Citations

Abstract Diffuse leptomeningeal glioneuronal tumors (DLGNT) are rare central nervous system tumors. Our understanding of the clinical course, prognostic factors, and optimal treatment strategies remains limited. Histologic, molecular, and clinical data were collected and analyzed for 25 patients with DLGNT reviewed at St. Jude Children’s Research Hospital. The median age at diagnosis was 6 years (11 months-20 years). Nineteen (72%) patients were male. The disease was localized at diagnosis in 12 (48.0%) patients, with most tumors localized in the spinal cord. BRAF fusion was present in 22 (95.6%) of 23 patients who had material for testing. Of 18 patients who had tissue for DNA methylation profiling, 3 (16.7%) matched to DLGNT MC-1, 3 (16.7%) to DLGNT MC-2, and 12 (66.7%) did not match to a DLGNT subclass. Median follow-up was 63 months. Twenty-four (96.0%) patients received adjuvant therapy after initial diagnosis or at progression. Thirty-five cytotoxic chemotherapy and 24 single-agent targeted therapy regimens were used in 22 and 13 patients, respectively. With targeted therapy, 3 (12%) patients had partial responses. Eleven (44.0%) received radiotherapy. Median progression-free survival (PFS) after a patient’s first chemotherapy, targeted therapy, or radiotherapy regimen was 44 months (1-77), 11 months (4-39), and 16.5 months (9-23), respectively. The 5-year PFS was 18.7%±9.2, while the 5-year overall survival (OS) was 82.4%±9.2. The 5-year OS was lower in patients older than 9 years at diagnosis (60.0%±21.9 vs 91.7%±8.0, p = 0.02). Ki67 >4% was associated with worse PFS (0%±0 vs 24.6%±11.9 at 5 years, p = 0.03). Initial disease dissemination (localized vs. disseminated) and 1q gain status do not influence PFS or OS. PFS for DLGNT remains poor across all treatment modalities, including targeted therapies, despite the high frequency of BRAF fusions. Due to its rarity, establishing optimal therapeutic strategies for DLGNT requires additional treatment and outcome data and prospective multi-institutional trials.

Abstract Objective To investigate the ability of artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output and delta age, defined as AI‐estimated age minus chronological age, to predict adverse vascular outcomes in patients with migraine. Background Migraine, especially migraine with aura, is associated with an increased risk of cardiovascular events. Using normal sinus rhythm ECG, our institution has developed AI‐ECG models that predict the probability of AF and estimate patients' age, and from that, delta age (estimated age minus actual age) has been shown to reflect cardiovascular health and physiologic aging. Methods We conducted a retrospective cohort study that included adult patients with migraine who had at least one digital, standard 12‐lead ECG from 2000 to 2020 at any Mayo Clinic site. The first normal sinus rhythm ECG was defined as the index ECG, and those with adverse vascular events or AF prior to, or within 3 days after, index ECG were excluded. AI‐ECG AF prediction model output and delta age were obtained from the index ECG. The primary outcome was a composite endpoint of adverse vascular events that included acute myocardial infarction, acute ischemic stroke, venous thromboembolism, and death. New‐onset AF was also recorded as a secondary outcome. Results A total of 29,928 patients were included. The mean age was 44.3 ± 14.5 years, and the median follow‐up time was 54 months. During the study period, 4662 (15.6%) patients had adverse vascular events, and 1384 (4.6%) patients developed new‐onset AF. A higher AF prediction model output was associated with a higher risk of adverse vascular outcomes (adjusted hazard ratio [aHR]:1.15 [95% confidence interval: 1.12 to 1.18] for every 10% increase in prediction model output), as was a larger delta age (aHR:1.16 [1.12 to 1.21] for every 10‐year increase in delta age). Moreover, AI‐ECG AF prediction model outputs of ≥1% (aHR: 1.45 [1.36 to 1.54]) and delta age ≥−1 years (aHR: 1.09 [1.02 to 1.16]) were found to be the optimal cutoffs to dichotomize patients into high‐risk and low‐risk groups for adverse vascular events. Higher AI‐ECG AF prediction model output at baseline also predicted a higher likelihood of having new‐onset AF during the study period (aHR: 1.31 [1.26 to 1.37]), and the development of new‐onset AF was associated with a higher risk of adverse vascular events (aHR: 2.43 [2.17 to 2.73]). Conclusions For patients with migraine, a higher AI‐ECG AF prediction model output and delta age are predictive of adverse vascular outcomes. A higher AF prediction model output is also predictive of future AF development. Furthermore, we identified the cutoffs for both AI‐ECG models that can distinguish patients between high‐ and low‐risk groups. Our results showed that AI‐ECG models could inform clinical practice and identify patients with migraine at risk for future adverse vascular events.

BACKGROUND AND OBJECTIVES: Surgeon-team familiarity is a critical factor that affects surgical outcomes. The relationships between familiarity and surgical outcomes are varied, and an operational definition of this measure remains underdeveloped. This study creates an effective time-based measure of surgeon-team familiarity and assesses its association with clinical and operational outcomes for neurosurgery. METHODS: Retrospective analysis of 9049 neurosurgery cases performed at 2 campuses of a tertiary academic medical center over a 3-year timeframe was conducted. This study measures normalized case duration as a proxy for operational performance and 30-day postoperative complications for clinical outcomes. Multivariate linear regression and logistic regression were used to analyze relationships between surgeon-team familiarity and surgical outcomes, with risk factors as control variables for case mix. RESULTS: For normalized case duration, the coefficient of surgeon-team familiarity is negative (β = −0.015, P = .03), but the squared term exhibits a positive relationship (β = 0.049, P = .003). The overall regression is statistically significant ( R 2 = 0.046, adjusted R 2 = 0.045, P < .001). The logistic regression shows that surgeon-team familiarity scores are negatively correlated (odds ratio = 0.356, 95% CI [0.126, 0.987]) with the risks of postoperative complications. It is statistically significant and explains between 2.4% (Cox & Snell R Square) and 6.0% (Nagelkerke R Square) of the variance for postoperative complications. CONCLUSION: This study found that surgeon-team familiarity is positively associated with neurosurgery outcomes. Teams familiar with the surgeons will likely improve operational performance and reduce the risk of postoperative complications, whereas too much familiarity may lengthen the case duration. Surgical practice leaders can use electronic health record data to define appropriate familiarity scores and incorporate key metrics when developing surgical team assignments for optimal operating room performance.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Small Cell Lung Cancer provide recommendations for diagnostic workup, staging, and treatment. These NCCN Guidelines Insights highlight recent updates, with a particular focus on changes to systemic therapies and radiation treatment in the NCCN Guidelines for Small Cell Lung Cancer.

Abstract Background Gastrointestinal (GI) cancer remains a major global health challenge. This study provides updated 2022 estimates of GI cancer incidence and mortality worldwide, along with projections of incident cases and deaths through 2050 to inform cancer control strategies. Methods In this population‐based study, data from the GLOBOCAN 2022 database were used to examine incidence and deaths associated with GI cancer. By using global demographic projections, this study estimated the projected number of incident cases and deaths until the year 2050. Results By 2050, the global burden of GI cancer is projected to reach 9.06 million incident cases (+85%) and 6.42 million deaths (+93%) from 2022. Increases in incidence are projected for both females (+87%) and males (+83%), as are increases in mortality for both females (+97%) and males (+91%). Whereas the Western Pacific region will have the highest absolute burden, with 3.88 million new cases and 2.79 million deaths, the greatest relative increase is expected in Africa, with incidence and deaths rising by 157% and 160%, respectively. As measured by the Human Development Index (HDI), the steepest increases are projected in low (+151% incidence; +152% deaths) and medium (+112% incidence; +114% deaths) HDI countries. Overall, all GI cancer types are projected to increase, led by pancreatic cancer in incidence (+95%) and colorectal cancer in deaths (+103%). Conclusions GI cancer incidence and deaths are expected to nearly double by 2050, with increases across sexes, cancer types, and regions, especially in lower HDI countries.

Background Many guidelines list migraine with aura (MwA) as a contraindication to estrogen-containing combined hormonal contraceptives (CHCs) due to vascular risks. However, current evidence is based on small sample studies with potential influence by confounding factors. Additionally, few studies have examined the vascular risk associated with modern CHCs with lower dose estrogen, particularly in relation to aura status. The present study aims to investigate the vascular risk of modern CHCs in women with migraine with and without aura. Methods We used a de-identified electronic medical record database with 120 million patients across multiple health systems in the United States of America. We included female patients aged 18–45 years who received a migraine diagnosis code, had at least three office visits within three years, and were prescribed at least one migraine-specific medication within 6 months following the first outpatient visit. All data after 2010 were included. Patients with prior cardiovascular events were excluded. Our composite endpoint consisted of acute ischemic stroke, acute myocardial infarction, deep vein thrombosis/pulmonary embolism and intravenous thrombolytic administration. We stratified our analysis according to CHC exposure and aura status and compared the incidence of the endpoint with high-dimensional propensity score-matching between CHC users and non-users in (i) the overall cohort, (ii) MwA and (iii) migraine without aura (MwoA); between MwA and MwoA in (iv) patients prescribed CHCs; and in (v) those without CHC prescriptions. Results We included 5535 patients who received CHC prescriptions and 21,520 who did not. 114 (2.06%) of CHC users and 547 (2.54%) of CHC non-users had at least one vascular event. With propensity score-matched comparison, the composite endpoint did not significantly differ between CHC and non-CHC in the overall migraine group, those with MwA and the MwoA group. In those prescribed CHC, MwA and MwoA did not differ in all the outcomes. For CHC non-users, MwA was associated with a higher incidence of acute ischemic stroke (hazard ratio = 2.45; 95% confidence interval = 1.58–3.78; p < 0.001; n = 6201 in each group) and the composite endpoint (hazard ratio = 1.34; 95% confidence interval = 1.08–1.67; p = 0.008). Conclusions Our real-world study showed that exposure to modern CHC was not associated with a significant increase in vascular risk in women aged 18–45 years with migraine, MwA or MwoA who have no prior cardiovascular events. However, in those who never received CHC, MwA was associated with higher vascular risks compared to MwoA. While limitations exist using large scale electronic medical record databases for analysis, our results suggest that carefully designed prospective studies should be conducted to reassess the vascular risk associated with CHC use in women with migraine, especially MwA.

Background Migraine is a highly prevalent neurological disorder with many treatment options, both pharmacological and non-pharmacological. Artificial intelligence (AI) has great potential to optimize treatment selection strategies for individual patients. This review provides an overview of AI models and the techniques used to predict migraine treatment outcomes. Methods We conducted a literature search in PubMed and examined studies that reported employing AI models to predict migraine preventive and acute treatment outcomes. We also explored incorporating AI/machine learning to enhance personalized migraine treatment strategies, including forecasting migraine attacks. Additionally, we summarized future research directions, including incorporating multimodality data, using AI frameworks for the discovery of novel treatment targets, and advancing the field with innovative AI techniques such as digital twins, conversational AI and virtual AI agents. Results Studies have employed ML and deep learning on a combination of clinical features and imaging data to predict acute or preventive migraine treatment outcomes with reported success. Continued model optimization, validation, and prospective assessment of the clinical utility of deploying ML models in real-world settings are crucial. Conclusions While AI has demonstrated success in predicting migraine treatment responses, future research incorporating novel AI techniques and diverse data sources could pave the way to advance personalized migraine treatment.

Immune checkpoint inhibitors (ICIs) are an important class of cancer treatment. Endocrine immune-related adverse events (E-irAEs) account for a significant proportion of irAEs in ICI-treated patients. Diagnosing E-irAEs accurately and timely can be challenging in the absence of clear categorization and standardization across oncology, endocrinology and other specialties. While existing guidelines provide some broad directions to diagnosis and management, they lack clarity on irAE-specific symptom evaluation and work-up, as well as assessment of severity. These limitations can then impact triage, time-sensitive management and care escalation to specialists such as oncoendocrinologists. The objective of this study was to develop consensus-based statements on disease definitions for E-irAEs. A core working group of endocrinologists with expertise in the oncoendocrinology field drafted a survey with statements outlining the general approach to E-irAEs, disease definition and management approach of six specific diagnoses: ir-thyroiditis thyrotoxic phase, ir-thyroiditis hypothyroidism, ir-Graves’ disease, ir-hypophysitis, ir-adrenalitis and ir-type 1 diabetes mellitus. Severity grading tables were drafted for three disease categories—“thyroid”, “pituitary and adrenal”, and “diabetes mellitus”. A two-round modified Delphi process, using the RAND/University of California Los Angeles (RAND/UCLA) Appropriateness Method, was employed. In this process, a 25-member voting panel consisting of endocrinologists, oncologists and other specialists and healthcare providers with experience in management of ICI-treated patients and irAEs was recruited. The panel rated anonymously on usability, accuracy, appropriateness or agreement of 41 items on a 9-point scale in Survey 1, after which a meeting was convened. Statements were modified based on voting results from Survey 1 and Meeting 1, and the process was repeated in Survey 2 and Meeting 2. At the end of this process, consensus was achieved for all statements. Our study findings address the gap in standardized nomenclature, clinical, laboratory and radiological evaluations, and management principles of E-irAEs. With consensus achieved from a panel of experts from a variety of disciplines, we anticipate that the statements can form the basis for standardization of the diagnostic process and improvement of patient care.

ABSTRACT Background The available risk prediction models are inadequate to identify a true low‐risk patient for developing new‐onset atrial fibrillation (AF) after typical atrial flutter (AFL) ablation. We aimed to determine whether adding markers of atrial myopathy to HATCH score ( h ypertension, a ge ≥75 years, t ransient ischemic attack/stroke, c hronic obstructive pulmonary disease, and h eart failure) can improve prediction of new‐onset AF after ablation of typical AFL. Methods The study included 208 consecutive patients who underwent successful ablation of typical AFL at Minneapolis VA Medical Center and University of Minnesota Medical Center. Patients with history of AF prior to ablation were excluded. Results Among the 208 patients, 76 (36.5%) developed new‐onset AF post AFL ablation. Mean follow‐up duration was 62 ± 31.8 months. HATCH score was not associated with new‐onset AF. When adding atrial myopathy (presence of at least one of the following: PTFV 1 > 5000 µV*ms, interatrial block determined by biphasic inferior p wave with duration >120 ms, left atrium (LA) diameter ≥44 mm, or LA index ≥3 cm/m 2 ) to HATCH score (HATCH‐A 2 ), the combination was independently associated with new‐onset AF. The AF incidence between HATCH‐A 2 score of 0–1 and ≥2 were 6.7% and 40.5%, respectively (Odds ratio 9.04, 95% confidence interval: 2.05–39.81, p = 0.004). Particularly, when HATCH‐A 2 score was 0, none of the patients developed AF. Conclusions Adding atrial myopathy to HATCH score improved predictability and could be used to delineate a true low‐risk patient of new‐onset AF after typical AFL ablation.