European heart journal. Digital health最新文献

Aims: Patients with primary left ventricular hypertrophy (LVH) often experience a diagnostic delay of several years, largely related to fragmented knowledge among different specialties and the rarity of the conditions. We developed and validated a digital support tool to guide the physician in the differential diagnostic process of patients presenting with primary LVH.

Methods and results: A total of 818 patients with definitive diagnosis of sarcomeric hypertrophic cardiomyopathy (HCM) or one of its phenocopies [479 (62%) males, 48 ± 24 years] were included. Pre-specified disease-specific red flags (RFs) were categorized into five domains: family history, signs/symptoms, electrocardiography, echocardiographic, and laboratory. Each patient's characteristics were inserted by two independent and blind investigators into the app. The diagnostic outcome, based on the presence/absence of RF, was categorized as follows: (i) most likely diagnosis, (ii) possible diagnosis, and (iii) less likely diagnosis. A total of 2979 RFs were identified and non-sarcomeric phenocopies exhibited a higher RF burden than sarcomeric HCM (3.9 vs. 2.7 RFs per patient, P = 0.007), with systemic features and extracardiac findings being strong predictors of non-sarcomeric disease. Thick-Heart App correctly classified 93% of cases into the most likely diagnosis category (sensitivity of 88-100%, specificity 97%). The positive predictive value (PPV) for TTR amyloidosis reached 92%, while Friedrich's ataxia was correctly identified in all cases (PPV = 100%).

Conclusion: The Thick-Heart App correctly classified 93% of cases into the most-likely diagnosis category (sensitivity 88-100%, specificity 97%). Our study underscores the potential clinical value of digital decision support tools to enable timelier identification of specific cardiomyopathies, by promoting awareness in non-reference settings.

Introduction: Integrating machine learning (ML) models into wearable or connected devices to deliver early warning alerts prior to atrial fibrillation (AF) onset may represent an effective preventive strategy. Machine learning algorithms applied to two-lead Holter electrocardiogram (ECG) recordings can support the development of predictive models capable of detecting imminent paroxysmal AF episodes within short-term windows. This approach could facilitate a more targeted 'pill-in-the-pocket' (PITP)-like intervention strategy, potentially enhancing timely therapeutic actions and improving patient outcomes.

Aim: This study aimed to identify patients currently in sinus rhythm who will experience an AF episode within the subsequent hours by analysing 24-h Holter ECG recordings with ML.

Methods: We established a novel database comprising 95 871 manually analysed Holter ECG recordings, identifying 1319 episodes of paroxysmal AF from 872 patients. Among these, 835 AF episodes from 506 recordings had more than 60 min of normal sinus rhythm prior to AF onset and more than 10 min of sustained AF following onset. Patients were stratified into five age groups: all patients combined, under 60 years, 60-70 years, 70-80 years, and over 80 years. Additionally, 365 recordings from 347 patients without rhythm abnormalities were identified and classified, from which two ECG segments were selected. Two deep learning (DL) models were trained on raw ECG data to predict AF onset. To compare DL models with traditional ML approaches using heart rate variability (HRV) parameters, we employed a random forest classifier and a gradient-boosted decision tree model (XGBoost, XGB).

Results: The decision trees models trained on HRV parameters delivered the best predictive performance. The most significant results were observed for episodes lasting more than 5 min of AF, achieving an area under the receiver operating characteristic curve of 0.919 (95% CI: 0.879-0.958) and an area under the precision-recall curve of 0.919 (95% CI: 0.879-0.958) for XGB. At a decision threshold of 0.5, accuracy was 84.5% (81.2-87.8), sensitivity was 83.0% (79.5-86.4), specificity was 86.6% (79.3-93.9), positive predictive value was 90.2% (85.5-94.9), negative predictive value was 78.4% (74.7-82.1), and the F1 score was 86.2% (83.5-89.0).

Conclusion: These findings indicate that HRV parameters contain crucial information for the short-term prediction of AF onset, supporting preventive strategies. Integration of such predictive models into wearable mHealth technologies could facilitate a PITP-like preventive approach, potentially reducing AF-related morbidity. Prospective studies are warranted to validate these promising results further.

Aims: Despite the excellent clinical outcomes of the HeartMate 3 (HM3) left ventricular assist device, the current pump monitoring limits in-depth pump data analysis. This study investigated HM3 pump parameters collected non-invasively with HM3 Snoopy during orthostatic transitions (OTs).

Methods and results: In this single-centre cohort study, a standardized OT protocol was developed, involving postural changes between supine, seated, and standing. Data were recorded using the HM3 Snoopy and a Holter electrocardiogram. Pump flows (Q_MIN, Q_MEAN, Q_MAX), pulsatility index (PI), pump speed, MagLev parameters, and heart rate were synchronized per second. The primary outcome was the identification of distinct orthostatic pump flow response phenotypes. Further, a binary classifier using MagLev parameters, to differentiate between supine and upright patient positions, was developed and assessed. In 25 HM3 patients (age: 61.2 ± 9.6 years, female: 12%, body mass index: 26.8 ± 4.7 kg/m²), greater flow alterations were observed during transitions from supine to standing vs. seated to standing, with most significant changes in Q_MIN [3 (-13; 10)%]. Phenotypes were identified across 75 OTs as no flow response (60%), undesired unloading with a loss in Q_MIN ≥ 50% (20%), and loss of pulsatility ≥ 50% (16%). The MagLev patient position classifier achieved a median sensitivity of 88% and specificity of 86% across the entire cohort.

Conclusion: Three HM3 pump flow response phenotypes were identified in response to OTs, challenging the utilization of PI events to detect undesired unloading events. A MagLev-based position classifier revealed potential for differentiation of HM3 patient position.

Aims: Effective management of emergencies, particularly acute coronary syndrome (ACS), demands rapid, guideline-based interventions to optimize outcomes. However, many medical students and young professionals report feeling unprepared due to limited hands-on experience. Virtual reality (VR) presents a promising training tool, though its efficacy remains unproven.

Methods and results: In this single-center study, 247 medical students were assigned to three different guidance modes to manage a virtual ST-elevation myocardial infarction patient using the Simulation-based Training of Emergencies for Physicians using Virtual Reality (STEP-VR) application. A pre-post-test design, based on European Society of Cardiology (ESC) guidelines, was used to evaluate learning outcomes. Our results showed a significant increase in knowledge after the training. Students in the tutor-moderated 'human guidance' group demonstrated the greatest knowledge improvement ( $M=+24\text{\%},\mathrm{SD}=13\text{\%}$ ), being significantly better than the 'no guidance' group ( $M=14\text{\%}$ , $\mathrm{SD}=9\text{\%}$ ). However, there was no significant difference between the 'human guidance' group and the 'integrated guidance' group ( $M=+19\text{\%},\mathrm{SD}=14\text{\%}$ ), which used an embedded learning mode within STEP-VR. To evaluate the potential impact on clinical performance, we calculated composite quality indicators based on ESC-defined metrics. Consistently, we found a significant improvement in these indicators [clinical quality indicators (CQI) 0.47 (pre) vs. 0.76 (post) and 0.8 (post), respectively], with no significant difference between the 'human guidance' and 'integrated guidance' groups.

Conclusion: In conclusion, our findings demonstrate that VR-based acute coronary syndrome/ST-elevation myocardial infarction training is both operationally feasible and educationally effective. Notably, integrated guidance yielded outcomes comparable to tutor-led instruction, underscoring the potential of this approach as a platform for independent, extracurricular learning. While our data suggest VR training may support clinical performance, future studies with objective assessments are needed to confirm its real-world value.

Aims: Knowledge of cardiorespiratory fitness (i.e. VO₂peak) is important for determining prognosis and prescribing exercise for patients with heart disease undergoing cardiac rehabilitation (CR) programmes. In this explorative study, we investigated the accuracy of a novel equation using seismocardiography (SCG) at rest for the estimation of VO₂peak (SCG eVO₂peak) and whether it could detect changes following CR. An interim data analysis was planned after 50% of patients had undergone testing, allowing for adjustment of the SCG eVO₂peak. We compared the SCG eVO₂peak with a cardiopulmonary exercise test (CPET).

Methods and results: We included 125 patients with new-onset ischaemic heart disease (IHD, n = 58) or heart failure with reduced left ventricular ejection fraction (HFrEF, n = 67) from an outpatient CR clinic. Testing included SCG eVO₂peak and CPET measurement. The adjusted SCG 4.7_HD was validated in 30 and 34 patients with IHD and HFrEF, respectively. Forty-four out of the 67 patients with HFrEF were tested after completing 12 weeks of CR. A mean absolute percentage error (MAPE) ≤10% was decided for clinical relevance. The SCG 4.7_HD overestimated VO₂peak (2.1 mL min^-1 kg^-1, P = 0.003) with 95% limits of agreement ranging ±10.9 mL min^-1 kg^-1 when compared with CPET. The standard error of estimation was 6.0 mL min^-1 kg^-1, and MAPE was 29.1%. No correlation was observed between delta SCG 4.7_HD and CPET after CR for patients with HFrEF.

Conclusion: The SCG eVO2peak is not supported for clinical purposes in patients with IHD or HFrEF based on a poor-to-moderate agreement with large estimation errors and the inability to detect changes following CR.

Trial registration: The study is registered at ClinicalTrials.gov (NCT05520307).

Ambulatory cardiac monitoring (ACM) allows long-term electrocardiogram (ECG) monitoring to detect arrhythmias with different modalities, ranging from short-term Holter monitoring (up to 48 h) to long-term continuous patch ECG monitors (up to 14 days), external event recorders (up to 30 days), and implantable loop recorders (ILRs). Access and reimbursement for ACM across Europe are not well understood. We performed a systematic review and analysis to understand ACM reimbursement across Europe, including a review of the reimbursement systems in each country and a detailed inspection of clinical coding and provider reimbursement. Level of reimbursement is dependent on many factors, including clinical setting (inpatient, outpatient, and day case), hospital length of stay, diagnosis, complications/severity, geographical location, hospital type, and device model and manufacturer. In most countries, reimbursement is performed for the monitoring procedure itself, without considering the time extension of monitoring and the specific type of device used for monitoring. The monetary value of reimbursement varies by country for both ACM and ILR [for Holter from €17.49 to €939.78 and for ILR from €416.14 (provider reimbursement only) to €18,718 (provider reimbursement bundled with ILR device)]. Holter and ILR are universally reimbursed, but newer ACM technologies with extended duration of monitoring, including long-term continuous monitoring and event recorders, are not. Across Europe, we found large variation in monetary values for reimbursement for ACM and ILR. We also found limited reimbursement and access to longer-duration ACM technologies. These findings suggest heterogeneous and problematic access to evidence-based tools for longer-duration monitoring.

Patients and healthcare professionals require clinical prediction models to accurately guide healthcare decisions, although an awareness of the limitations of regression-based models has recently increased. Deep learning (DL) has emerged as a promising alternative to traditional regression-based models, due to its ability to effectively analyse heterogeneous types of data, ranging from numerical variables to medical images. Building a DL model presents various challenges, including conceptualizing the clinical problem, selecting appropriate variables and model architecture, and providing explainability. We propose a four-step pipeline for developing DL-based prediction models for cardiac magnetic resonance image analysis. This framework aims to support researchers in exploring DL application across the broad spectrum of cardiology, with a specific focus on advancement in arrhythmic risk prediction. The field of cardiomyopathy faces challenges when assessing arrhythmic risk due to the low accuracy of the current prediction models. Research efforts have focused on developing DL models able to predict major arrhythmic events in dilated cardiomyopathy. While the initial results are promising, further tests are needed before translating these models into clinical practice.

Aims: Familial hypercholesterolaemia (FH) is the most prevalent autosomal dominant disorder, affecting about 1 in 200-250 individuals. It is the leading cause of early and aggressive coronary artery disease.

Methods and results: We analysed patients with genetically confirmed FH or a score >8 on the Dutch Lipid Clinics Network criteria from the National Registry of the Spanish Atherosclerosis Society, including individuals enrolled from January 2010 to December 2017. The model utilized a dataset incorporating family history, clinical characteristics, laboratory results, genetic data, imaging studies, and lipid-lowering treatment details. Eighty per cent of the population was allocated for training the AI algorithm and 20% was used for testing. A Histogram-based Gradient Boosting Classification Tree was used. The stability of the AI system was assessed using K-fold cross-validation. Shapley additive explanations methodology analysed the influence of different variables by sex. Youden's J statistic established the optimal cut-off point. A total of 1764 patients were included (51.8% women), among whom 264 experienced major adverse cardiovascular events (MACEs), with 8% being women. The final model incorporated 82 variables, achieving metrics of precision for MACE accuracy (0.92), recall (0.89), F1-score (0.91), and receiver operating characteristic (0.88; 95% confidence interval, 0.85-0.90). In the model, age, gamma-glutamyl transferase levels, and subclinical disease significantly impacted risk for women, while year of birth, age at initiation of statin treatment, and HbA1c levels were more influential for men. The optimal risk threshold was 0.25.

Conclusion: Artificial intelligence-machine learning algorithms are promising tools for enhancing vascular risk stratification, revealing critical sex-based differences.

Aims: The potential application of wearable technology solutions for detecting out-of-hospital cardiac arrest (OHCA) is increasingly explored to enhance survival outcomes, but questions related to device accuracy, psychological well-being, privacy, and equal access need to be sorted out before implementation in clinical care and society. This qualitative interview study investigates patients' and physicians' perspectives on end-user needs, preferences, and potential barriers to smartwatch-based OHCA detection.

Methods and results: During the first cycle, 17 patients with elevated OHCA risk were interviewed individually (n = 8) or with their partner (n = 9). The second cycle consisted of interviews with 18 physicians, including cardiologists (n = 9), and other physicians involved in the clinical care of OHCA: general physicians (n = 3), intensivists (n = 3), and neurologists (n = 3). Verbatim interview transcripts were inductively coded for thematic analysis. Five overarching themes were derived: (1) acceptance, use, and optimal informed consent; (2) identifying the target population; (3) technology-related barriers, such as false alarms, localization, and locked doors; (4) design preferences related to privacy, comfort, and hardware alternatives; and (5) public-private partnerships, costs, and equitable access.

Conclusion: This study is the first to explore the perspectives of patients and physicians on smartwatch-based OHCA detection using qualitative analysis of interview data. The results provide important building blocks for the ethically and psychologically sound development and implementation of smartwatch-based OHCA detection in clinical practice, taking the social context into account. The availability of OHCA detection using wearable devices to a wide range of people requires further attention, with emphasis on populations at elevated risk of cardiac arrhythmias.

Aims: We aimed to develop and externally validate a convolutional neural network (CNN) using sinus rhythm electrocardiograms (ECGs) and CHARGE-AF features to predict incident paroxysmal atrial fibrillation (AF), benchmarking its performance against the CHARGE-AF score.

Methods and results: We curated 157 192 sinus ECGs from 76 986 patients within the New York University (NYU) Langone Health system, splitting data into training, validation, and test sets. Two cohorts, from suburban US outpatient practices and Greek tertiary hospitals, were used for external validation. The model utilizing the sinus ECG signal and all CHARGE-AF features achieved the highest test set area under the receiver operator curve (AUC) (0.89) and area under the precision recall curve (AUPRC) (0.69), outperforming the CHARGE-AF score alone. Model robustness was maintained in the external US cohort (AUC 0.90, AUPRC 0.67) and the European cohort (AUC 0.85, AUPRC 0.78). Subgroup analyses confirmed consistent performance across age, sex, and race strata. A CNN using ECG signals alone retained strong predictive ability, particularly when simulating missing or inaccurate clinical data.

Conclusion: Our CNN integrating sinus rhythm ECGs and CHARGE-AF features demonstrated superior predictive performance over traditional risk scoring alone for detecting incident paroxysmal AF. The model maintained accuracy across geographically and clinically diverse external validation cohorts, supporting its potential for broad implementation in AF screening strategies.