European heart journal. Digital health最新文献

Aims: Cardiac rehabilitation (CR) programmes are effective, but they are underutilized. Digitally enabled CR programmes (DeCR) offer alternative means of healthcare delivery. We aimed to assess the effects of a DeCR programme on cardiovascular risk factors and healthcare utilization.

Methods and results: In this observational cohort study that used propensity score matching, privately insured Australian patients, recruited nationally following a cardiac hospitalization, were given a digital app and received weekly telehealth consultations. Risk factors were assessed before and after the intervention. Propensity scoring methods were used to compare differences in 30-day, 90-day, and 12-month rehospitalizations, hospital-days, and mortality rates in the DeCR group with patients who undertook: (i) usual care (n = 266) or (ii) face-to-face CR (F2F-CR, n = 115). Overall, 172 intervention patients (70% men, age 68 ± 10 years, 36% living in regional/remote areas) were enrolled (59% agreed to participate and 91% completed final follow-up). The DeCR group had significant improvements in most risk factors. Rehospitalization and mortality rates were similar between the DeCR group and both comparison groups at all time points (all P > 0.05). Patients in the DeCR group spent significantly fewer days in hospital compared with usual care within 30-days (P = 0.026), 90-days (P = 0.003), and 12-months (P = 0.04) post-discharge. Cardiac-related rehospitalization bed days were reduced at 30-days (P = 0.005) and 90-days (P = 0.017) but not 12-months (P = 0.20). There were no group differences between DeCR and F2F-CR across any outcomes (all P > 0.05).

Conclusion: DeCR was associated with lower healthcare utilization than usual care, yet comparable compared with F2F-CR. DeCR represents a suitable option for cardiac patients post-discharge.

Lay summary: We investigated whether a digitally enabled cardiac rehabilitation (DeCR) programme, delivered to patients following a heart disease hospitalization, improved patients' cardiovascular disease risk factors and whether they had a reduction in rehospitalizations, spent fewer days in hospital and improved survival compared with matched controls who undertook either face-to-face cardiac rehabilitation (F2F-CR) or usual care.• DeCR was associated with similar healthcare utilization outcomes compared with F2F-CR. This suggests that the potential benefits of DeCR may be comparable. Additionally, DeCR programmes create an opportunity for patients to choose the style of CR to undertake and have an advantage of broader access.• The DeCR group spent significantly fewer readmission days in hospital compared with the usual care group, which may reflect differences in the nature of rehospitalizations when they occur.

Aims: Hypertension is a leading cause of death worldwide, yet many hypertensive cases remain undiagnosed. Wearable, cuffless blood pressure (BP) monitors could be deployed at scale, but their accuracy remains undetermined.

Methods and results: This study validated a popular consumer-grade wearable BP monitor (W-BPM, Aktiia), using a medical-grade ambulatory device (A-BPM, Mobil-O-Graph), for reference. Thirty-one participants (aged 19-62 years, 17 (55%) females, in office BP 121 ± 15 over 77 ± 12 mmHg) simultaneously wore both devices for 24 h. Systolic BP (SBP), diastolic BP (DBP), and heart rate (HR) were measured in pre-set intervals by the A-BPM and at rest by the W-BPM. Agreement was assessed using standard methods. Accuracy in identifying high BP (mean 24 h SBP/DBP > 130/80 mmHg) was assessed. Compared to A-BMP, mean SBP and DBP tended to be slightly lower during the day and not significantly different at night. Nocturnal BP dipping and BP variability were significantly underestimated by the W-BPM. Agreement between the two devices was poor to moderate (limits of agreement of about -30/+30 mmHg for SBP and -20/+15 mmHg for DBP, correlation coefficients between 0.20 and 0.42). Sensitivity and specificity for high BP detection were around 50% and 80%, respectively. Limiting the analysis to measures taken in similar conditions (within 10 min and with HR within ±10 b.p.m.) did not improve agreement.

Conclusion: Low agreement suggests that the cuffless device may not be a suitable replacement for standard 24 h cuff-based ambulatory monitoring. Further data are required to assess the clinical role of cuffless BP monitors.

Aims: Despite advancements in disease-modifying therapies, the rate of hospitalizations in patients with heart failure (HF) remains high, with an increased risk of future adverse events and healthcare costs. In this context, the AZIMUTH study aims to evaluate the large-scale applicability of a smartphone app-based model of care to improve the quality of care and clinical outcomes of HF patients.

Methods and results: The AZIMUTH trial is a multicentre, prospective, pragmatic, interventional, single-cohort study enrolling HF patients. Three hundred patients will be recruited from four different sites. For comparative analyses, historical data from participating hospitals for the 6 months before enrolment and propensity-matching score analyses from GENERATOR HF DataMart, will be used. The estimated duration of the study is 6 months. During the whole observational period, the patients are asked to provide information regarding their clinical status, transmit remote clinical parameters, and periodically answer validated questionnaires, the Kansas City Cardiomyopathy Questionnaire Health and Morisky Medication Adherence Scale 8-item, on a mobile application, through which healthcare providers implement therapeutic adjustments and remote clinical assessments. The primary objective of this study is to evaluate the feasibility, usability, and perceived benefits for key stakeholders (patients and clinical staff) of the AZIMUTH digital platform in the enrolled patients when compared to standard of care. Secondary endpoints will be the description of the rate of hospital readmissions, ambulatory visits and prescribed therapy in the 6 months following enrolment in the experimental group compared to both the historical and propensity-matched cohorts.

Conclusion: The AZIMUTH aims to enhance HF management by leveraging digital technologies to support the care process and enhance monitoring, engagement, and personalized treatment for HF patients.

Aims: Synthetic electrocardiograms (ECGs) for inherited cardiac diseases may overcome the issue related to data scarcity for artificial intelligence (AI)-based algorithms. This study aimed to evaluate experienced cardiologists' ability to differentiate synthetic and real Brugada ECGs.

Methods and results: A total of 2244 ECG instances (50% synthetic generated by a generative adversarial network, 50% real Brugada patients' ECGs) were evaluated by 7 cardiologists, each with >15 years of experience. All ECGs were standard 12-lead recordings acquired with identical settings (paper speed 25 mm/s, amplitude 10 mm/mV) and randomly assigned without identifying markers. The examination was blinded and conducted in 2 rounds with at least 2 h gap between rounds to assess potential learning effects and intra-rater reliability. Each physician classified the recordings as 'real' or 'synthetic' without having any additional information. Performance metrics, including accuracy, sensitivity, specificity, and intra-rater reliability (Cohen's Kappa), were analyzed. Brugada syndrome (BrS) specialists' repeated evaluations were characterized by low accuracy (first round 40%, second round 42%), specificity (first round 22%, second round 26%) and sensitivity (first round 58%, second round 58%). Intra-rater reliability varied widely (Cohen's Kappa: -0.12 to 0.80).

Conclusion: Synthetic Brugada ECGs cannot be adequately distinguished from real patients' ECGs by BrS specialists.

The European Union's Artificial Intelligence Act (AI Act), published in July 2024, is a pioneering horizontal regulatory framework aimed at ensuring the ethical and safe integration of AI technologies across sectors, including healthcare. While it offers the potential to improve patient care and drive innovation, it also presents challenges for healthcare providers, such as identifying high-risk applications, ensuring transparency in algorithms, and mitigating data bias. However, there are several challenges in its implementation. These include unclear guidance for certain technologies, the need to ensure fairness for diverse patient populations, effective monitoring of AI performance after deployment, and clarifying responsibility in cases of errors. Additionally, varying levels of resources among EU countries may lead to inconsistent implementation of the regulations. This article explores the core elements of the AI Act and its relevance to cardiology and identifies key gaps and unanswered questions that need to be addressed to effectively advance AI-driven medical practices.

Aims: To develop a deep-learning-based system for recognition of subclinical atherosclerosis on a plain frontal chest X-ray.

Methods and results: A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest X-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients [58.4% male, median age 63 (51-74) years] with available paired chest X-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months. The CAC score calculated on chest CT was used as ground truth. The model was validated on a temporally independent validation cohort of 90 patients from the same institution (external validation). The diagnostic accuracy of the AI-CAC model assessed by the area under the curve (AUC) was the primary outcome. Overall, median AI-CAC score was 35 (0-388) and 28.9% patients had no AI-CAC. AUC of the AI-CAC model to identify a CAC >0 was 0.90 (95%CI 0.84-0.97) in the internal validation cohort and 0.77 (95%CI 0.67-0.86) in the external validation cohort. Sensitivity was consistently above 92% in both cohorts. In the overall cohort (n = 540), among patients with AI-CAC = 0, a single ASCVD event occurred, after 4.3 years. Patients with AI-CAC > 0 had significantly higher Kaplan Meier estimates for ASCVD events (13.5% vs. 3.4%, log-rank = 0.013).

Conclusion: The AI-CAC model seems to accurately detect subclinical atherosclerosis on chest X-ray with high sensitivity, and to predict ASCVD events with high negative predictive value. Adoption of the AI-CAC model to refine CV risk stratification or as an opportunistic screening tool requires prospective evaluation.

Aims: This study aimed to test whether seismocardiography (SCG) can detect changes in loading conditions and detect significant differences in SCG signals between healthy individuals and those with cardiovascular disease (CVD).

Methods and results: Twenty-six subjects (age 45 ± 16 years and 77% male) were included, 11 healthy subjects and 15 subjects with CVD. SCG was compared with transthoracic echocardiography (TTE) before and after infusion of 2 L of isotonic saline. Nine subjects (34%) with CVD did not tolerate the full infusion (2 L infusion intolerant), while the remaining 17 subjects (2 L infusion tolerant) successfully completed the infusion. Significant changes in SCG measurements were observed after infusion, including amplitudes Ls (19%, P = 0.015), Dd (23%, P = 0.016), and Ed (48%, P < 0.001) as well as most time intervals. TTE measurements also showed post-infusion changes in stroke volume (15%, P = 0.038), mitral annular velocity (7%, P = 0.013), left ventricular ejection time (1%, P = 0.035), and global longitudinal strain (6%, P = 0.003). Although SCG did not detect differences between the healthy and CVD groups, the diastolic amplitude Cd-Dd significantly differed between the infusion tolerant and intolerant groups (pre-infusion: 7.7 vs. 3.7 mg, P = 0.046; post-infusion: 8.3 vs. 4.1 mg, P = 0.034).

Conclusion: SCG can detect changes in pre-load in both healthy subjects and subjects with CVD. SCG were also able to detect differences in SCG diastolic amplitudes between infusion-tolerant and -intolerant subjects, which may indicate ability to detect diastolic dysfunction and differences in left ventricular filling pressures.

Aims: Discharge education reduces recurrent cardiac events in people after acute coronary syndrome (ACS). This trial investigates the effectiveness of a self-administered avatar-based discharge education application (app) on knowledge and clinical outcomes among inpatients compared with usual care.

Methods and results: Single-centre randomized controlled trial of adults hospitalized with ACS who were being discharged home. The app addressed heart disease diagnosis, treatment, risk factors, symptoms, and secondary prevention. Primary outcome was heart disease knowledge at three months. Secondary outcomes were quality of life, cardiac rehabilitation attendance, hospital re-presentations, symptom beliefs, physical activity, and smoking status. Satisfaction and app costs were also evaluated. Participants (n = 84) were 86% male and aged 60 ± 11 years. Both groups had improved knowledge and quality of life. There was no difference in knowledge between groups at three months after adjusting for baseline scores [0.88 points, 95% confidence interval (CI) -5.00, 6.76]. Cardiac rehabilitation attendance was 74% and 64% in the intervention and control groups, with no differences between groups (relative risk 1.15, 95% CI 0.87, 1.51). Ninety-two per cent found the app easy to use, but only 50% used the app as anticipated. Economic analysis showed that the intervention was dominant.

Conclusion: In this sample of people with ACS with high cardiac rehabilitation attendance, the app was highly acceptable but did not improve knowledge compared with usual care. Knowledge improved in both groups and may have potential to reduce cost to the health service with the app. Further work should explore the most appropriate target audience for app-based education.

Clinical trial registration: ACTRN12622001436763.

Aims: Autonomic nervous system activation plays an important role in the pathophysiology of atrial fibrillation (AF). It can be determined using heart rate variability (HRV). We aimed to evaluate the feasibility of using photoplethysmography (PPG) recordings for the assessment of the ultra-short-term HRV.

Methods and results: TeleCheck-AF is a structured mobile health approach, comprising the on-demand use of a PPG-based smartphone application prior to a scheduled teleconsultation to ensure comprehensive remote AF management. Participants with at least one PPG recording in sinus rhythm were included to assess resting heart rate, root mean square of successive differences (RMSSD), patient compliance and data consistency. In total, 855 patients [39.4% women] with 13 465 recordings were included. Patient compliance was 95.2% (IQR 76.2-114.3%). Median heart rate per patient was 66.5 (IQR 60.0-74.0) b.p.m., median RMSSD per patient was 40 (IQR 33-50) ms and median recording consistency was ±5.2 (IQR 3.8-7.0) b.p.m. and ±14.8 (IQR 9.3-21.1) ms, respectively. RMSSD was lower in men than women, in patients with CHA₂D₂-VA-Score 0, with a history of AF, and following ablation of AF. Older age and lower body mass index were associated with higher RMSSD.

Conclusion: The ultra-short-term HRV can be determined in 1-min PPG recordings with high user compliance and high inter-recording consistency within a structured mobile health AF management approach. The strategy used in this study may also be feasible for the management of other conditions in which the HRV plays a role for diagnostics and therapy.

Aims: Artificial intelligence (AI)-enhanced 12-lead electrocardiogram (ECG) can detect a range of structural heart diseases (SHDs); however, it has a limited role in community-based screening. We developed and externally validated a noise-resilient single-lead AI-ECG algorithm that can detect SHDs and predict the risk of their development using wearable/portable devices.

Methods and results: Using 266 740 ECGs from 99 205 patients with paired echocardiographic data at Yale New Haven Hospital, we developed AI Deep learning for Adapting Portable Technology in HEART disease detection (ADAPT-HEART), a noise-resilient, deep learning algorithm, to detect SHDs using lead I ECG. SHD was defined as a composite of having a left ventricular ejection fraction of < 40%, moderate or severe left-sided valvular disease, and severe left ventricular hypertrophy. ADAPT-HEART was validated in four community hospitals in USA, and the population-based cohort of ELSA-Brasil. We assessed the model's performance as a predictive biomarker among those without baseline SHD across hospital-based sites and the UK Biobank. The development population had a median age of 66 [interquartile range, 54-77] years and included 49 947 (50.3%) women, with 18 896 (19.0%) having any SHD. ADAPT-HEART had an area under the receiver operating characteristics curve (AUROC) of 0.879 (95% confidence interval, 0.870-0.888) with good calibration for detecting SHD in the test set, and consistent performance in hospital-based external sites (AUROC: 0.852-0.891) and ELSA-Brasil (AUROC: 0.859). Among individuals without baseline SHD, high vs. low ADAPT-HEART probability conferred a 2.8- to 5.7-fold increase in the risk of future SHD across data sources (all P < 0.05).

Conclusion: We propose a novel model that detects and predicts a range of SHDs from noisy single-lead ECGs obtainable on portable/wearable devices, providing a scalable strategy for community-based screening and risk stratification for SHD.