European journal of preventive cardiology最新文献

Introduction: Albuminuria is a significant marker for cardiovascular disease(CVD) in both diabetics and non-diabetics. However, its relationship with myocardial fibrosis in the general population remains unclear.

Methods: The study included 2,112 participants (52%female, mean age69±9years) from the MESA cohort who had urine albumin and creatinine measurements and underwent CMR with T1-mapping for myocardial fibrosis evaluation in 2010. Analyses were stratified by sex and diabetes stage. Multivariable linear and logistic regression models assessed associations of albuminuria/albumin-creatinine ratio(ACR) with extracellular volume (ECV), native T1-time, and myocardial scar.

Results: In fully adjusted models, a one-SD increase in log-transformed albumin and ACR was associated with 0.15% and 0.2% higher ECV and 2.7% and 2.6% higher native T1 time, respectively. In men, a one-SD increase in log-transformed albumin and ACR was associated with greater ECV (0.3% each) and higher native T1 time (3.3% and 2.6%, respectively). Among women, no significant associations with albuminuria or ACR were found. In participants with prediabetes, log-transformed urine albumin and ACR were positively associated with ECV (p < 0.05) but not with native T1. In diabetics, ACR showed a modest association with ECV and native T1, while log-transformed albumin was significantly associated with 0.3% greater ECV and 3% higher native T1 (p<0.05). The odds of myocardial scar were not associated with higher albumin/ACR after adjusting for CV risk factors.

Conclusions: Elevated albuminuria levels are related to subclinical fibrosis in a community-based setting, independent of traditional CV risk factors. This association was more prominent among prediabetics, diabetics and among males.

Aims: The vast majority of sudden cardiac death (SCD) cases occur in the general population with few known risk factors instead of just patients already identified to be at high risk, making the prediction of SCD very difficult. Therefore, a better screening tool should be developed to facilitate early identification.

Methods and results: To estimate the risk of SCD, we trained and validated a machine learning model on electronic health record (EHR) data covering 17 172 359 drug prescriptions and 1 639 057 hospital diagnoses up to 5 years for cases and controls. Training was done on data obtained from a cohort of 12 338 SCD cases in Greater Paris and 12 338 controls from 2011 to 2015. We then validated the results on two external cohorts: a temporal cohort in the same area from 2016 and 2020 with 11 620 SCD cases and 11 620 controls and a geographical cohort from the University of Washington (Seattle, USA) with 892 SCD cases and 892 controls from 2013 to 2021. In the 5 years preceding the SCD, cardiovascular diagnoses were prevalent in only a few patients, scarce in many patients, and totally nonexistent for 25.7% of subjects. Our model achieved an area under the curve of 0.81 [95% confidence interval (CI), 0.80-0.82] and 0.66 (95% CI, 0.58-0.73) in the validation and geographical cohort, respectively. The prediction model discriminated SCD from the general population, especially in the highest decile, where the model detected 26% and 33% of all SCD in the Paris and Seattle datasets, respectively. Our prediction model was specific to SCD and was not predictive of myocardial infarction. In addition to classical cardiovascular risk factors, various non-cardiovascular drugs and diagnoses contributed to the prediction model.

Conclusion: We propose a prediction model designed to identify individuals at high risk of SCD among the general population. When combined with EHR, this artificial intelligence model has the potential to assist in risk stratification and may help inform the prioritization of preventive strategies, contributing to more targeted use of cardiovascular health resources.

Aims: Electronic health records (EHR) can be used to target atrial fibrillation (AF) screening. We evaluated the performance of risk prediction models scalable across nationwide EHRs.

Methods: Retrospective cohort study individuals aged ≥30 years without diagnosed AF in the Clalit Health Services (Israel) EHR dataset between January 1 2019 and June 30, 2019. The primary outcome was a diagnosis of AF or atrial flutter (AFl) within 6 months. The FIND-AF, CHA2DS2-VASc and C2HEST scores were evaluated, with prediction performance assessed overall and by sex. The optimum threshold to apply in prospective screening was determined with a lift analysis.

Results: Of 2,166,795 individuals in the cohort 4,275 developed AF within 6 months. Prediction performance was strongest for FIND-AF (AUROC 0.871, 95% CI 0.864-0.877; calibration slope 0.73, 95% CI 0.67-0.79) compared with CHA2DS2-VASc (AUROC 0.838, 95% CI 0.831-0.845; calibration slope 0.63, 95% CI 0.60-0.67) and C2HEST scores (AUROC 0.834, 95% CI 0.823-0.844; calibration slope 0.62, 95% CI 0.58-0.65), including in women (FIND-AF AUROC 0.883, 95% CI 0.876-0.889; CHA2DS2-VASc AUROC 0.865, 95% CI 0.858-0.872; C2HEST AUROC 0.853, 95% CI 0.846-0.861) and men (FIND-AF AUROC 0.857, 95% CI 0.850-0.864; CHA2DS2-VASc AUROC 0.835, 95% CI 0.828-0.843; C2HEST AUROC 0.814, 95% CI, 0.806-0.822). Lift analysis suggested that screening the top 15% of FIND-AF risk compared to screening by age would identify 72% compared to 63% of AF diagnoses.

Conclusion: The FIND-AF machine learning algorithm was scalable in routine EHR data with good discrimination for incident AF. Prospective evaluation is now required to evaluate risk-guided AF screening.

Aims: Although weekend warrior physical activity is linked to lower mortality in the general population, whether it confers similar benefits among individuals with cardiovascular disease (CVD) remains unknown. This study aimed to investigate the association between a weekend warrior activity pattern and mortality in patients with CVD.

Methods and results: We performed a sub-cohort analysis of 8,128 UK Biobank participants with CVD. Physical activity patterns were defined by weekly moderate-to-vigorous physical activity (MVPA) volume and distribution: weekend warrior (≥150 min/week MVPA, with ≥50% accumulated on 1-2 days), regularly active (≥150 min/week MVPA spread over >2 days), and inactive (<150 min/week MVPA). Outcomes were all-cause, CVD-specific, and cancer-specific mortality. Cox proportional hazards models were used to estimate hazard ratios (HRs) with corresponding 95% confidence intervals (CIs) for mortality across activity patterns. Of the participants, 3,004 (44.09%) were weekend warriors, 1,540 (18.95%) were regularly active, and 3,584 (36.96%) were inactive. Over a median follow-up of 7.85 years, 881 deaths occurred. Compared with the inactive pattern, both weekend warrior (HR, 0.61; 95% CI: 0.52-0.71) and regularly active (HR, 0.68; 95% CI: 0.56-0.82) patterns were associated with lower risks of all-cause mortality among participants with CVD. Similar associations were observed for CVD- and cancer-specific mortality.

Conclusions: Among individuals with CVD, the weekend warrior pattern was associated with a lower risk of mortality, with survival benefits comparable to those observed for the regularly active pattern. This pattern may serve as a practical alternative for patients who have difficulty maintaining regular activity across the week.

Aims: The association between socioeconomic deprivation and atrial fibrillation (AF) remains debated, with limited evidence on its interaction with genetic predisposition. We aimed to evaluate the combined effect of socioeconomic deprivation and genetic risk on incident AF.

Methods: Participants without prior AF from the UK Biobank were included. Socioeconomic deprivation was defined as the top 5% of the Index of Multiple Deprivation (IMD). Genetic risk was assessed using a polygenic risk score (PRS) for AF. The primary outcome was incident AF. Findings were replicated in an independent South Korean cohort.

Results: Among 425,423 participants (mean age 56.5±8.1 years; 45.2% male), 21,264 were in the deprived group. The deprived group was younger and had more comorbidities. AF incidence was higher in the deprived vs. non-deprived group (incidence rate 5.8 vs. 4.7/1,000 person-years; adjusted hazard ratio (aHR) 1.16 [95% CI 1.09-1.24], P<0.001). Most IMD components (except housing and living environment) were linked to increased AF risk (aHR ranging from 1.13 to 1.19). Deprivation had a stronger effect in those with low genetic risk (lowest PRS tertile: aHR 1.36 [1.19-1.56]) compared to those with high genetic risk (highest PRS tertile: aHR 1.11 [1.01-1.22]; interaction P=0.0013). Results were consistent in the Korean cohort (n=7,898).

Conclusion: Socioeconomically deprived individuals have a significantly higher risk of AF, with this risk further influenced by genetic predisposition. Targeted public health policies and personalized AF screening for high-risk groups may help lessen the burden of AF and improve cardiovascular health.