European heart journal. Digital health最新文献

Aims: Increased left ventricular mass has been associated with adverse cardiovascular outcomes including incident cardiomyopathy and atrial fibrillation. Such associations have been studied in relation to total left ventricular hypertrophy, while the regional distribution of myocardial hypertrophy is extremely variable. The clinically significant and genetic associations of such variability require further study.

Methods and results: Here, we use deep learning-derived phenotypes of disproportionate patterns of hypertrophy, namely, apical and septal hypertrophy, to study genome-wide and clinical associations in addition to and independent from total left ventricular mass within 35 268 UK Biobank participants. Using polygenic risk score and Cox regression, we quantified the relationship between incident cardiovascular outcomes and genetically determined phenotypes in the UK Biobank. Adjusting for total left ventricular mass, apical hypertrophy is associated with elevated risk for cardiomyopathy and atrial fibrillation. Cardiomyopathy risk was increased for subjects with increased apical or septal mass, even in the absence of global hypertrophy. We identified 17 genome-wide associations for left ventricular mass, 3 unique associations with increased apical mass, and 3 additional unique associations with increased septal mass. An elevated polygenic risk score for apical mass corresponded with an increased risk of cardiomyopathy and implantable cardioverter-defibrillator implantation.

Conclusion: Apical and septal mass may be driven by genes distinct from total left ventricular mass, suggesting unique genetic profiles for patterns of hypertrophy. Focal hypertrophy confers independent and additive risk to incident cardiovascular disease. Our findings emphasize the significance of characterizing distinct subtypes of left ventricular hypertrophy. Further studies are needed in multi-ethnic cohorts.

Aims: Wearable health technologies are increasingly popular. Yet, wearable monitoring only works when devices are worn as intended, and adherence reporting lacks standardization. In this study, we aimed to explore the long-term adherence to a wrist-worn activity tracker in the prospective SafeHeart study and identify patient characteristics associated with adherence.

Methods and results: This study enrolled 303 participants, instructed to wear a wrist-worn accelerometer day and night for 6 months. Long-term adherence was defined as valid days (≥22 h of wear time) divided by expected days, and daily adherence as mean hours of wear time per 24 h period. Optimal, moderate, and low long-term and daily adherence groups were defined as long-term adherence above or below 95 and 75% and daily adherence above or below 90 and 75%. Regression models were used to identify patient characteristics associated with long-term adherence. In total, 296 participants [median age 64 years; interquartile range (IQR) 57-72; 19% female] were found eligible, yielding 44 003 days for analysis. The median long-term adherence was 88.2% (IQR 74.6-96.5%). A total of 83 (28%), 127 (42.9%), and 86 (29.1%) participants had optimal, moderate, and low long-term adherence, and 163 (55.1%), 87 (29.4%), and 46 (15.5%) had optimal, moderate, and low daily adherence, respectively. Age and smoking habits differed significantly between adherence levels, and increasing changeover intervals improved the degree of long-term adherence.

Conclusion: Long-term adherence to a wearable activity tracker was 88.2% over a 6-month period. Older age and longer changeover interval were positively associated with long-term adherence. This serves as a benchmark for future studies that rely on wearable devices.

Trial registration number: The National Trial Registration number: NL9218 (https://onderzoekmetmensen.nl/).

[This corrects the article DOI: 10.1093/ehjdh/ztae013.].

Aims: Heart failure (HF) registries provide valuable insights into patient management and quality of care. However, healthcare professionals face challenges due to the administrative burden of participation in registries. This study aims to evaluate the impact of education through an engagement toolkit on HF nurse practitioners' participation rate and data completeness in a national registry: the Netherlands Heart Registration-Heart Failure (NHR-HF) registry.

Methods and results: Engage-HF is an observational study (intervention at the HF nurse level) with a pretest-posttest design within the participating hospitals. Between December 2022 and April 2024, 28 HF nurse practitioners from 12 hospitals will participate in a 24-week educational programme using the Engage-HF engagement toolkit. The main interaction platform in this toolkit is a gamified smartphone-based educational application called BrightBirds. The complete toolkit includes this educational application with weekly challenges, interactive posters, pop-ups, and alert messages, and a follow-up call at Week 4. The primary endpoints are the NHR-HF participation rates and data completeness at 1 and 6 months after using the toolkit. Additionally, we will analyse the experience of participants with the toolkit concerning their HF registry and knowledge of ESC 2021 HF guidelines.

Conclusion: The Engage-HF study is the first to explore the impact of education through a gamified engagement toolkit to boost participation rates in a HF registry (NHR-HF) and test participant knowledge of the ESC 2021 HF guidelines. This innovative approach addresses challenges in the rollout of healthcare registries and the implementation of guidelines by providing a contemporary support base and a time-efficient method for education.

Aims: Despite the highest prevalence of stroke, obesity, and diabetes across races/ethnicities, paradoxically, Hispanic/Latino populations have the lowest prevalence of atrial fibrillation and major Minnesota code-defined ECG abnormalities. We aimed to use Latent Profile Analysis in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) population to obtain insight into epidemiological discrepancies.

Methods and results: We conducted a cross-sectional analysis of baseline HCHS/SOL visit. Global electrical heterogeneity (GEH) was measured as spatial QRS-T angle (QRSTa), spatial ventricular gradient azimuth (SVGaz), elevation (SVGel), magnitude (SVGmag), and sum absolute QRST integral (SAIQRST). Statistical analysis accounted for the stratified two-stage area probability sample design. We fitted a multivariate latent profile generalized structural equation model adjusted for age, sex, ethnic background, education, hypertension, diabetes, smoking, dyslipidaemia, obesity, chronic kidney disease, physical activity, diet quality, average RR' interval, median beat type, and cardiovascular disease (CVD) to gain insight into the GEH profiles. Among 15 684 participants (age 41 years; 53% females; 6% known CVD), 17% had an increased probability of likely abnormal GEH profile (QRSTa 80 ± 27°, SVGaz -4 ± 21°, SVGel 72 ± 12°, SVGmag 45 ± 12 mVms, and SAIQRST 120 ± 23 mVms). There was a 23% probability for a participant of being in Class 1 with a narrow QRSTa (40.0 ± 10.2°) and large SVG (SVGmag 108.3 ± 22.6 mVms; SAIQRST 203.4 ± 39.1 mVms) and a 60% probability of being in intermediate Class 2.

Conclusion: A substantial proportion (17%) in the Hispanic/Latino population had an increased probability of altered, likely abnormal GEH profile, whereas 83% of the population was resilient to harmful risk factors exposures.

Aims: This study evaluated the feasibility of the intermittent use of direct oral anticoagulants (DOACs) guided by continuous rhythm monitoring via a clinically validated wearable smart device in high-bleeding risk (HBR) patients with symptomatic paroxysmal atrial fibrillation (AF) otherwise subjected to chronic anticoagulation after percutaneous coronary intervention (PCI).

Methods and results: The INTERMITTENT registry was a 3-year prospective observational study at eight Italian centres. Inclusion criteria were elective or urgent PCI, Academic Research Consortium HBR criteria, history of symptomatic 12-lead ECG detected paroxysmal AF episodes, indication to DOACs, and use of a wearable smart device (Apple Watch™). Thirty days after PCI, patients free of AF episodes discontinued DOAC. However, if an AF episode lasting >6 min or a total AF burden > 6 h over 24 h was detected, DOAC was initiated for 30 consecutive days, and withdrawn afterwards if no further AF episodes occurred. At the discretion of the referring physician, intermittent anticoagulation was offered to 89 patients, whereas continuous treatment with DOACs was prescribed to 151 patients. During a follow-up of 298 ± 87 days, the average duration of oral anticoagulation was significantly shorter in the intermittent anticoagulation group (176 ± 43 days, P = 0.0001), representing a 40% reduction in anticoagulation time compared to the continuous group. Ischaemic and bleeding endpoints were not significantly different between the two groups. Propensity score-matching resulted in a total of 69 matched patients with intermittent vs. continuous anticoagulation, respectively. During a follow-up of 291 ± 63 days, there was a significant 46% reduction in anticoagulation time in the intermittent compared to the continuous group (P = 0.0001).

Conclusion: In HBR patients with a history of paroxysmal AF episodes who underwent PCI, intermittent anticoagulation guided by continuous rhythm monitoring with a wearable device was feasible and decreased significantly the duration of anticoagulation.

Aims: Aortic elongation can result from age-related changes, congenital factors, aneurysms, or conditions affecting blood vessel elasticity. It is associated with cardiovascular diseases and severe complications like aortic aneurysms and dissection. We assess qualitatively and quantitatively explainable methods to understand the decisions of a deep learning model for detecting aortic elongation using chest X-ray (CXR) images.

Methods and results: In this study, we evaluated the performance of deep learning models (DenseNet and EfficientNet) for detecting aortic elongation using transfer learning and fine-tuning techniques with CXR images as input. EfficientNet achieved higher accuracy (86.7% $\pm$ 2.1), precision (82.7% $\pm$ 2.7), specificity (89.4% $\pm$ 1.7), F1 score (82.5% $\pm$ 2.9), and area under the receiver operating characteristic (92.7% $\pm$ 0.6) but lower sensitivity (82.3% $\pm$ 3.2) compared with DenseNet. To gain insights into the decision-making process of these models, we employed gradient-weighted class activation mapping and local interpretable model-agnostic explanations explainability methods, which enabled us to identify the expected location of aortic elongation in CXR images. Additionally, we used the pixel-flipping method to quantitatively assess the model interpretations, providing valuable insights into model behaviour.

Conclusion: Our study presents a comprehensive strategy for analysing CXR images by integrating aortic elongation detection models with explainable artificial intelligence techniques. By enhancing the interpretability and understanding of the models' decisions, this approach holds promise for aiding clinicians in timely and accurate diagnosis, potentially improving patient outcomes in clinical practice.

[This corrects the article DOI: 10.1093/ehjdh/ztad010.].

Aims: Current deep learning algorithms for automatic ECG analysis have shown notable accuracy but are typically narrowly focused on singular diagnostic conditions. This exploratory study aims to investigate the capability of a single deep learning model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a single ECG collected in the emergency department.

Methods and results: In this study, we assess the performance of a model trained to predict a broad spectrum of diagnoses. We find that the model can reliably predict 253 ICD codes (81 cardiac and 172 non-cardiac) in the sense of exceeding an AUROC score of 0.8 in a statistically significant manner.

Conclusion: The model demonstrates proficiency in handling a wide array of cardiac and non-cardiac diagnostic scenarios, indicating its potential as a comprehensive screening tool for diverse medical encounters.