500 unruptured and 2,435 ruptured). Endovascular treatment for unruptured AVMs declined significantly from 1195 cases in 2016 to 780 in 2022 (−34.7%, p = 0.020). Over the same period, the proportion of AVMs presenting ruptured increased from 15.7% to 25.7%, a 63.7% relative increase ( p = 0.015). Multivariable analysis confirmed rising odds of rupture over time among endovascularly treated patients (OR = 1.12, 95% CI 1.00–1.25, p = 0.042). Among 11,205 ruptured AVM patients, the in-hospital mortality rate of thosewho did not undergo surgery rose 67.2% over time (6.7% in 2016 to 11.2% in 2022, p = 0.006). Conclusion: The study reveals an increase in the probability of patients presenting with ruptured AVMs and being observed with a consequent increase in in-hospital mortality. This may be an unintended consequence of less aggressive strategies following the ARUBA trial, prompting a need to reevaluate current management techniques amidst rising costs, morbidity, and mortality.

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.

BACKGROUND AND OBJECTIVES: Effective patient education is essential in neurosurgery, but many materials exceed recommended readability levels, which can limit comprehension and informed consent. Simplification can also alter tone, potentially introducing bias. Recent studies have used large language models such as Chat Generative Pre-trained Transformer (ChatGPT) to simplify neurosurgical patient education materials (PEMs), but the impact of this process on sentiment and emotional tone remains unclear. Our objective was to assess the sentiment and emotional tone of neurosurgical PEMs before and after conversion to a lower reading level by ChatGPT. METHODS: A total of 336 neurosurgical PEMs covering stroke, spinal stenosis, hydrocephalus, epilepsy, and pituitary brain tumors were analyzed for readability, sentiment, and emotion. Each was then simplified to a seventh grade level using GPT-4.0. Readability was evaluated using Flesch-Kincaid Grade, Flesch Reading Ease, Gunning Fog Index, Automated Readability Index, Coleman-Liau Index, and Simple Measure of Gobbledygook. Sentiment and emotional tone were described using the Valence Aware Dictionary and sEntiment Reasoner (VADER) algorithm and National Research Council Canada Emotion Lexicon. Paired statistical t -tests assessed the significance of changes. RESULTS: Simplification produced substantial improvements in readability across all 6 indices and all neurosurgical topics ( P < .001). Sentiment shifted toward increased positivity, reflected by higher VADER compound scores, more positive tokens, and fewer neutral tokens. Disgust decreased significantly across every topic, whereas sadness, surprise, and joy increased modestly; fear and anger showed no significant change. Topic-level analyses mirrored global patterns, demonstrating consistent directional effects. Overall, simplification achieved large readability gains while introducing small but measurable alterations in emotional tone. CONCLUSION: The decrease in neutral and negative sentiment suggests a shift toward more persuasive language. Modest but consistent shifts in sentiment and emotional tone accompanying artificial intelligence–assisted simplification highlight the potential for unintended affective shifts during artificial intelligence simplification and warrant monitoring when deploying large language models for patient-facing materials. Current PEMs pose a communication barrier between patient and provider, but providers must be careful.