European Heart Journal最新文献

Background and aims: Individuals with coronary artery disease have poorer mental health, health-related quality of life (HR-QoL), and cognition compared with (age-matched) controls. Exercise training may attenuate these effects. The aim is to systematically review and meta-analyse the effects of different exercise types and settings on brain structure/function, cognition, HR-QoL, mental health (e.g. depression, anxiety), and sleep in patients with coronary artery disease.

Methods: A systematic search was conducted and a network meta-analysis compared (i) exercise types, high-intensity interval training (HIIT), HIIT + resistance (HIIT + R), moderate-intensity training (MIT), MIT + R and stretching-toning-balance training, and (ii) exercise settings, in-person and home-based.

Results: A total of 42 randomized controlled trials with a parallel group design were identified, of which 36 were included in the meta-analysis. Few studies included cognition (n = 2), sleep (n = 2), and none brain structure/function (n = 0). Most studies examined HR-QoL (n = 30), depression (n = 15), and anxiety (n = 9), in which outcomes were meta-analysed. HIIT + R, HIIT, and MIT were associated with improved HR-QoL vs. no exercise (i.e. usual care) [standardized mean difference, SMD: 1.53 (95% confidence interval 0.83; 2.24), 0.44 (0.15; 0.73), and 0.44 (0.20; 0.67), respectively]. In-person exercise was associated with larger and significant improvements [HR-QoL SMD: 0.51 (0.28; 0.74), depressive SMD: -0.55 (-1.03; -0.07), and anxiety symptoms SMD: -1.16 (-2.05; -0.26)] compared with no exercise, whereas home-based programmes were not significantly associated with improvements in these outcomes. Findings were robust in secondary (i.e. intervention duration and volume) and sensitivity analyses excluding high risk of bias studies.

Conclusions: Exercise training, especially in-person sessions, was associated with improved HR-QoL, depression and anxiety, independently of exercise type. However, this study raises concern about the effectiveness of home-based programmes in improving these outcomes.Study protocol was registered in PROSPERO (ID: CRD42023402569).

Background and aims: The importance of risk stratification in patients with chest pain extends beyond diagnosis and immediate treatment. This study sought to evaluate the prognostic value of electrocardiogram feature-based machine learning models to risk-stratify all-cause mortality in those with chest pain.

Methods: This was a prospective observational cohort study of consecutive, non-traumatic patients with chest pain. All-cause death was ascertained from multiple sources, including the CDC National Death Index registry. Six machine learning models were trained for survival analysis using 73 morphological electrocardiogram features (80% training with 10-fold cross-validation and 20% testing), followed by a variational Bayesian Gaussian mixture model to define distinct risk groups. The resulting classification performance was compared against the HEART score.

Results: The derivation cohort included 4015 patients (age 59 ± 16 years, 47% women). The mortality rate was 20.3% after a median follow-up period of 3.05 years (interquartile range 1.75-5.32). Extra Survival Trees outperformed other forecasting models, and the derived risk groups successfully classified patients into low-, moderate-, and high-risk groups (log-rank test statistic = 121.14, P < .001). This model outperformed the HEART score, reducing the rate of missed events by >90% with a negative predictive value and sensitivity of 93.4% and 85.9%, compared to 89.0% and 75.0%, respectively. In an independent external testing cohort (N = 3095, age 59 ± 15 years, 44% women, 30-day mortality 3.5%), patients in the moderate [odds ratio 3.62 (1.35-9.74)] and high [odds ratio 6.12 (2.38-15.75)] risk groups had significantly higher odds of mortality compared to those in the low-risk group.

Conclusions: The externally validated machine learning-based model, exclusively utilizing features from the 12-lead electrocardiogram, outperformed the HEART score in stratifying the mortality risk of patients with acute chest pain. This may have the potential to impact the precision of care delivery and the allocation of resources to those at highest risk of adverse events.

Background and aims: Current heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy to predict HF risk.

Methods: Across multinational cohorts in the Yale New Haven Health System (YNHHS), UK Biobank (UKB), and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), individuals without baseline HF were followed for the first HF hospitalization. An AI-ECG model that defines cross-sectional left ventricular systolic dysfunction from 12-lead ECG images was used, and its association with incident HF was evaluated. Discrimination was assessed using Harrell's C-statistic. Pooled cohort equations to prevent HF (PCP-HF) were used as a comparator.

Results: Among 231 285 YNHHS patients, 4472 had primary HF hospitalizations over 4.5 years (inter-quartile range 2.5-6.6). In UKB and ELSA-Brasil, among 42 141 and 13 454 people, 46 and 31 developed HF over 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years. A positive AI-ECG screen portended a 4- to 24-fold higher risk of new-onset HF [age-, sex-adjusted hazard ratio: YNHHS, 3.88 (95% confidence interval 3.63-4.14); UKB, 12.85 (6.87-24.02); ELSA-Brasil, 23.50 (11.09-49.81)]. The association was consistent after accounting for comorbidities and the competing risk of death. Higher probabilities were associated with progressively higher HF risk. Model discrimination was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. In YNHHS and ELSA-Brasil, incorporating AI-ECG with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone.

Conclusions: An AI model applied to a single ECG image defined the risk of future HF, representing a digital biomarker for stratifying HF risk.

Background and aims: Overweight and obesity are modifiable risk factors for atherosclerotic cardiovascular disease (ASCVD) in the general population, but their prevalence in individuals with heterozygous familial hypercholesterolaemia (HeFH) and whether they confer additional risk of ASCVD independent of LDL cholesterol (LDL-C) remains unclear.

Methods: Cross-sectional analysis was conducted in 35 540 patients with HeFH across 50 countries, in the EAS FH Studies Collaboration registry. Prevalence of World Health Organization-defined body mass index categories was investigated in adults (n = 29 265) and children/adolescents (n = 6275); and their association with prevalent ASCVD.

Results: Globally, 52% of adults and 27% of children with HeFH were overweight or obese, with the highest prevalence noted in Northern Africa/Western Asia. A higher overweight/obesity prevalence was found in non-high-income vs. high-income countries. Median age at familial hypercholesterolaemia diagnosis in adults with obesity was 9 years older than in normal weight adults. Obesity was associated with a more atherogenic lipid profile independent of lipid-lowering medication. Prevalence of coronary artery disease increased progressively across body mass index categories in both children and adults. Compared with normal weight, obesity was associated with higher odds of coronary artery disease in children (odds ratio 9.28, 95% confidence interval 1.77-48.77, adjusted for age, sex, lipids, and lipid-lowering medication) and coronary artery disease and stroke in adults (odds ratio 2.35, 95% confidence interval 2.10-2.63 and odds ratio 1.65, 95% confidence interval 1.27-2.14, respectively), but less consistently with peripheral artery disease. Adjusting for diabetes, hypertension and smoking modestly attenuated the associations.

Conclusions: Overweight and obesity are common in patients with HeFH and contribute to ASCVD risk from childhood, independent of LDL-C and lipid-lowering medication. Sustained body weight management is needed to reduce the risk of ASCVD in HeFH.

Development of specific therapies addressing the underlying diseases' mechanisms constitutes the basis of precision medicine. Transthyretin cardiac amyloidosis (ATTR-CM) is an exemplar of precise therapeutic approach in the field of heart failure and cardiomyopathies. A better understanding of the underlying pathophysiology, more precise data of its epidemiology, and advances in imaging techniques that allow non-invasive diagnosis have fostered the development of new and very effective specific therapies for ATTR-CM. Therapeutic advances have revolutionized the field, transforming a rare, devastating, and untreatable disease into a more common disease with several therapeutic alternatives available. Three main types of therapies (stabilizers, suppressors, and degraders) that act at different points of the amyloidogenic cascade have been developed or are currently under investigation. In this review, the key advances in pathophysiology and epidemiology that have occurred in the last decades along with the different therapeutic alternatives available or under development for ATTR-CM are described, illustrating the role of precision medicine applied to cardiovascular disorders. Pending questions that would need to be answered in upcoming years are also reviewed.