European heart journal. Digital health最新文献

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.

Heart failure (HF) is a global pandemic and accounts for substantial morbidity and healthcare expenditure, largely due to frequent hospitalizations. While traditionally HF patients are followed with intermittent clinical assessments, wearable technologies offer continuous, real-time monitoring, potentially enabling earlier detection and tailored interventions to prevent hospitalization. This systematic review evaluates the impact of non-invasive wearable devices on hospitalizations in HF. Following PRISMA guidelines, literature searches were conducted in PubMed and Scopus using keywords related to HF, hospitalization, and wearable technology on 1 March 2024, and re-run on 3 December 2024. Studies assessing the link between wearable devices and HF-related hospitalization rates were included. Data extraction covered population characteristics, study design, type of device, and hospitalization rates. Risk of bias was assessed using ROBINS-I and ROB-2 tools. Meta-analysis was attempted but not performed due to significant heterogeneity (I²>90%). From 2247 records, eight studies involving 1823 patients were finally analysed. Devices included ReDS, VitalPatch, ZOLL LifeVest, and ZOLL-HFMS, with follow-up ranging from 30 to 646 days. Wearable devices allowed prediction of HF hospitalization within 6.5-32 days in advance. Wearable-guided therapy compared to traditional assessment showed an 89% relative reduction at 30 days in a single-blind randomized-controlled trial, and 78% and 87% reductions in 30-day and 90-day hospitalization rates in observational studies. Although these data highlight the potential of wearable devices in HF management, future research should test predefined wearable-guided treatment algorithms on strong endpoints and address cost-effectiveness and data security in large randomized-controlled trials with longer follow-up. Registration This review was registered with PROSPERO (CRD42024519282).

Aims: Mitral and tricuspid regurgitation (MR and TR) are common in older adults and associated with substantial morbidity and mortality. While transthoracic echocardiography (TTE) is the diagnostic gold standard, access remains limited in many care settings. Artificial intelligence (AI)-based echocardiographic analysis may help address this diagnostic gap.

Methods and results: We externally validated a deep learning algorithm developed by Aisap.ai using TTE studies from the Mayo Clinic Health System (2013-23). The model analyses echocardiographic images to classify atrioventricular regurgitation severity and was evaluated against cardiologist interpretations. Performance was assessed using binary (normal-mild vs. moderate-severe) and ordinal (normal, mild, moderate, severe) classification schemes. Among 1541 eligible TTEs, the model returned predictions for 578 studies (38%). Performance analysis was limited to these cases. The MR cohort included 280 studies and the TR cohort 298. For MR, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.98 [95% confidence interval (CI): 0.97-0.99], with 91% accuracy, 95% sensitivity, and 89% specificity. For TR, the AUC was 0.96 (95% CI: 0.94-0.98), with 84% accuracy, 91% sensitivity, and 80% specificity.

Conclusion: In cases where a prediction was generated, the model demonstrated high diagnostic performance in identifying clinically significant atrioventricular regurgitation. These findings support the feasibility of AI-assisted echocardiography in diverse populations, while underscoring the need for technical alignment between model requirements and local acquisition practices to ensure real-world applicability.

Aims: Data-driven clustering techniques may improve heart failure (HF) categorisation and provide prognostic insights. The present study aimed to elucidate the underlying pathophysiology of acute HF phenotypes based on pulmonary and systemic congestion at both the tissue (PTC, pulmonary tissue congestion; STC, systemic tissue congestion) and intravascular (PIVC, pulmonary intravascular congestion; SIVC, systemic intravascular congestion) level and to assess the association of identified phenotypes with a composite outcome of HF hospitalisation and death.

Methods and results: Nineteen clinical, laboratory, and echocardiographic congestion markers were analyzed using clustering techniques to identify phenotypes in patients with worsening HF in the Nancy-HF cohort (n = 741), followed by validation of the clustering model in the BIOSTAT-CHF cohort (n = 4254). Network analysis was conducted using 363 proteins to identify underlying biological pathways. Five congestion phenotypes were identified: (1) PTC-dilated left ventricle (LV), (2) PTC-HFpEF, (3) PTC, STC-atrial fibrillation (AF), (4) PIVC-dilated left atrium (LA) and LV and (5) Global congestion. Compared with the 'PTC-dilated LV' phenotype, the risk of composite outcome was higher in 'PTC, STC-AF' and 'Global' congestion phenotypes [adjusted HR: 1.74 (1.13-2.67) and 2.41 (1.60-3.63), respectively]. In BIOSTAT-CHF, 'Global' congestion phenotype was associated with significantly higher risk [HR: 1.64 (1.04-2.58)]. In network analysis, the immune response pathway was linked to all phenotypes. 'PTC-HFpEF' was related to lipid, protein and angiotensin metabolism, 'PTC, STC-AF' was related to kinase-mediated signalling, extracellular matrix organisation and TNF-regulated cell death, while 'PIVC-dilated LA & LV' was related to kinase-mediated signalling and hemostasis.

Conclusion: In worsening HF, clustering techniques identified clinical congestion profiles associated with both long-term clinical risk and differences in biomarkers, suggesting potential different underlying pathophysiologies. These clusters can be applied using the available online model to identify phenotypes as well as associated risks (https://cic-p-nancy.fr/ai-cong-hf/).

Aims: The effectiveness of telehealth care programmes in reducing mortality among patients with chronic conditions has been well established. Valuable insights into patients' conditions can be gleaned through daily telecommunication between patients and nurse case managers. We hypothesized that using natural language processing can predict acute deterioration in patients with chronic conditions in telehealth care programme based on the nursing records and speech dialogues occurring during daily telecommunication.

Methods and results: We conducted a retrospective study utilizing audio recording transcripts from telecommunication sessions between patients and nurse case managers at our telehealth care centre, along with nursing notes as input data. Pre-trained transformer-based neural network models were constructed to predict emergency room (ER) visits within a 2-week timeframe. The case group included 94 patients with 585 speech recordings and nursing records, while the control group included 36 patients with 396 speech recordings and nursing records. Our results showed that employing transcripts and a bidirectional encoder representations from transformers (BERT)-base model with a sliding window for predicting ER visits yielded moderate accuracy 0.75 (interquartile range: 0.742, 0.773). The inclusion of long short-term memory in the model did not significantly enhance accuracy. Notably, combining nursing records and transcripts as inputs exhibited superior performance, achieving an overall accuracy of 0.892 (interquartile range: 0.891, 0.893) by the six models.

Conclusion: Our study demonstrates the feasibility of predicting ER visits using telehealth dialogue transcripts and nursing notes with pre-trained transformer models. The incorporation of nursing notes significantly enhances the model's performance, providing a valuable method for improving predictive accuracy in telehealth care.

Aims: To assess the performance of an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm in identifying patients with moderate to severe aortic stenosis (AS) in an Asian cohort from a tertiary care centre.

Methods and results: We identified a randomly selected patients ≥60 years old who underwent echocardiography and ECG within in 31 days between 2012 and 2021 at the Samsung Medical Center in Korea. Patients with previous cardiac surgery, prosthetic valves, or pacemakers were excluded. The AI-ECG model, originally developed and validated by Mayo Clinic in the USA, was applied without fine-tuning. Performance metrics, including the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, were calculated to compare AI-ECG predictions with TTE-confirmed AS status. Among 5425 patients, 1095 had moderate to severe AS, and 4330 age- and sex-matched patients without AS were included as controls. The AI-ECG model achieved an AUC of 0.85 (95% CI: 0.84-0.87) in detecting moderate to severe AS. Sensitivity, specificity, PPV, NPV, and accuracy were 0.83, 0.65, 0.37, 0.94, and 68.29%, respectively. The model's performance was consistent across various age and sex subgroups, with sensitivity increasing in older patients.

Conclusion: The AI-ECG model developed in the USA demonstrated comparable performance in detecting moderate to severe AS in an Asian cohort compared with its original validation population. These findings highlight the potential utility of AI-ECG as a non-invasive screening tool for AS across diverse patient populations.

Aims: Assessing myocardial fibrosis (MF) in patients with prior myocardial infarction (MI) is crucial for prognosis. Artificial intelligence-assisted electrocardiography (AI-ECG) has a great potential to detect MF. However, training a precise AI-ECG model requires voluminous ECGs. A biosimulation model may be an efficient substitution. This study aimed to develop and validate a novel artificial intelligence-assisted method using 12-lead electrocardiography (AI-MI-12ECG).

Methods and results: The AI-MI-12ECG was trained by a biosimulation model to visualize the presence, location, and size of MF in post-MI patients. A total of 182 post-MI patients were included in this prospective study. The MF detected by AI-MI-12ECG and the cardiologist were compared with the late gadolinium-enhanced (LGE) area of cardiac magnetic resonance (CMR). The results show that AI-MI-12ECG exhibited strong correlation with LGE in identifying the MI location (R = 0.955). Compared with CMR-LGE, AI-MI-12ECG achieved receiver operating characteristic curves of 0.95, 0.95, and 0.89 for left anterior descending coronary artery (LAD), right coronary artery (RCA), and left circumflex coronary artery (LCX) territories, respectively, with high accuracies for LAD (0.95), RCA (0.97), and LCX (0.91).

Conclusion: The AI-MI-12ECG trained using the biosimulation model in post-MI patients was adequately aligned with CMR-LGE. This highlights its potential for accurate detection of fibrosis and identification of individuals with significant infarct burdens.

Aims: Personalized risk assessment tools (PRTs) are recommended by cardiovascular guidelines to tailor prevention, diagnosis, and treatment. However, PRT implementation in clinical routine is poor. ACRIBiS (Advancing Cardiovascular Risk Identification with Structured Clinical Documentation and Biosignal Derived Phenotypes Synthesis) aims to establish interoperable infrastructures for standardized documentation of routine data and integration of high-resolution biosignals (HRBs) enabling data-based risk assessment.

Methods and results: Established cardiovascular risk scores were selected by their predictive performance and served as basis for building a core cardiovascular dataset with risk-relevant clinical routine information. Data items not yet represented in the Medical Informatics Inititative (MII) Core Dataset (CDS) FHIR profiles will be added to an extension module 'Cardiology' allowing for maximum interoperability. HRB integration will be implemented at each site through a modular infrastructure for electrocardiography (ECG) processing. Predictive performance of PRTs and their dynamic recalibration through HRB integration will be evaluated within the ACRIBiS cohort consisting of 5250 prospectively recruited patients at 15 German academic cardiology departments with 12-month follow-up. The potential of visualising these risks to improve patient education will also be assessed and supported by the development of a self-assessment app.

Discussion: The ACRIBiS project presents an innovative concept to harmonize clinical data documentation and integrate ECG data, ultimately facilitating personalized risk assessment to improve patient empowerment and prognosis. Importantly, the consensus-based documentation and interoperability specifications developed will support the standardisation of routine patient data collection at the national and international levels, while the ACRIBiS cohort dataset will be available for broad secondary use.

Trial registration: The study is registered at the German study registry (DRKS): #DRKS00034792.