Citations

Importance Primary brain and central nervous system cancer (collectively referred to as CNS cancer) comprises 2% of all human cancers and poses significant health and economic challenges in the United States. Objective To analyze CNS cancer burden in the US, stratified by time, location (state and division), sex, age group, and Sociodemographic Index (SDI). Design, Setting, and Participants This cross-sectional study involved a repeated analysis of Global Burden of Disease Study (GBD) 2021 data in 2024. Using data from 183 sources, CNS cancer metrics in the US were estimated across states and years. US CNS cancer metrics across all sexes and age groups were included in the GBD. Exposure CNS cancer diagnosis. Main Outcomes and Measures Overall and age-standardized estimates of the incidence, prevalence, mortality, disability-adjusted life-years (DALYs), years of life lost, and years lived with disability per 100 000 population, including 95% uncertainty intervals (UIs), and time trends. Results In 2021, for all age groups and sexes across the US, there were 31 780 incident cases (95% UI, 29971.1 to 32843.9). Age-standardized incidence, DALYs, and mortality rates per 100 000 population were 6.91 (95% UI, 6.58 to 7.12), 134.38 (95% UI, 129.83 to 137.95), and 4.1 (95% UI, 3.87 to 4.22), respectively. Despite no significant change observed in the overall incidence between 1990 and 2021, DALY and mortality rates decreased by 15.77% (95% UI, −17.75% to −13.68%) and 8.41% (95% UI, −11.09% to −6.22%), respectively. Substantial geographic variability was noted. Mississippi, Alabama, Kentucky, and Kansas (West North Central and East South Central divisions) and West Virginia faced persistently high burdens over the past 30 years. Sex differences were evident; disease burden was consistently higher in males compared with females. Age-specific estimates showed a bimodal distribution: the youngest group (<5 years) showed a significant decrease in incidence rate (−34.42% to −11.56%), whereas older age groups (>70 years) experienced increasing trends. DALYs and mortality rates were negatively correlated with SDI (ρ = −0.6860 and ρ = −0.6391; P < .001). Conclusions and Relevance These findings provide valuable insights into the CNS cancer burden across the US by age, sex, location, and SDI, enabling better public health status assessments, health care policy restructuring, and resource redistribution for improved care.

Background: Macromastia causes significant physical and psychological distress in adolescents. Although reduction mammaplasty alleviates these symptoms, the mental health impact of macromastia in younger populations remains underexplored. This systematic review and meta-analysis aimed to evaluate mental health and quality of life outcomes in adolescents/young women undergoing reduction mammaplasty. Methods: A systematic search of PubMed, Embase, and the Cochrane Central Register of Controlled Trials was conducted from inception to September 2024. Studies reporting mental health or quality of life outcomes through the BREAST-Q, Short Form-36, Rosenberg Self-Esteem Scale, and Eating Attitudes Test were included. Odds ratios and mean difference with 95% confidence intervals were pooled using a random-effects model. Heterogeneity was assessed with I 2 statistics. The Risk of Bias in Nonrandomized Studies of Interventions tool was used to assess study quality. Meta-analysis was performed on 4 BREAST-Q domains. Results: Of 701 articles identified, 8 observational studies involving 660 patients were included. Pooled BREAST-Q scores postsurgery showed significant improvements across all 4 domains. Improvement in Short Form-36, Rosenberg Self-Esteem Scale, and Eating Attitudes Test scores was noted in individual studies, though meta-analysis was limited by incomplete data. Conclusions: These results indicate that reduction mammaplasty significantly improves the physical, psychosocial, and sexual well-being of adolescents/young women with macromastia.

Background: Cardiomyopathy occurring during pregnancy or in the postpartum period is a leading case of maternal death in the US. The most commonly described form of pregnancy related cardiomyopathy (PRCM) associated with left ventricular systolic dysfunction is peripartum cardiomyopathy, defined as left ventricular ejection fraction (LVEF) <45% on echocardiography. PRCM symptoms often overlap with those of normal pregnancy and the lack of validated screening tools limits effective recognition, contributing to diagnostic delays. Recently, several artificial intelligence enabled electrocardiogram (AI-ECG) models have been developed and evaluated for PRCM detection. Aim&Hypothesis: We sought to investigate the pooled diagnostic performance of AI-ECG models for detecting PRCM. We hypothesized that AI-ECG models would be effective for detecting PRCM. Methods: A systematic search of PubMed, Scopus, and Cochrane was performed by combining search terms “artificial intelligence”, “machine learning”, “deep learning”, “algorithm”, “ECG”, “EKG”, “electrocardiogram”, “peripartum cardiomyopathy”, “pregnancy”, “pregnancy related cardiomyopathy” , to identify studies investigating the use of AI-ECG models for PRCM detection. Studies included those utilizing standard 12-lead ECGs as well as single lead ECGs from various devices. For inclusion in the meta-analysis, studies had to provide complete confusion matrix data for the AI-ECG model. Inverse variance random effects model meta-analysis was performed. Pooled performance estimates with corresponding 95% CIs are presented in forest plots and a summary receiver operating characteristics (sROC) curve. Results: Our search identified a total of 201 studies (PubMed: 64, Embase: 117, Cochrane: 20) from which 6 studies involving 11 AI-ECG models and 3,006 patients were included in the meta-analysis (Fig. 1) . Following random effect bivariate meta-analysis, the AI-ECG models yielded a pooled sensitivity of 0.721 (95% CI 0.676–0.762); specificity of 0.931 (95% CI 0.932–0.938); diagnostic odds ratio (DOR) of 44.658 (95% CI 23.843–83.644); and AUC of 0.896 (95% CI 0.87–0.91), for PRCM detection (Fig. 2A-C, 3) . Conclusion: AI-ECG models appear to be effective tools for detecting PRCM and may represent a viable solution to reduce diagnostic delays consequently improving maternal outcomes. Additional studies are needed to evaluate its impact on clinical outcomes in pragmatic settings.

Background: Acute myocardial infarction (AMI) represents a significant component of cardiovascular disease (CVD) mortality among the young and middle-aged populations of the United States (US). Research Question: To analyze the long-term trends and the impact of the COVID-19 pandemic on AMI-related mortality among the young and middle-aged populations of the US. Methods: Data from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) multiple causes of death database were used to analyze death certificates from 1999 to 2023 for AMI-related deaths among the young and middle-aged population (aged 25-64) of the US. Age-adjusted mortality rates (AAMRs) per 100,000 people and associated annual percent changes (APCs) and average APC (AAPCs) were analyzed using Joinpoint regression analysis. Mortality trends were stratified by sex, race/ethnicity, and census region. Results: From 1999 to 2023, there were 970,454 AMI-related deaths among U.S. adults aged 25-64 years. The annual number of AMI-related deaths decreased from 44,040 in 1999 to 31,522 in 2023. The overall AAMR per 100,000 decreased from 31.02 deaths (95% CI, 30.73 to 31.31) in 1999 to 15.29 (95% CI, 15.11 to 15.46) in 2023 (AAPC -2.92%, 95% CI -3.22 to -2.75). The AAMR per 100,000 declined at a faster rate from 31.02 in 1999 to 19.57 in 2010 (1999-2010, APC -4.26), followed by a slower rate from 19.57 in 2010 to 16.67 in 2019 (2010-2019, APC -1.41), The declining trend was disrupted by a transient increase during the COVID-19 pandemic, with a peak AAMR of 19.73 (95% CI, 19.53 to 19.93) in 2021 (2019-2021, APC 8.52). The declining trend resumed from 2021 to 2023, with AAMR decreasing to the lowest levels of 15.29 in 2023 (2021-2023, APC -12.58). Heterogeneity across demographic and regional groups has narrowed during these 25 years. However, they are still prevalent, with men ( figure 1 ), non-Hispanic (NH) Black or African American, NH American Indian or Alaska Native ( figure 2 ), and the residents of the Southern United States ( figure 3 ) having higher mortality rates. Conclusion: Over the past 25 years, AMI-related mortality has declined in the young and middle-aged population of the US, although rates spiked during the COVID-19 pandemic before resuming their decline in 2022. While disparities among demographic and regional groups have narrowed, they still exist, necessitating comprehensive efforts to improve cardiovascular health outcomes.

Objective To evaluate outcomes in patients with type 2 endoleak (T2E) and the impact of treatment outside the device instructions for use (IFU). Methods This retrospective single-center study included abdominal aortic aneurysms (AAA) patients who underwent EVAR (2011- 2024). Primary outcomes were 30-day and mid-term complications, reintervention, and mortality by T2E status. Propensity score matching was applied and secondary analysis stratified outcomes by IFU compliance, comparing within ( in IFU) or outside( out IFU) IFU in each T2E group. Results Among 154 EVAR patients (137 men (89.1%) and 17 women (11.0%); mean age 77.5 ± 7.6 years), 77 were T2E(−), and 77 were T2E(+). Baseline characteristics were similar. Thirty-day complications were comparable (T2E(−): 5, 6.4% vs T2E(+): 2, 2.56%, P = 0.72). The mean follow-up duration was 2.7 ± 2.4 years. Sac diameter change (Δ) decreased in the T2E(−) (7.7 ± 8.4 mm) and enlarged in the T2E(+) (1.79 ± 12.5 mm; P < 0.01). Mid-term graft-related complications (T2E(−): 3, 3.8% vs T2E(+): 49, 62.8%, P < 0.01) and reinterventions (T2E(−): 3, 3.8%, vs T2E(+): 20, 25.6% P < 0.01) were higher in the T2E(+) with no difference in mortality ( P = 1.00). These findings remained after excluding type 1 endoleaks. Among the T2E(+), those treated out IFU had longer hospital stays ( out IFU: 3.0 ± 2.8 days vs in IFU: 1.8 ± 2.0 days, P = 0.03), higher T1E ( out IFU: 3, 33.3% vs in IFU: 1, 4.34%, P < 0.01), higher rates of reintervention ( out IFU: 9, 100.0% vs in IFU:17; 24.6%, P < 0.01) and higher all-cause mortality than those within IFU criteria ( out IFU: 5, 55.0% vs in IFU: 13, 18.8%, P < 0.01). IFU status did not significantly affect outcomes in T2E(−) patients. Conclusions T2E is associated with sac growth and increased reinterventions, but not higher mortality. On subgroup analysis, those T2E(+) treated out IFU had longer hospitalizations, higher T1E rates, more reinterventions, and higher all-cause; but not aneurysmal-related mortality.

Treatment-induced ovarian function loss is a significant concern for many young patients with breast cancer. Accurately predicting this risk is crucial for counselling young patients and informing their fertility-related decision-making. However, current risk prediction models for treatment-related ovarian function loss have limitations. To provide a broader representation of patient cohorts and improve feature selection, we combined retrospective data from six datasets within the FoRECAsT (Infertility after Cancer Predictor) databank, including 2679 pre-menopausal women diagnosed with breast cancer. This combined dataset presented notable missingness, prompting us to employ cross imputation using the k-nearest neighbours (KNN) machine learning (ML) algorithm. Employing Lasso regression, we developed an ML model to forecast the risk of treatment-related amenorrhea as a surrogate marker of ovarian function loss at 12 months after starting chemotherapy. Our model identified 20 variables significantly associated with risk of developing amenorrhea. Internal validation resulted in an area under the receiver operating characteristic curve (AUC) of 0.820 (95% CI: 0.817–0.823), while external validation with another dataset demonstrated an AUC of 0.743 (95% CI: 0.666–0.818). A cutoff of 0.20 was chosen to achieve higher sensitivity in validation, as false negatives—patients incorrectly classified as likely to regain menses—could miss timely opportunities for fertility preservation if desired. At this threshold, internal validation yielded sensitivity and precision rates of 91.3% and 61.7%, respectively, while external validation showed 92.9% and 60.0%. Leveraging ML methodologies, we not only devised a model for personalised risk prediction of amenorrhea, demonstrating substantial enhancements over existing models but also showcased a robust framework for maximally harnessing available data sources.

ABSTRACT Suicidality contributes substantially to global morbidity and mortality, yet despite its heritability, its biological etiology remains largely elusive. We conducted multi-ancestry genome-wide association study meta-analyses of suicidal ideation (259,747 cases), suicide attempt (64,993 cases), suicide death (9,197 cases), and suicidal behavior (suicide attempt/ death, 75,300 cases), across 54 cohorts (e.g., the Psychiatric Genomics Consortium, Million Veteran Program, UK Biobank). We identified 77 significant loci across meta-analyses, including 59 previously unreported for suicidality. SNP-based heritability ranged from 2.0-6.7% and there were strong, yet incomplete, genetic correlations between suicidality phenotypes (0.70-0.88). Fine-mapping prioritized putative causal SNPs and 20 credible genes. Enrichment analyses implicated synaptic pathways and neuronal populations predominantly in subcortical brain regions (e.g., amygdala excitatory, medium spiny, hippocampal CA1-3). Together, these findings establish suicidality as a polygenic set of traits with both shared and distinct genetic influences, providing a foundation for future studies of suicide biology and etiology.