Abstract Background The clinical importance of right ventricular (RV) - pulmonary arterial (PA) coupling has been reported in various cardiac diseases such as right heart failure, pulmonary hypertension, and valvular dysfunction. However, there is limited data on patients with non-valvular atrial fibrillation (AF). We aim to investigate whether RV-PA uncoupling affects the clinical outcome in patients with non-valvular AF. Methods A total of 807 consecutive patients with non-valvular AF who underwent echocardiography (mean age 70 ± 12 years, 39% female) were included. We excluded patients with significant (at least moderate degree) valvular diseases, left ventricular ejection fraction (LVEF) <50%, cardiac implantable electronic devices, previous cardiac surgery, and poor echocardiographic image for RV analysis. RV-PA coupling was assessed by the ratio of RV global longitudinal strain (GLS) to PA systolic pressure (PASP), and decreased RV-PA coupling was defined as less than 0.52 %/mmHg based on on the receiver operating characteristic analysis. The primary outcome was the composite of all-cause death and hospitalization for heart failure. Results Over a mean of 4.7 ± 3.5 years of follow-up, 178 (22%) patients had experienced primary outcomes. Among them, 107 (13%) patients were hospitalized for HF and 90 (11%) patients died. Patients with decreased RV-PA coupling were older (73 ± 11 vs. 68 ± 12 years, p<0.001), had a higher prevalence of females (51 vs. 33%, p<0.001) and diabetes (33 vs. 23%, p=0.003), lower left ventricular ejection fraction (60 ± 5 vs. 61 ± 5%, p=0.011), higher E/e’ (14 ± 5 vs. 11 ± 4, p<0.001), larger left atrial volume index (78 ± 37 vs. 62 ± 22 ml/m2, p<0.001) and larger RV end-systolic area (11 ± 4 vs. 10 ± 3 cm2/m2, p=0.001) compared to those with preserved RV-PA coupling. In multivariate Cox regression analysis, decreased RV-PA coupling was associated with worse clinical outcomes even after adjusting for age, diabetes, E/e’, and RV end-systolic area (Hazard ratio [HR] 1.50, 95% Confidence interval [CI] 1.09 – 2.07, p=0.014) (Figure 1). Conclusions In patients with non-valvular AF, RV-PA coupling was associated with clinical outcome, suggesting that it may provide additional information for predicting future cardiovascular risk in this population.

In this cohort study of young and middle-aged Korean women, at low risks for CVD mortality, those with HR-HPV infection had higher death rates from CVD, specifically ASCVD and IHD, with a more pronounced trend in obese individuals.

Abstract Background The clinical importance of right ventricular (RV) - pulmonary arterial (PA) coupling has been reported in various cardiac diseases such as right heart failure, pulmonary hypertension, and valvular dysfunction. However, there is limited data on patients with non-valvular atrial fibrillation (AF). We aim to investigate whether RV-PA uncoupling affects the clinical outcome in patients with non-valvular AF. Methods A total of 807 consecutive patients with non-valvular AF who underwent echocardiography (mean age 70 ± 12 years, 39% female) were included. We excluded patients with significant (at least moderate degree) valvular diseases, left ventricular ejection fraction (LVEF) <50%, cardiac implantable electronic devices, previous cardiac surgery, and poor echocardiographic image for RV analysis. RV-PA coupling was assessed by the ratio of RV global longitudinal strain (GLS) to PA systolic pressure (PASP), and decreased RV-PA coupling was defined as less than 0.52 %/mmHg based on on the receiver operating characteristic analysis. The primary outcome was the composite of all-cause death and hospitalization for heart failure. Results Over a mean of 4.7 ± 3.5 years of follow-up, 178 (22%) patients had experienced primary outcomes. Among them, 107 (13%) patients were hospitalized for HF and 90 (11%) patients died. Patients with decreased RV-PA coupling were older (73 ± 11 vs. 68 ± 12 years, p<0.001), had a higher prevalence of females (51 vs. 33%, p<0.001) and diabetes (33 vs. 23%, p=0.003), lower left ventricular ejection fraction (60 ± 5 vs. 61 ± 5%, p=0.011), higher E/e’ (14 ± 5 vs. 11 ± 4, p<0.001), larger left atrial volume index (78 ± 37 vs. 62 ± 22 ml/m2, p<0.001) and larger RV end-systolic area (11 ± 4 vs. 10 ± 3 cm2/m2, p=0.001) compared to those with preserved RV-PA coupling. In multivariate Cox regression analysis, decreased RV-PA coupling was associated with worse clinical outcomes even after adjusting for age, diabetes, E/e’, and RV end-systolic area (Hazard ratio [HR] 1.50, 95% Confidence interval [CI] 1.09 – 2.07, p=0.014) (Figure 1). Conclusions In patients with non-valvular AF, RV-PA coupling was associated with clinical outcome, suggesting that it may provide additional information for predicting future cardiovascular risk in this population.

In this cohort study of young and middle-aged Korean women, at low risks for CVD mortality, those with HR-HPV infection had higher death rates from CVD, specifically ASCVD and IHD, with a more pronounced trend in obese individuals.

10533 Background: Accurate risk assessment allows the precise personalized screening of breast cancer. Conventional statistical risk models have been used that estimate the probability of breast cancer incidence with patient demographics, detailed personal and family history. The purpose of this study was to investigate the feasibility of the AI-powered Imaging Biomarker in mammography (IBM) which was developed to predict the risk of future breast cancer beyond the mammographic breast density. Methods: We trained and developed the deep learning risk model, the AI-powered IBM, using a total of 36,955 examinations from 21,438 patients, who underwent at least one mammogram using Hologic or Siemens machines and pathology-confirmed breast cancer outcomes. To discover the feasibility of the AI-powered IBM, mammograms and various clinical information including pathology-confirmed breast cancer outcomes were collected, which were only used for external validation. C-indices and receiver operating characteristic (ROC) curves for 1- to 5-year outcomes were obtained. We compared 5-year ROC area under the curves (AUCs) of our AI-powered IBM and statistical risk models including the Tyrer-Cuzick model and the Gail model, which were most commonly used and widely known, using DeLong’s test. Results: A total of 16,894 mammograms were collected for external validation, of which 4,002 were followed by a cancer diagnosis within 5 years. Our AI-powered IBM obtained C-index of 0.758, and the model demonstrated the risk of breast cancer with AUC of 0.895 (95% CI: 0.880, 0.909) at 1-year, 0.839 (0.824, 0.852) at 2-year, 0.807 (0.794, 0.819) at 3-year, 0.783 (0.7947, 0.819) at 4-year. The 5-year AUC of our AI-powered IBM was 0.808 (0.792, 0.822). Our AI-powered IBM showed significantly higher 5-year AUC than the Gail model (AUC: 0.572, P< 0.001) and the Tyrer-Cuzick model (0.569, P< 0.001). Conclusions: A deep learning AI-powered IBM using mammograms has a substantial potential to advance toward the robust risk prediction of breast cancer over conventional risk models. This approach for risk stratification of breast cancer might be feasible to improve personalized screening programs.

<b>Background/Objective</b>: Existing abbreviated Geriatric Depression Scales (GDSs), derived via Classical Test Theory (CTT), often sacrifice accuracy for brevity and retain non-specific items. We aimed to develop a minimum-item GDS maintaining diagnostic performance equivalent to the full 30-item scale (GDS30) using Item Response Theory (IRT). <b>Methods</b>: This cross-sectional study employed rigorous 5:5 split-sample cross-validation. Participants included 6525 older adults (aged ≥60 years) from community-based (Korean Longitudinal Study on Cognitive Aging and Dementia) and clinical settings (geropsychiatry clinic). Depression was diagnosed through standardized clinical interviews based on DSM-IV criteria. Two-parameter logistic IRT models estimated item discrimination and difficulty parameters. Sequential item reduction with DeLong tests identified the minimum number of items required to maintain GDS30-equivalent area under the curve (AUC). <b>Results</b>: The 10-item IRT-optimized scale (GDS10-IRT) achieved an AUC of 0.856 (95% CI: 0.809-0.895) in the validation set, showing no significant difference from GDS30 (AUC = 0.883; <i>p</i> = 0.396). Conversely, the 15-item GDS (GDS15) demonstrated significantly lower AUC than GDS30 (<i>p</i> < 0.001) despite having more items. GDS10-IRT achieved a 234% improvement in efficiency ratio (AUC/items) over GDS30. Notably, Item 16 ("feeling downhearted and blue"), identified as the most discriminating symptom (a = 2.53), is absent from the GDS15 but included in GDS10-IRT. <b>Conclusions</b>: IRT-based item selection achieves GDS30-equivalent diagnostic accuracy with only 10 items, outperforming the widely used GDS15. By recovering high-discrimination items excluded by CTT, the GDS10-IRT offers a more efficient, specific screening tool for late-life depression.

Metabolic health and nonobesity independently and favorably impact EE remission. Maintaining normal weight and healthy metabolic status may contribute to EE remission.

Fractions of cancer cases and mortality due to infectious agents are projected to decrease from 2015 to 2030. There should be adequate intervention from authorities to control <i>H. pylori</i>, HBV, and HPV and to reduce the cancer burden.

<b>Background/Objectives:</b> The risk of sarcopenic obesity across menopausal transition stages remains unclear. This study investigated the association between menopausal stage and the risk of sarcopenic obesity. <b>Methods:</b> This longitudinal study included 4766 Korean women aged 42-52 years (median follow-up duration, 9.1 years). Menopausal transition was classified by STRAW + 10 stages: premenopause, early transition, late transition, and postmenopause. Sarcopenic obesity was defined as ASM index < 5.7 kg/m² combined with PBF ≥ 35%. ASM and PBF were measured by bioelectrical impedance analysis with an InBody 720 device. Obesity, defined by BMI or waist circumference, was used for the sensitivity analyses. Associations were analyzed using generalized estimating equations. <b>Results:</b> Participants had a mean age and BMI of 42.2 years (SD 3.0) and 22.1 kg/m² (SD 2.9), respectively; 16.1% had high body fat, and 15.6% had low muscle mass. Increased age was associated with higher odds of sarcopenic obesity (OR 1.07, 95% CI: 1.03-1.10). Compared to pre-menopause, late transition (OR 1.49, 95% CI: 1.17-1.89) and post-menopause (OR 1.67, 95% CI: 1.26-2.23) were significantly associated with sarcopenic obesity, independent of confounders. Similar trends were observed using waist circumference-based definitions. No significant associations were identified using BMI-based definitions (BMI ≥ 23 kg/m²), although positive trends were observed. <b>Conclusions:</b> In middle-aged Korean women undergoing natural menopause, sarcopenic obesity increased across menopausal stages, becoming significant from the late transition. These unfavorable changes were more evident when using body composition measures, suggesting that BMI alone may underestimate such changes during menopausal transition.