Citations

Background: Acute ischemic stroke (AIS) from primary medium vessel occlusions (MeVO) is a prevalent condition associated with substantial morbidity and mortality. Despite the common use of mechanical thrombectomy (MT) in AIS, predictors of poor outcomes in MeVO remain poorly characterized. Methods: In this prospectively collected, retrospectively reviewed, multicenter, multinational study, data from the MAD-MT (Multicenter Analysis of primary Distal medium vessel occlusions: effect of Mechanical Thrombectomy) registry were analyzed. The study included 1568 patients from 37 academic centers across North America, Asia, and Europe, treated with MT, with or without intravenous tissue plasminogen activator (IVtPA), between September 2017 and July 2021. Results: Among the 1568 patients, 347 (22.2%) experienced very poor outcomes (modified Rankin score (mRS), 5–6). Key predictors of poor outcomes were advanced age (odds ratio (OR): 1.03; 95% confidence interval (CI): 1.02 to 1.04; p < 0.001), higher baseline National Institutes of Health Stroke Scale (NIHSS) scores (OR: 1.07; 95% CI: 1.05 to 1.10; p < 0.001), pre-operative glucose levels (OR: 1.01; 95% CI: 1.00 to 1.02; p < 0.001), and a baseline mRS of 4 (OR: 2.69; 95% CI: 1.25 to 5.82; p = 0.011). The multivariable model demonstrated good predictive accuracy with an area under the receiver-operating characteristic (ROC) curve of 0.76. Conclusions: This study demonstrates that advanced age, higher NIHSS scores, elevated pre-stroke mRS, and pre-operative glucose levels significantly predict very poor outcomes in AIS-MeVO patients who received MT. These findings highlight the importance of a comprehensive risk assessment in primary MeVO patients for personalized treatment strategies. However, they also suggest a need for cautious patient selection for endovascular thrombectomy. Further prospective studies are needed to confirm these findings and explore targeted therapeutic interventions.

ObjectiveWe aimed to evaluate lung cancer risk in patients with rheumatoid arthritis (RA) and RA‐interstitial lung disease (ILD).MethodsWe performed a retrospective, matched cohort study of RA and RA‐ILD within the Veterans Health Administration (VA) between 2000 and 2019. Patients with RA and RA‐ILD were identified with validated administrative‐based algorithms, then matched (up to 1:10) on age, gender, and VA enrollment year to individuals without RA. Lung cancers were identified from a VA oncology database and the National Death Index. Conditional Cox regression models assessed lung cancer risk adjusting for race, ethnicity, smoking status, Agent Orange exposure, and comorbidity burden among matched individuals. Several sensitivity analyses were performed.ResultsWe matched 72,795 patients with RA with 633,937 patients without RA (mean age 63 years; 88% male). Over 4,481,323 patient‐years, 17,099 incident lung cancers occurred. RA was independently associated with an increased lung cancer risk (adjusted hazard ratio [aHR] 1.58 [95% confidence interval (CI) 1.52–1.64]), which persisted in never smokers (aHR 1.65 [95% CI 1.22–2.24]) and in those with incident RA (aHR 1.54 [95% CI 1.44–1.65]). Compared to non‐RA controls, prevalent RA‐ILD (n = 757) was more strongly associated with lung cancer risk (aHR 3.25 [95% CI 2.13–4.95]) than RA without ILD (aHR 1.57 [95% CI 1.51–1.64]). Analyses of both prevalent and incident RA‐ILD produced similar results (RA‐ILD vs non‐RA aHR 2.88 [95% CI 2.45–3.40]).ConclusionRA was associated with a >50% increased risk of lung cancer, and those with RA‐ILD represented a particularly high‐risk group with an approximate three‐fold increased risk. Increased lung cancer surveillance in RA, and especially RA‐ILD, may be a useful strategy for reducing the burden posed by the leading cause of cancer death.

With an annual cumulative occurrence of approximately 15,000 in North America, all childhood cancers are rare. Very rare cancers as defined by both the European Cooperative Study Group for Rare Pediatric Cancers (EXPeRT) and the Children’s Oncology Group (COG) fall into two principal categories: those so uncommon (fewer than 2 cases/million) that their study is challenging even through cooperative group efforts (e.g. pleuropulmonary blastoma, desmoplastic small round cell tumor) and those that are far more common in adults and therefore rarely studied in children (e.g. thyroid, melanoma, gastrointestinal stromal tumor). [1](#ref-0001) Treatment strategies for these latter tumors are typically based on adult guidelines, although the pediatric variants of these tumors may harbor different genetic signatures and demonstrate different behavior. If melanoma and differentiated thyroid cancer are excluded, other rare cancer types account for only 2% of the cancers in children aged 0 to 14.[1](#ref-0001) This article highlights several of the most common rare tumor types.

Abstract The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient’s demographics and social determinants of health. While current investigations in neuro-oncology have broadly utilized artificial intelligence (AI) to enrich tumor diagnosis and more accurately predict treatment response, postoperative complications, and survival, equity-driven applications of AI have been limited. However, AI applications to advance health equity in the broader medical field have the potential to serve as practical blueprints to address known disparities in neuro-oncologic care. In this consensus review, we will describe current applications of AI in neuro-oncology, postulate viable AI solutions for the most pressing inequities in neuro-oncology based on broader literature, propose a framework for the effective integration of equity into AI-based neuro-oncology research, and close with the limitations of AI.

Ramadan is the Islamic holy month when Muslims around the world fast from dawn to sunset. This 30-day pattern of intermittent diurnal fasting can have a significant physiologic impact on the body. Importantly, oral intake is forbidden during this time, and many patients do not wish to take medications. From a clinical perspective, this potentially impacts healthcare delivery and chronic disease states. Despite these important changes, awareness of individual patient practices remains limited among healthcare providers in North America, which may worsen health disparities in Muslim patients. A fundamental understanding of the cultural and physiological implications of fasting during Ramadan can improve cultural competence and patient outcomes. In this paper, we review the physiologic changes during fasting, medical exemptions to fasting, and special considerations for the care of Muslim patients with chronic conditions who may fast during Ramadan.

Study Design Prospective Cohort Study. Objective The present study aims to determine if the racial representation of patients enrolled in a large prospective scoliosis registry is reflective of the general United States population. Further, we studied whether there was an association between race, pre-operative parameters, outcomes and loss to follow-up. Methods Prospectively collected data for patients who underwent spinal fusion for adolescent idiopathic scoliosis (AIS) was reviewed, including self-reported race/ethnicity. The U.S. pediatric population and U.S. patients enrolled in the prospective registry were compared. The data obtained was analyzed for variations between races, for pre-operative variables and follow-up. Results Of the 2210 included patients in the registry 66% of patients reported as White, while 52% of the 2018 U.S. pediatric population reported as White. 15% of the registry reported as Hispanic/Latino compared to 22% of the U.S. pediatric population, 13% Black compared to 14% of the U.S. pediatric population, and 4% Asian compared to 5% of the U.S. pediatric population. Asian and White patients had statistically significant higher 2-year follow-up in all but one of six enrollment sites ( P < 0.001). Native American, Other, and Hispanic/Latino patients had the highest BMIs. Native American and Black patients had the highest pre-op thoracic Cobb angles. Pre-op ages of Black, Hispanic, and Native American patients were statistically lower ( P < 0.01). Conclusion This study demonstrates the association between race and patient follow-up and pre-operative factors in patients who underwent surgery for AIS. Black, Native American, and Hispanic populations were underrepresented both at pre-op and follow-up when compared to their relative proportion in the U.S. pediatric population.

Abstract INTRODUCTION We aimed to evaluate clinical interpretation cutpoints for two plasma phosphorylated tau (p‐tau)217 assays (ALZpath and Lumipulse) as predictors of amyloid status for implementation in clinical practice. METHODS Clinical performance of plasma p‐tau217 against amyloid positron emission tomography status was evaluated in participants with mild cognitive impairment or mild dementia ( n = 427). RESULTS Using a one‐cutpoint approach (negative/positive), neither assay achieved ≥ 90% in both sensitivity and specificity. A two‐cutpoint approach yielding 92% sensitivity and 96% specificity provided the desired balance of false positives and false negatives, while categorizing 20% and 39% of results as indeterminate for the Lumipulse and ALZpath assays, respectively. DISCUSSION This study provides a systematic framework for selection of assay‐specific cutpoints for clinical use of plasma p‐tau217 for determination of amyloid status. Our findings suggest that a two‐cutpoint approach may have advantages in optimizing diagnostic accuracy while minimizing potential harm from false positive results. Highlights Phosphorylated tau (p‐tau)217 cutpoints for detection of amyloid pathology were established. A two‐cutpoint approach exhibited the best performance for clinical laboratory use. p‐tau217 assays differed in the percentage of results categorized as intermediate.

Abstract Background Artificial intelligence (AI) has emerged as a tool to potentially increase the efficiency and efficacy of cardiovascular care and improve clinical outcomes. This study aims to provide an overview of applications of AI in cardiac surgery. Methods A systematic literature search on AI applications in cardiac surgery from inception to February 2024 was conducted. Articles were then filtered based on the inclusion and exclusion criteria and the risk of bias was assessed. Key findings were then summarized. Results A total of 81 studies were found that reported on AI applications in cardiac surgery. There is a rapid rise in studies since 2020. The most popular machine learning technique was random forest ( n = 48), followed by support vector machine ( n = 33), logistic regression ( n = 32), and eXtreme Gradient Boosting ( n = 31). Most of the studies were on adult patients, conducted in China, and involved procedures such as valvular surgery (24.7%), heart transplant (9.4%), coronary revascularization (11.8%), congenital heart disease surgery (3.5%), and aortic dissection repair (2.4%). Regarding evaluation outcomes, 35 studies examined the performance, 26 studies examined clinician outcomes, and 20 studies examined patient outcomes. Conclusion AI was mainly used to predict complications following cardiac surgeries and improve clinicians' decision‐making by providing better preoperative risk assessment, stratification, and prognostication. While the application of AI in cardiac surgery has greatly progressed in the last decade, further studies need to be conducted to verify accuracy and ensure safety before use in clinical practice.