European heart journal. Digital health最新文献

Aims: Quality of life (QoL) improvement is a primary driver for atrial fibrillation (AF) catheter ablation (CA), yet its determinants remain unclear. We aimed to identify patient phenotypes with distinct post-ablation QoL trajectories, determine their key predictors, and clarify their association with arrhythmia recurrence and reintervention.

Methods and results: We prospectively followed 213 patients (median age 60 years, 31% female) undergoing AF CA at a tertiary hospital for 2.2 years [interquartile range (IQR): 1.6-2.6]. A digital health application collected real-time electronic patient-reported outcomes (PROs), including the AF Effect on QoL (AFEQT) questionnaire. Reference charts were generated from QoL trajectories of recurrence-free patients. Machine learning (ML) identified subgroups with distinct QoL trajectories, and explainable artificial intelligence (AI) highlighted key predictors. Quality of life improved by +26 AFEQT points [95% confidence interval (CI): 18-33] within 3 months post-ablation and remained stable thereafter, despite significant heterogeneity in individual responses. Patients with AF recurrence showed significantly lower QoL gains (P = 0.010). Machine learning identified three phenotypes: a younger cluster with the largest QoL improvements, an emotive cluster with higher recurrence rates and minimal QoL benefits despite additional antiarrhythmic reinterventions, and an older cluster with established cardiovascular risk factors. Anxiety, age, and AF duration emerged as key discriminators.

Conclusion: ML defined three clinically coherent phenotypes, each exhibiting distinct QoL trajectories and ablation outcomes. Explainable AI clarified how individual psychological and biological traits interact to shape these outcomes, highlighting the potential for tailored multidisciplinary care beyond individualized rhythm control strategies.

Aims: Spontaneous cardioversion (SCV) is commonly observed in patients presenting to emergency departments (EDs) with primary atrial fibrillation (AF). Predicting SCV could facilitate timely discharge and avoid costly admissions. We sought to evaluate whether SCV could be predicted using artificial intelligence-enabled electrocardiograms (AI-ECGs) and whether this could produce cost savings.

Methods and results: We recruited patients presenting to EDs with primary AF throughout 2022-23. Patients were excluded if the outcome of their AF episode was unclear, or the ECG was not accessible. Spontaneous cardioversion prediction was attempted using ResNet50, EfficientNet, and DenseNet convolutional neural network (CNN) architectures and subsequently an ensemble learning model. We then performed a cost-minimization analysis to estimate the cost effect of a prediction-guided 'wait-and-see' protocol. There were 1159 presentations to the ED, of which 502 had sufficient data for inclusion. The median age was 74.0 years and 54.0% were women. Spontaneous cardioversion occurred in 227 (45.2%) patients and was more frequent in younger patients (P < 0.001). The ensemble learning model outperformed individual CNNs, achieving an accuracy of 69.7% (SD 5.91) and a receiver operating characteristic area under the curve (ROC AUC) of 0.742 (SD 0.037) with a sensitivity and specificity of 0.736 (SD 0.068) and 0.657 (SD 0.150), respectively. The per patient cost was $4681 if all patients were admitted, which reduced to $3398 with a prediction-guided 'wait-and-see' protocol with a 33.3% reduction in overall hospitalization.

Conclusion: Artificial intelligence-enabled electrocardiogram can predict SCV in patients presenting to EDs with primary AF, and a prediction-guided 'wait-and-see' protocol utilizing AI-ECG could lead to substantial cost savings and reduced hospitalization.

Aims: This study aims to investigate the ownership of wearable health devices across different demographic groups and usage patterns among Chinese adults.

Methods and results: This was a cross-sectional study, with all data originating from the Huawei Blood Pressure Health Study, a real-world study aimed at exploring blood pressure management through wearable devices in China. Data were remotely collected using mobile phones and Huawei Watch D from 23 February 2022 to 31 March 2024. The system utilized artificial intelligence algorithms to assess participants' risk of hypertension and provided risk alarm feedback via mobile phones and watches. A total of 75 918 participants from 31 provinces were included, with an average age of 47 years. Most of the participants were concentrated in the economically developed South China and East China regions. Among the participants, 73.8% used the Watch D for blood pressure monitoring, and 10.5% received risk alerts. The rate of blood pressure monitoring on the day they received the alert was 78%. However, the rate significantly decreased between 6 months and 1 year (Mann-Kendall test, Z = -2.85, P < 0.05). For hypertensive patients, the blood pressure monitoring rate was 84% on the day they joined the study and decreased over time (Mann-Kendall test, Z = -3.09, P < 0.05). However, it remained above 50% within 6 months.

Conclusion: This study provides evidence of the digital health divide in the utilization of wearable devices among the Chinese population. Additionally, it proposes a potentially follow-up interval for employing wearable devices for maintaining compliance with blood pressure monitoring.

Study registration: URL: https://www.chictr.org.cn/.

Unique identifier for huawei-bphs: ChiCTR2200057354.

Aims: Recurrent congestive episodes are a primary cause of hospitalizations in patients with heart failure. Hitherto, outpatient management adopts a reactive approach, assessing patients clinically through frequent follow-up visits to detect congestion early. This study aims to assess the capabilities of a self-supervised contrastive learning-derived risk index to detect episodes of acute decompensated heart failure (ADHF) in patients using continuously recorded wearable time-series data.

Methods and results: This is the protocol for a single-arm, prospective cohort pilot study that will include 290 patients with ADHF. Acute decompensated heart failure is diagnosed by clinical signs and symptoms, as well as additional diagnostics (e.g. NT-proBNP). Patients will receive standard-of-care treatment, supplemented by continuous wearable-based monitoring of vital signs and physical activity, and are followed for 90 days. During follow-up, study visits will be conducted and presentations without clinical ADHF will be referred to as 'regular' and data from these episodes will be presented to a deep neural network that is trained by a self-supervised contrastive learning objective to extract features from the time-series that are typical in regular periods. The model is used to calculate a risk index measuring the dissimilarity of observed features from those of regular periods. The primary outcome of this study will be the risk index's accuracy in detecting episodes with ADHF. As secondary outcome data integrity and the score in the validated questionnaire System Usability Scale will be evaluated.

Conclusion: Demonstrating reliable congestion detection through continuous monitoring with a wearable and self-supervised contrastive learning could assist in pre-emptive heart failure management in clinical care.

Clinical trial registration: The study was registered in the German clinical trials register (DRKS00034502).

Aims: Atrial fibrillation (AF) is a common arrythmia that increases the risk of thromboembolism. Despite the effectiveness of anticoagulation in AF, underuse remains a substantial problem. Clinical decision support (CDS) systems may increase adherence to guideline recommended anticoagulation in AF. However, evidence regarding the cost-effectiveness of these interventions is lacking. The aim of this study was therefore to evaluate the cost-effectiveness of a CDS for AF.

Methods and results: We developed a disease progression model with a Markov structure and simulated a cohort of hypothetical individuals with AF through a standard of care and a CDS strategy. The adherence to anticoagulation in the model was based on the treatment effect reported in the CDS-AF trial, which evaluated the effect of a CDS in patients with AF in the primary care in Östergötland, Sweden. The cost-effectiveness of the CDS-AF intervention compared with standard of care was determined by estimating costs and quality-adjusted life years (QALYs) gained over a lifetime time horizon and was reported as an incremental cost-effectiveness ratio (ICER) assessed against a decision-threshold of €50 000. Uncertainty was evaluated using both one-way and probabilistic sensitivity analysis (PSA). The CDS-intervention resulted in fewer ischaemic strokes but more bleedings. The mean per patient gain in QALYs was 0.012 and the ICER was €963 per QALY gained. The result of the PSA indicated a high probability that the ICER was below €50 000.

Conclusion: The CDS intervention used in the CDS-AF trial appears to yield health gains at a lower cost than typically considered cost-effective.

Trial registration: NCT02635685.

Hypertrophic cardiomyopathy (HCM) is a heterogeneous disease where, despite recent advances, accurate diagnosis, risk stratification, and personalized treatment remain challenging. Artificial intelligence (AI) offers a transformative approach to HCM by enabling rapid, precise analysis of complex data. This article reviews the current and potential applications of AI in HCM. AI enhances diagnostic accuracy by analysing electrocardiograms, echocardiography, and cardiac magnetic resonance images, differentiating HCM from other forms of left ventricular hypertrophy, identifying subtle phenotypic variations, and standardizing myocardial fibrosis assessment. Multimodal AI-driven approaches improve risk stratification, therapeutic decision-making, and monitoring of both established and novel therapies, such as cardiac myosin inhibitors. Emerging AI-driven in silico trials and digital twin platforms highlight the potential of combining data-driven and knowledge-based AI with biophysical models to simulate patient-specific disease trajectories, supporting preclinical evaluation and personalized care. As a multidisciplinary case study, the SMASH-HCM consortium is presented to illustrate how digital twin technologies and hybrid modelling can bring AI into clinical practice. Integration of genetic data further enhances AI's ability to identify at-risk individuals and predict disease progression. However, widespread AI applications raise concerns regarding data privacy, ethical considerations, and the risk of biases. Guidelines for researchers and developers-e.g. on trustworthy AI, regulatory frameworks, and transparent policies-are essential to address these possible pitfalls. As AI rapidly evolves, it has the potential to revolutionize drug discovery, disease management, and the patient journey in HCM, making interventions more precise, timely, and patient-centred.

Aims: Among patients with acute pulmonary embolism (PE), rapid identification of those with highest clinical risk can help guide life-saving treatment. However, current risk stratification algorithms involve a multistep process requiring physical exam, imaging, and laboratory results. We investigated the utility of electrocardiogram (ECG) alone to rapidly identify patients at elevated clinical risk by developing and validating a feature-based artificial intelligence (AI) model to predict clinical risk.

Methods and results: Patients who were diagnosed with PE over a 9-year period, had an ECG within 1 day of presentation, and were evaluated by our PE response team (PERT) were included. A feature-based random forest model was trained to predict the PERT team's risk stratification from the ECG alone. The ability of the model to predict the clinical risk categorization and the accuracy of both risk stratification approaches in predicting mortality were examined on a holdout test set. Of the overall cohort of 1376 patients, 55% had a submassive (intermediate risk) or massive (high risk) PE, which were grouped together as 'severe PE'. The AI-ECG model was able to predict the clinical classification (low-risk vs. severe PE) with an AUC of 0.83 and F1 score of 0.78 in a holdout test set. A 30-day mortality and in-hospital mortality were significantly different between patients classified by the model as low vs. elevated risk.

Conclusion: AI-based analysis of 12-lead ECGs may provide a useful tool in the risk stratification of PE, allowing for rapid identification and treatment of those at highest risk of adverse outcomes.

Introduction: Sensor placement, activity type influencing wrist movements, and individual characteristics impact accuracy of wrist-worn photoplethysmography (PPG)-based heart rate (HR) monitors. This study investigated technical interventions to optimize PPG accuracy in patients with cardiac disease.

Methods and results: The Fitbit Inspire 2 PPG monitor was evaluated across three cohorts, using a Polar H10 chest strap as reference: (ⅰ) 10 healthy volunteers performed wrist movements with the monitor placed one or three fingers above the wrist to identify optimal placement; (ⅱ) 10 volunteers engaged in sport activities (walking, running, cycling, rowing); (ⅲ) 30 cardiac rehabilitation patients were monitored during exercise to assess baseline accuracy. Patients with low accuracy [mean absolute percentage error (MAPE) < 10% for <70% of training time] underwent technical interventions (sensor cleaning, forearm shaving, position fixation, and/or relocation to the volar wrist). Placement three vs. one fingers above the wrist was significantly more accurate (mean difference in MAPE: -11.4%, P < 0.001). Walking showed the highest accuracy (MAPE = 3.8%), followed by cycling (MAPE = 6.9%) and running (MAPE = 8.5%), while rowing had the lowest accuracy (MAPE = 13.4%, P < 0.001). Among CR patients, 66.7% achieved high baseline accuracy. Technical interventions improved accuracy in 50.0% of those with low baseline accuracy, but no significant predictors of optimization success were identified.

Conclusion: Accurate PPG-based monitoring requires a sensor placed higher on the wrist. Nevertheless, only two-thirds of patients are suitable for such monitoring, with improvement by technical adaptations possible (but impractical) in the others. Therefore, assessing baseline accuracy is a prerequisite before relying on these devices for activity guidance.

Aims: The role of temporary mechanical circulatory support (tMCS) after out-of-hospital cardiac arrest (OHCA) remains controversial. This study evaluates machine learning (ML) models for predicting mortality and neurological outcomes, highlighting their potential as a tool to guide early tMCS decision-making.

Methods and results: This retrospective study analysed five years of data from 564 adult non-traumatic OHCA patients treated at Marburg University Hospital. Four ML models (ANN, SVM, RF, XGBoost) were trained to predict in-hospital mortality and neurological outcome based on demographic, clinical, and treatment-related variables. Feature selection and SHAP analysis were used to optimize performance and identify patients potentially benefiting from tMCS. Overall, 144 patients (31.2%) out of 461 patients who fulfilled the inclusion criteria received tMCS: 39 left-ventricular microaxial flow pump, 76 venoarterial extracorporeal membrane oxygenation (VA-ECMO), and 29 biventricular support (ECMELLA). In 69 patients (14.9%) VA-ECMO implantation was performed as part of extracorporeal cardiopulmonary resuscitation. The survival rate of the tMCS group was 34.7% (50/144) compared to 52.7% (167/317) in the non-tMCS group. The highest predictive power for survival probability (with/without tMCS) could be achieved by XGBoost and RF when applied to the non-tMCS group. Machine learning identified 2.5% of non-tMCS patients likely to survive if treated with tMCS. In 23 (RF model) and 31 (XGBoost model) patients, the probability of survival increased by at least 5% with tMCS compared to their predicted outcome without tMCS. RF slightly outperformed XGBoost [area under the receiver operating characteristic curve (AUC) 0.85 vs. AUC 0.82].

Conclusion: XGBoost and RF models accurately predict mortality and tMCS benefit in OHCA patients, supporting ML-based personalized therapy.

Aims: Heart failure with preserved ejection fraction (HFpEF) is difficult to diagnose due to the lack of a definitive diagnostic marker; multiple tests are required, including advanced evaluations. This study aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model for predicting HFpEF.

Methods and results: This retrospective cohort study included patients from a single tertiary centre who underwent echocardiography, N-terminal prohormone of B-type natriuretic peptide measurement, and ECG within a defined timeframe. Patients were classified as HFpEF (HFA-PEFF score ≥5) or control (HFA-PEFF score <5). Patients were divided into training, validation, and test subsets at a 7:1:2 ratio for model development and validation. Using the collected ECGs, a convolutional neural network was trained to predict HFpEF; its performance was assessed using the area under the receiver operating characteristic curve (AUROC). Among the 13 081 patients included, 5795 (44.3%) were classified as HFpEF and 7286 (55.7%) were classified as control. The AI-enabled ECG model demonstrated good discriminative performance [AUROC 0.81; 95% confidence interval (CI) 0.79-0.82]. Subgroup analyses stratified by HFpEF risk factors confirmed consistent model performance. Prognostic evaluation revealed that patients with a positive AI-ECG classification experienced significantly worse outcomes relative to those with a negative classification, including higher risks of cardiac death (1.1% vs. 0.1%; hazard ratio 9.56; 95% CI 1.24-73.53; P = 0.030) and heart failure hospitalization (2.8% vs. 0.6%; hazard ratio 5.91; 95% CI 2.08-16.81; P = 0.001) at 5 year.

Conclusion: The AI-ECG model is a reliable tool for predicting HFpEF, as defined by the HFA-PEFF score, and effectively stratifies patients according to prognosis. Integration of this model into clinical practice may simplify and enhance the diagnostic process for HFpEF.