Emily S. Lau

Background: The PREVENT-HF equations estimate 10-year risk of overall heart failure (HF), inclusive of both HF with preserved and reduced ejection fraction (HFpEF; HFrEF). Given differential risk factor profiles, pathophysiology, and therapeutic strategies for each HF subtype, we sought to examine the performance of PREVENT-HF for subtype-specific risk assessment. Methods: We included data from five pooled longitudinal cohort studies (baseline exam 1989 to 2013) and two longitudinal primary-care electronic health record-based samples (Beth Israel Deaconess Medical Center [BIDMC], baseline 2004 to 2014; Mass General Brigham [MGB], baseline 2001 to 2018). We assessed the performance of the PREVENT-HF equations in predicting 10-year incident HFrEF and HFpEF, including model discrimination (area under the receiver operating characteristic curve [AUROC]; average precision [AP]/event rate) and calibration (integrated calibration index [ICI]), in adults aged 30-79 years without prevalent cardiovascular disease or HF. Results: The pooled cohort studies included 23,652 (55% women, age 60 ± 13 years), of whom 1,232 developed HF (432 [35%] HFpEF, 522 [42%] HFrEF). The BIDMC sample included 29,815 (55% women, 50 ± 13 years), of whom 259 (1%) developed HF (133 [51%] HFpEF, 101 [39%] HFrEF). MGB included 138,999 (62% women, 52 ± 13 years), of whom 1050 developed HF (586 [56%] HFpEF, 381 [36%] HFrEF). PREVENT-HF demonstrated better discrimination for HFpEF than HFrEF across all samples: in the pooled cohorts, the AUROC for HFpEF was 0.824 (95% CI 0.807, 0.839) versus 0.789 (95% CI 0.770, 0.807) for HFrEF; in BIDMC, the AUROC for HFpEF was 0.897 (95% CI 0.874, 0.918) versus 0.833 (95% CI 0.791, 0.869) for HFrEF; in MGB, the AUROC for HFpEF was 0.864 (95% CI 0.848, 0.880) versus 0.797 (95% CI 0.771, 0.820) for HFrEF. Similar results were reflected in the AP/event rate ratios: 2.92 (95% CI 2.47, 3.54) for HFpEF compared to 2.66 (95% CI 2.33, 3.14) for HFrEF in the pooled cohorts; 8.08 (95% CI 6.12, 12.30) for HFpEF compared to 5.12 (95% CI 3.84, 8.17) for HFrEF in BIDMC; and 6.84 (95% CI 5.66, 8.28) for HFpEF compared to 4.12 (95% CI 3.37, 5.16) for HFrEF in MGB. Calibration was similar for HFpEF and HFrEF across samples. For example, in the pooled cohorts, the ICI for HFpEF was 0.041 (95% CI 0.039, 0.043) and for HFrEF was 0.038 (95% CI 0.036, 0.040). Conclusions: PREVENT-HF demonstrated excellent discrimination for both HF subtypes across community-based cohorts and real-world samples, with consistently better discrimination for HFpEF as compared to HFrEF. Given PREVENT-HF was developed to estimate overall HF risk rather than subtype-specific risk, future studies are needed to establish subtype-specific risk thresholds.

Cardiovascular disease remains the leading cause of death among women in the United States and worldwide, yet current approaches to prevention and management do not fully account for female‐specific biology. To address this need, the American Heart Association, in collaboration with the American Academy of Pediatrics, the American College of Obstetricians and Gynecologists, and the Society for Maternal‐Fetal Medicine, published a Presidential Advisory defining the Female Life Stage construct, which defines four distinct stages across the life course: Perimenarche (Stage 1), Ovarian Cycling (Stage 2), Perimenopause (Stage 3), and Postmenopause (Stage 4), with pregnancy including the time before and after (Peripregnancy period) recognized as a distinct life period when cardiovascular risk may be modified. ¹ To provide examples of how this framework and the accompanying consensus guidance published in an accompanying Scientific Statement can be translated into a personalized care approach, this special report presents six hypothetical cases spanning the Female Life Stages, including adolescence and young adulthood, the peripartum period, midlife, perimenopause, and postmenopause. ² These cases illustrate how a female‐specific life course framework that reflects hormonally‐mediated physiologic transitions can promote earlier recognition of cardiovascular risk, guide cardiovascular screening and inform cardiovascular management. The cases underscore how hormonal health (e.g., menstrual cycle assessment, pregnancy planning and prevention, assessing and managing perimenopausal and postmenopausal symptoms) and psychosocial health (e.g., anxiety, depression, disordered eating) have cardiovascular implications. Moreover, this approach integrates well‐established female‐specific cardiovascular risk factors (e.g., premature menopause and adverse pregnancy outcomes), in addition to less well‐recognized risk factors (e.g., early menarche, polyendocrine metabolic ovarian syndrome). We also review key evidence to inform a patient‐centered approach to exogenous hormone therapy and associated cardiovascular risks for a variety of indications, including management of abnormal uterine bleeding, ovarian suppression, pregnancy prevention, and menopausal symptoms. By contextualizing the Female Life Stage framework within practical case scenarios, this article provides clinicians with a structured, evidence‐based approach to improve cardiovascular prevention and management for girls and women.

Right ventricular (RV) size and function inform prognosis in many cardiovascular diseases, but assessment by two-dimensional transthoracic echocardiography is challenging. Here we show that a deep learning model, Dimensional Reconstruction of Imaging Data-Right Ventricle (DROID-RV), can estimate RV end-diastolic diameter and identify RV systolic dysfunction from transthoracic echocardiography videos. We trained DROID-RV in 17,141 ambulatory cardiology patients (51,188 studies and 383,881 video clips) and validated model predictions in 9,112 primary care patients and 831 patients with RV ejection fraction assessed by three-dimensional transthoracic echocardiography. Across multiple test samples, DROID-RV accurately estimates RV end-diastolic diameter and identifies RV dysfunction, although model prediction error increases at extremes of RV size and function. We additionally show that RV size and function predictions are associated with incident mortality, atrial fibrillation and heart failure. Our work demonstrates how deep learning can automate RV assessment and risk stratification at scale using two-dimensional echocardiography.

BACKGROUND: Cardiorespiratory fitness, commonly measured as peak oxygen consumption (peak VO ₂ , is a strong predictor of cardiovascular health. Circulating lipids have been linked to exercise responses and metabolic adaptation, but the bioactive lipid species associated with objectively measured aerobic capacity and their dynamic regulation during exercise remain incompletely defined. METHODS: We performed directed, nontargeted liquid chromatography–mass spectrometry profiling of circulating bioactive lipids in adults undergoing clinically indicated cardiopulmonary exercise testing, relating resting lipid levels and acute exercise-induced lipid changes to peak VO ₂ . We then examined fitness-associated lipid species across complementary models of physiological stress, including marathon running, chronic voluntary wheel running in mice, and the postprandial Burmese python model. RESULTS: Among 1896 detected lipid species in the primary human cohort, 47 were associated with peak VO ₂ after false-discovery rate correction, including 16 metabolites with known molecular identities. Higher levels of several eicosanoids and free fatty acids were associated with greater peak VO ₂ , including docosahexaenoic acid, arachidic acid, and 12,13-dihydroxy-9Z-octadecenoic acid. In paired rest and peak exercise samples, 37 of 47 fitness-associated lipid species changed acutely with exercise. Among metabolites with known identities, exercise-induced changes in 12,13-dihydroxy-9Z-octadecenoic acid, 8-hydroxyicosa-9,11,14-trienoic acid, 11,12-epoxy-5Z,8Z,14Z-eicosatrienoic acid, and 8,9-dihydroxy-5Z,11Z,14Z-eicosatrienoic acid were associated with peak VO ₂ . In marathon runners, 43 of 47 fitness-associated lipid species changed after prolonged endurance exercise. Exploratory animal analyses demonstrated overlapping lipid regulation in chronic voluntary wheel-running mice and in the postprandial Burmese python model. CONCLUSIONS: Circulating bioactive lipids, particularly eicosanoids and free fatty acids, are associated with human cardiorespiratory fitness and are dynamically regulated during exercise. Overlapping lipid responses across complementary human and animal models identify candidate lipid pathways that may merit future mechanistic study.

Background: Sex differences in clinical presentations of heart failure (HF) are pervasive, yet differences in invasive hemodynamic measures across HF subtypes remain incompletely characterized. Understanding these distinctions is critical, as sex‐specific physiology influences disease progression and prognostic assessment in HF. Methods: We conducted a retrospective cohort study of 1818 patients with HF who underwent clinically indicated right heart catheterization, stratified by preserved versus reduced ejection fraction. We examined the association of sex with invasive hemodynamic measures of right ventricular, pulmonary vascular, and left ventricular function using multivariable linear regression. Results: We studied n=709 individuals with heart failure with preserved ejection fraction (HFpEF) (44% women, mean age 68±10 years) and n=1109 with heart failure with reduced ejection fraction (HFrEF) (26% women, mean age 64±12 years). Women and men had similar measures of right ventricular function across HF subtypes (multivariable‐adjusted P >0.05 for all). However, women with both HFpEF and HFrEF exhibited worse pulmonary vascular function, with significantly higher pulmonary vascular resistance and lower pulmonary artery compliance (multivariable‐adjusted P P =0.01 for HFpEF; P <0.001 for HFrEF). Conclusions: Among a large hospital‐based sample of patients with HF, women and men exhibited similar indices of right ventricular function, despite women having worse pulmonary vascular function. Cardiac power index was similar between sexes in both HFpEF and HFrEF, whereas aortic pulsatility index was higher in women with HFrEF. Taken together, sex differences in hemodynamic measures were largely similar across individuals with HFpEF and HFrEF. Future studies are needed to delineate clinical implications of these sex differences.

Was not provided for this article.