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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.

Abstract Disclosure: H. Ahmad: None. M. Kakakhel: None. A. Ullah: None. M. Saeed: None. J. Ikram: None. A. Qadeer: None. M. Khawar: None. H. Jawad: None. S. Haider: None. R. Haider: None. F. Ahmed: None. R.W. Kirchoff: None. Background: Type 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by the destruction of insulin-producing cells in the pancreas. It is a global health concern with regional variation in incidence and prevalence. This study aims to analyze the regional incidence and prevalence of T1DM among adolescents and young adults (15-39 years) from 1990 to 2021 in 26 regions. Methods: Global Burden of Disease (GBD) data for 2021 were analyzed to assess regional incidence and prevalence of T1DM among adolescents and young adults. The Annual Average Percentage Change (AAPC) was calculated to evaluate trends from 1990 to 2021, with statistical significance determined using p-values. Results: In 2021, middle socio-demographic index (SDI) has the highest incidence (54541) as well as prevalence (1.86 million), followed by Low-middle SDI (I: 48416, P: 1.80 million) and South Asia (I: 46310, P: 1.73 million). Oceania has the lowest incidence (356) as well as prevalence (12885), followed by Andean Latin America (I: 911, P: 31954) and Caribbean regions (I: 1214, P: 54610), respectively. The largest increase in AAPC of incidence was showed by Southern Latin America (2.23, CI: 2.11-2.30), High-income Asia Pacific (2.22, CI: 2.18-2.26), and Western Europe (1.80, CI: 1.73-1.84) regions, while Western Europe (1.72, CI: 1.66-1.75), Eastern Europe (1.64, CI: 1.63-1.66), and Australasia (1.38, CI: 1.36-1.40) showed largest increase in AAPC of prevalence. The Caribbean region had the greatest decline in AAPC of prevalence (-0.11, CI: -0.08 to -0.12) followed by Eastern Sub-Saharan Africa (-0.07, CI: -0.06 to -0.09) and Central Latin America (-0.04, CI: -0.02 to -0.07). There was no region with decline in incidence. All AAPC values were statistically significant (p &lt; 0.000001). Conclusion: Middle SDI region exhibited the highest incidence and prevalence, highlighting the vulnerability of these populations, while Oceania and Andean Latin America bore the least burden. Notably, several regions, particularly in Europe, experienced substantial increases in both incidence and prevalence, emphasizing the need for targeted interventions. Conversely, some regions, such as the Caribbean, showed encouraging declines in prevalence, suggesting that effective prevention and control strategies can be implemented. These findings underscore the importance of tailored approaches to address the evolving burden of T1DM. Presentation: Saturday, July 12, 2025

Abstract Disclosure: M. Kakakhel: None. S. Fatima: None. H. Ahmad: None. A. Ibrahim: None. W. Rahman: None. R. Haider: None. H. Jawad: None. A. Qadeer: None. M. Khawar: None. F. Ahmed: None. J. Ikram: None. R. Kirchoff: None. Background: Across the world, type 1 diabetes mellitus (T1DM) is a major health problem for teenagers and young people. This study aims to examine the incidence and prevalence of T1DM by gender in the 15-39 age range between 1990 and 2021. Methods: We analyzed data from the Global Burden of Disease (GBD) to evaluate incidence and prevalence rates of T1DM in each gender from 1990 to 2021. The Annual Percentage Change (APC) and The Annual Average Percentage Change (AAPC) was used to assess trends, with p-values determining the statistical significance. Results: From 1990 to 2021, the global prevalence of T1DM was found to be increased steadily for both genders, with the highest rates recorded in 2021: 247.32 per 100,000 for females and 246.47 per 100,000 for males. Incidence rates in 2021 showed a contrasting pattern, with males exhibiting higher rates (7.18 per 100,000) compared to females (5.98 per 100,000). AAPC analysis showed that males exhibit a faster rise in both prevalence and incidence compared to females over the study period. The difference in AAPC between males and females is more pronounced for prevalence (+0.3748%) compared to incidence (+0.1956%), with all AAPC values being statistically significant (p &lt; 0.000001). Following the APC analysis, other significant trends were revealed. Male incidence experienced the greatest increase during 2015-2021, with an APC of +1.9033% (p &lt; 0.000001), whereas female incidence increased most between 2011 and 2014 (APC = +1.3050%; p &lt; 0.000001). In contrast, the most significant decline in incidence was observed in females during 1990-1992, with an APC of +0.1665% (p = 0.006799), which was the lowest increase in the dataset. For prevalence, males exhibited the highest increase during 2015-2021, with an APC of +1.3938% (p &lt; 0.000001), whereas females had their peak increase during 2006-2009, with an APC of +0.8957% (p &lt; 0.000001). The slowest increase in prevalence for females occurred during 2014-2019, with an APC of +0.4591% (p &lt; 0.000001). Conclusion: The prevalence and incidence of T1DM among people aged 15-39 increased significantly between 1990 and 2021, with a greater increase in males. This highlights the need for gender-specific strategies in T1DM prevention and management, and research into the factors driving these disparities. Presentation: Sunday, July 13, 2025

Abstract Disclosure: M. Kakakhel: None. S. Fatima: None. H. Ahmad: None. M. Khan: None. A. Rai: None. A. Qadeer: None. R. Haider: None. M. Khawar: None. U. Habib: None. H. Jawad: None. J. Ikram: None. F. Ahmed: None. R. Kirchoff: None. Introduction: Type 1 Diabetes Mellitus (T1DM) is a disease that affects people all over the world. This condition causes significant morbidity, as measured by Disability-Adjusted Life Years (DALYs) and mortality rates, and its severity varies worldwide. The purpose of this study is to examine trends in T1DM-related death rates and DALYs across 26 geographical areas from 1990 to 2021 in order to provide a current picture of the disease's consequences and identify regional differences. Methods: This study examined DALYs and mortality rates per 100,000 people in individuals aged 15-39 years using data from the Global Burden of Disease Study from 1990 to 2021. Trends were assessed using the Average Annual Percentage Change (AAPC), with p-values used to define statistical significance. The study examined 26 regions with varied socioeconomic development indices (SDI). Using confidence intervals to account for data uncertainty, DALYs and mortality were examined to investigate the disease burden across time. Results: In 1990, the Middle SDI region had the highest DALYs at 298,352 (95% CI: 256,144 -349,670), which reached 382,857 (95% CI: 213,704-317,664) by 2021 (AAPC = 0.15, P-value &lt; 0.000001). The region with the highest DALYs in 2021 was the Low-Middle SDI, with 419,772 (95% CI: 344,307- 495,496) cases (AAPC = -2.93, P-value = &lt;0.000001). From 1990 to 2021, Oceania consistently had the lowest DALYs of 1,895 (95% CI: 1,264-2,577) and 4,210(95% CI: 3,011-5,724), respectively (AAPC= 0.15, P-value = 0.0004). Mortality rates in 1990 were highest in the Middle SDI area with 3,758 deaths (95% CI: 3,343-4,332) (AAPC= -1.23, P-value = &lt;0.000001), but by 2021, South Asia had the highest mortality rate at 5,189(95% CI: 4,272-6,087) (AAPC= -0.49). Oceania had the lowest mortality rate of 25 deaths (95% CI 145-35) (AAPC = 0.193, P-value = &lt;0.0004) in 1990, whereas Australasia had the lowest mortality rate of 28 deaths (95% CI 26-29) in 2021 (AAPC = -0.93, P-value = &lt;0.000001). Conclusion: From 1990 to 2021, the global burden of type 1 diabetes—as indicated by DALYs and death rates—underwent a substantial shift, with significant rises in several regions. DALYs were highest in the Middle SDI region in 1990 but have migrated to the Low-Middle SDI region by 2021, reflecting shifting healthcare issues and demographics. Mortality rates followed a similar pattern, with South Asia and the Low-Middle SDI regions experiencing the highest rates in 2021. Over both time periods, however, Oceania and Australasia consistently had the lowest DALYs and death rates. These findings emphasise the critical need for targeted interventions in high-burden areas. Addressing these differences is crucial for decreasing the global burden of T1DM. Presentation: Sunday, July 13, 2025