European heart journal. Digital health最新文献

Aims: We explored whether artificial intelligence (AI)-enabled electrocardiographic (ECG) sex discrepancy would predict atrial fibrillation (AF) recurrence after catheter ablation for paroxysmal AF.

Methods and results: The AI-ECG sex prediction model was developed from the MIMIC-IV and externally validated on CODE-15% (AUC 0.89) and UK Biobank (AUC 0.92) cohorts. After validation, we estimated AI-ECG sex from pre-procedural sinus rhythm ECGs among paroxysmal AF patients scheduled for catheter ablation using data from a pooled AF ablation cohort (n = 4385) in South Korea. ECG sex discrepancy was defined as ECG sex probability of more than 50% for the opposite sex. During a median follow-up of 24 months, 1094 recurrences developed [median age 60 (52-67) years; women 29.0%]. ECG sex discrepant patients were older, had more heart failure, and had elevated diastolic filling pressure compared with ECG sex non-discrepant patients. The odds ratio (OR) for left atrial enlargement was significantly higher among ECG sex discrepant women [adjusted OR 1.67, 95% confidence interval (CI) 1.14-2.44, P = 0.008] but not among men (adjusted OR 0.88, 95% CI 0.66-1.17, P = 0.368). The 5-year cumulative event rate of AF recurrence was significantly higher among ECG sex discrepant women (log rank, P = 0.015) but not among men (log rank, P = 0.871). The 5-year risk of AF recurrence was significantly higher among ECG sex discrepant women [hazard ratio (HR) 1.42, 95% CI 1.10-1.83, P = 0.007] but not among men (HR 1.01, 95% CI 0.76-1.34, P = 0.940).

Conclusion: Pre-procedural ECG sex discrepancy has a prognostic value for AF recurrence after catheter ablation for paroxysmal AF in women.

Artificial intelligence (AI) tools hold great promise for the rapid and accurate diagnosis of coronary artery disease (CAD) from intravascular optical coherent tomography (IVOCT) images. Numerous papers have been published describing AI-based models for different diagnostic tasks, yet it remains unclear, which models have potential clinical utility and have been properly validated. This systematic review considered published literature between January 2015 and December 2024 describing AI-based diagnosis of CAD using IVOCT. Our search identified 8600 studies, with 629 included after initial screening and 39 studies included in the final systematic review after quality screening. Our findings indicate that most of the identified models are not currently suitable for clinical use, primarily due to methodological flaws and underlying biases. To address these issues, we provide recommendations to improve model quality and research practices to enhance the development of clinically useful AI products.

Aims: The COVID-19 pandemic has raised patient awareness of their health and highlighted the importance of remote care. Smartphones and wearable devices are now becoming essential for managing cardiovascular disease. However, low digital health readiness among cardiology patients poses a significant challenge to the effective use of these technologies. This study evaluates digital health readiness and learning ability of Japanese cardiology patients using the Digital Health Readiness Questionnaire (DHRQ), while also assessing its reliability and validity.

Methods and results: This multicentre observational study evaluated digital health readiness among patients with cardiovascular risk factors at St. Marianna University Hospital and Kawasaki Municipal Tama Hospital. The DHRQ was employed, and confirmatory factor analysis was conducted to validate the measurement model. A total of 210 questionnaires were distributed, with 208 included in the analysis. Internal consistency, measured by Cronbach's alpha, exceeded 0.7 across all factors. Model fit was evaluated with standardised root mean square residual = 0.038, root mean square error of approximation = 0.071, comparative fit index = 0.962, and Tucker-Lewis index = 0.955. Age, education, and smartphone/smartwatch ownership significantly predicted higher DHRQ scores. Older age correlated with lower scores (P < 0.001), while higher education, smartphone (P < 0.001), and smartwatch ownership (P = 0.006) correlated with higher scores. Gender and income were not significant.

Conclusion: The DHRQ proved to be valid in Japan, with education level significantly affecting scores. Improved digital health readiness is suggested to enhance patients' management of health information and communication with healthcare providers, and is expected to be linked to future healthcare systems.

Aims: Rapid myocardial revascularization in patients with acute myocardial infarction (AMI) is essential to improve clinical outcomes. There is still room for improvement in the timely diagnosis of AMI. This study aimed to develop an artificial intelligence (AI) model using electrocardiograms (ECGs) for detecting AMI needing revascularization.

Methods and results: A total of 723 389 ECGs from 300 627 patients in the derivation cohort at a single centre between 2013 and 2020, including 5872 patients with AMI (1.95%) who underwent revascularization, were used for model training and internal testing. A transformer-based deep learning model, initially trained on about one million unlabelled ECGs through self-supervised learning, was fine-tuned for AMI detection. The model's final performance was evaluated in the internal test and the external validation set. The external validation was conducted at an independent centre between 2002 and 2020 using 261 429 ECGs from 259 454 patients, including 1095 patients with AMI (0.42%). By integrating self-supervised learning to train the AI model, we enhanced the AMI detection performance, as demonstrated by an increase in the area under the receiver operating characteristic curve (AUROC) from 0.910 (95% CI, 0.904-0.915) to 0.968 (95% CI, 0.965-0.971) in the external validation set. For ST-elevation myocardial infarction and non-ST-elevation myocardial infarction detection, the AUROCs were 0.991 (95% CI, 0.989-0.993) and 0.947 (95% CI, 0.942-0.952) in the external validation set, respectively.

Conclusion: This novel ECG-based AI model may be beneficial for the timely identification of patients with AMI needing revascularization.

Aims: Deep learning methods have shown impressive performance in detecting a range of diseases from electrocardiogram (ECG) waveforms, but the breadth of diseases that can be detected with high accuracy remains unknown, and in many cases the changes to the ECG allowing these classifications are also opaque. In this study, we aim to determine the full set of cardiac and non-cardiac conditions detectable from the ECG and to understand which ECG features contribute to the disease classification.

Methods and results: Using large datasets of ECGs and connected electronic health records from two separate medical centres, we independently trained PheWASNet, a multi-task deep learning model, to detect 1243 different disease phenotypes from the raw ECG waveform. We confirmed that the ECG can be used to detect chronic kidney disease (AUC = 0.80), cirrhosis (AUC = 0.80), and sepsis (AUC = 0.84), as well as a range of cardiac diseases, and also found new detectable conditions, including respiratory failure (AUC = 0.86), neutropenia (AUC = 0.83), and menstrual disorders (AUC = 0.84). We found that of the 37 non-cardiac strongly detectable conditions, 35 were detectable by the model output for just four diseases, suggesting that they have similar effects on the ECG. We found that high performance in some conditions including neutropenia, respiratory failure, and sepsis can be explained by linear models based on conventional measurements taken from the ECG.

Conclusion: Our study uncovers a range of diseases detectable in the ECG, including many previously unknown phenotypes, and makes progress towards understanding ECG features that allow this detection.

Aims: Whole blood RNA expression is modulated in response to signals from tissues, including the vessel wall. The primary objective of this study was to explore the ability of whole blood transcriptomes, analysed using artificial intelligence (AI), to predict coronary artery calcifications (CAC).

Methods and results: A total of 196 subjects [men aged 40-70 years and women aged 50-70 years without known cardiovascular disease (CVD)] were non-consecutively enrolled for CAC assessment via chest computed tomography. Whole blood RNA was isolated and sequenced. Different AI models were trained using clinical and transcriptomic variables as distinctive features to identify the presence of CAC (Agatston score >0). Finally, we compared the predictive performance of these models. The prevalence of CAC was 43.9%. The combined AI model, incorporating transcriptome data along with age, sex, body mass index, smoking status, diabetes, and hypercholesterolaemia, achieved an area under the curve (AUC) of 0.92 (95% CI, 0.88-0.95) for predicting the presence of CAC, with a sensitivity of 92%, specificity of 80%, positive predictive value of 81%, negative predictive value of 91%, and an overall accuracy of 86%. The combined AI model demonstrated significantly improved discrimination compared with the transcriptomic model (AUC 0.79; P = 0.009), the clinical variables model (AUC 0.72; P < 0.001), and the CVD risk model (AUC 0.68; P < 0.001).

Conclusion: In this pilot study, an AI model integrating whole blood transcriptome data with clinical risk factors demonstrated the ability to predict CAC, providing incremental value over clinical models. Further studies are needed to achieve more robust validation.

Aims: Treatment capacities on intensive care units (ICUs) are a limited resource reserved for high-risk patients. To facilitate risk stratification of ICU patients, several scoring systems have been developed over time. Among them, the Simplified Acute Physiology Score 3 (SAPS3) is the gold standard, but lacks specificity for cardiac ICU patients. Here, we propose a novel, fully automated, electrocardiogram-based cardiac autonomic risk stratification score (CAF_ICU) that substantially adds to current risk stratification strategies.

Methods and results: CAF_ICU is based on periodic repolarization dynamics, a marker of sympathetic overactivity and deceleration capacity of heart rate, a parameter of vagal imbalance. We developed CAF_ICU in a retrospective cohort of 355 ICU patients and subsequently validated the score in a cohort of 702 ICU patients, enrolled between February-November 2018 and December 2018-April 2020 at a large cardiac ICU in a tertiary hospital. The primary endpoint of the study was 30-day intrahospital mortality. Thirty (8.5%) and 100 (14.2%) patients reached the primary endpoint in the training and validation cohorts, respectively. CAF_ICU was significantly higher in non-survivors than survivors (2.58 ± 1.34 vs. 1.76 ± 0.97 units; P = 0.003 in the training cohort and 2.20 ± 1.05 vs. 1.70 ± 0.83 units; P < 0.001 in the validation cohort) and was a strong predictor of mortality in both the training [hazard ratio (HR) 25.67; 95% confidence interval (CI) 3.50-188.40; P = 0.001] and validation cohorts (HR 4.70; 95% CI 2.79-7.92; P < 0.001). In the pooled cohort, CAF_ICU significantly improved risk stratification based on SAPS3 (IDI-increase 0.033; 95% CI 0.010-0.061; P < 0.001).

Conclusion: ECG-based automatic autonomic risk stratification by means of PRD and DC is highly predictive of short-term mortality in the ICU and can be combined with the SAPS3-Score to identify patients with increased risk for intrahospital mortality. This method can be integrated in conventional monitors and may improve risk stratification strategies in cardiac ICUs.