European heart journal. Digital health最新文献

Aims: Automatic echocardiographic measurements using artificial intelligence have shown promising results; however, they have not been compared with manual measurements regarding heart failure (HF) progression and algorithm runtime.

Methods and results: Data came from the prospective HF study MyoVasc (NCT04064450), which involved a highly standardized 5-h examination, including comprehensive echocardiography, at a dedicated study centre between January 2013 and April 2018. Worsening of HF was a primary composite endpoint, recorded by structured follow-up, death certificates, and medical records. The automated assessment was performed using EchoDL, eight 3D convolutional neural networks (CNNs) trained to predict clinical parameters. Manual and automatic left ventricular ejection fraction (LVEF), E/E'-ratio and left ventricular mass (LVM) demonstrated a good intraclass correlation coefficient {LVEF: 0.75 [95% confidence interval (CI) 0.75-0.77], E/E'-ratio: 0.59 [CI 0.56-0.61], LVM: 0.64 [CI 0.62-0.66]}. After a median follow-up of 3.8 years (IQR 2.1-5.0), 470 patients experienced worsening of HF. In multivariable Cox analysis, comparison of manually and automatically assessed LVEF, E/E'-ratio and LVM demonstrated risk estimates slightly in favour of the CNNs. Direct comparison of C-indices showed significantly better model performance for automatically determined LVEF (0.71 vs. 0.73, P = 0.038) and E/E'-ratio (0.64 vs. 0.66, P = 0.013) and a trend for LVM (0.66 vs. 0.68, P = 0.063). Echo-DL required an average of 1053.4 ms (95% CI 1050.7-1056.0) to analyse a four-second-long echocardiogram.

Conclusion: Automated analysis of echocardiograms using 3D CNNs was comparable to manual measurements in predicting HF-specific outcomes. Echo-DL offers potential time savings and improved risk prediction in clinical settings, allowing integration into echocardiographic hardware.

Aims: Epicardial adipose tissue (EAT), located within the pericardial sac, has emerged as a biomarker for coronary artery disease (CAD) progression. This study aimed to develop and validate a deep learning-based system for automated EAT volume quantification using non-contrast computed tomography (NCCT) scans from a large, multi-centre, pan-Asian cohort.

Methods and results: A total of 1243 NCCT patient scans from three centres were used to train and internally validate a deep learning model based on 3D UNet++ architecture for pericardium segmentation, followed by intensity thresholding to derive EAT volume. Epicardial adipose tissue quantification required ∼30 s per scan. The final model was evaluated on an external testing cohort of 160 patients, including 90 non-Asian individuals. In this cohort, AI-predicted EAT volumes showed excellent agreement with expert annotations (r = 0.975; P < 0.0001). The Bland-Altman analysis demonstrated a mean bias of -5.2 cm³with 95% limits of agreement from -25.1 to 14.7 cm³. Among the non-Asian subgroup, model performance remained strong (r = 0.970; bias, -3.2 cm³; limits of agreement, -25.1-18.7 cm³). AI-derived EAT volume was independently associated with obstructive CAD (odds ratio 1.11; 95% confidence interval, 1.04-1.19; P = 0.004), after adjusting for confounders. The global χ² statistic increased from 81.7 with coronary calcium score alone to 93.3 when EAT volume was added (P = 0.001), indicating improved risk prediction.

Conclusion: We developed and validated a deep learning system for automated EAT volume quantification from NCCT scans. The model demonstrated high accuracy and generalizability across ethnically diverse populations, supporting its potential for routine EAT assessment and CAD risk stratification.

Trial registration: ClinicalTrials.gov Identifier: NCT05509010.

Aims: We aimed to compare performances of conventional survival models with machine learning (ML) survival models for incident heart failure (HF) in men and women without prevalent HF, cardiomyopathy (CM) or ischaemic heart disease (IHD), and to identify potential high-risk precursors overlooked by conventional survival models.

Methods and results: We predicted 10-year risk of incident HF in 266 306 women (2894 events) and 212 061 men (4213 events). We constructed multivariable Cox models, first using ∼ 400 baseline characteristics, and subsequently only those remaining after LASSO stability selection. We also used Random Survival Forest (RSF) and eXtreme Gradient Survival Boosting (XGBoost). Performances were assessed using internal cross validation and hold-out sets, with C-indices, calibration curves and net-benefit analyses. Model performances were comparable during internal validation: XGBoost (C-index ± SE) (men: 0.79 ± 0.0040, women: 0.83 ± 0.0023) showed similar performance to the multivariable Cox model (men: 0.80 ± 0.0031, women: 0.83 ± 0.0022) and Cox models after LASSO stability selection, while RSF showed numerically slightly lower performance (men: 0.78 ± 0.0025, women: 0.81 ± 0.0015). Findings were similar in the hold-out sets. Age, cystatin-C, lifetime treatments/medications, other heart disease, systolic blood pressure, and spirometry measures were identified as high-risk factors in both model types for both sexes. Additionally, sex-specific and model-specific risk factors were identified.

Conclusion: Machine learning models and Cox proportional hazard models performed well and similarly for 10-year incident HF risk prediction in the general population. However, sex-specific and model-specific risk predictors were found. Spirometry measures, rarely included in existing models, were identified as important risk factors. Our results suggest that ML models for HF prediction in the general population reveal insights that would otherwise remain unnoticed.

Aims: To assess the potential for artificial intelligence-enabled electrocardiogram (AI-ECG) to serve as a long-term cardiac surveillance tool and predict left ventricular systolic dysfunction in childhood cancer patients.

Methods and results: We assessed performance of our previously established AI-ECG model to predict left ventricular ejection fraction (LVEF) ≤50% and ≤40% in patients with childhood cancer during internal testing (Boston Children's Hospital) and external validation (Children's Hospital of Philadelphia). The internal test cohort comprised 447 patients [57% male; age at cancer diagnosis 11.2 (5.4-15.7) years; 1553 ECG-echo pairs at median age 13.5 (IQR 7.7-17.9) years; 6.4% with LVEF ≤50%; 1.3% with LVEF ≤40%], 28% with leukaemia, 16% with lymphoma, 8% with neuroblastoma, 8% with sarcoma, 2% with gastrointestinal cancers, 3% with genitourinary cancers, 6% with central nervous system cancers, 11% with other/unspecified cancers, and 18% with missing/unknown cancer labels. Treatment strategies included anthracyclines (35%), bone marrow transplant (7%), and radiation (1%). The external test cohort comprised 2964 patients [55.4% male; 7054 ECG-echo pairs at median age 11.6 (IQR 6.8-15.1) years; 2.5% with LVEF ≤50%; 0.9% with LVEF ≤40%]. Similar AUROCs (0.80-0.85), sensitivities (0.75-0.82), NPVs (0.986-0.996), and percent predicted negative (51-65%) were obtained across institutions to predict LVEF ≤50%, outperforming a biomarker-based model benchmark. Patients with high AI-ECG risk scores for LVEF ≤50% had higher rates of mortality [hazard ratio 3.1 (95% CI 1.8-5.3), P < 0.001] compared to patients with low AI-ECG risk scores.

Conclusion: AI-ECG shows promise as a digital biomarker for cardiac surveillance in the vulnerable childhood cancer survivor population.

Aims: Overdiagnosis in patients suspected of acute coronary syndrome (ACS) leads to unnecessary coronary angiographies, particularly in cases with non-specifically elevated troponin (Trop) levels. We established machine learning (ML) models integrating sequentially available prehospital and in-hospital variables to improve early prediction of the need for coronary re while minimizing overdiagnosis.

Methods and results: Retrospective cohort study analysing patients with suspected ACS from 2016 to 2020. Machine learning models were trained using data available at different diagnostic time points, including prehospital assessment, arterial blood gas analysis, full laboratory results, and sequential Trop measurements. A total of 2756 patients were included, identified through emergency physician protocols for ACS-related complaints. Patients with incomplete data or prehospital mortality were excluded.Model performance improved with additional diagnostic data. Model 1 (prehospital data only) achieved an area under the receiver operating characteristic (AUROC) of 0.76 (95% confidence interval [CI] 0.72-0.79), while Model 4 (including sequential Trop testing) reached 0.87 (95% CI 0.83-0.91). Adding early hospital diagnostics (Model 2) significantly improved accuracy compared with Model 1 (0.65 vs. 0.78). Sequential Trop testing in Model 4 did not substantially enhance performance compared with single Trop testing in Model 3 (AUROC 0.87, 95% CI 0.83-0.91 vs. 0.86, 95% CI 0.82-0.91). Misclassification analysis revealed that underdiagnosed patients were typically older females with dyspnoea and known coronary artery disease but no ST-elevations. Overdiagnosed patients had higher body mass index, ST-elevations, regional wall motion abnormalities, and impaired left ventricular ejection fraction but lacked significant sequential Trop elevation.

Conclusion: Prehospital assessments combined with early in-hospital diagnostics provide reliable stratification of coronary intervention need, potentially optimizing clinical decision-making and resource utilization.

Aims: Artificial Intelligence (AI) models applied to standard 12-lead ECGs enable estimation of biological age (AI-ECG age), which has shown prognostic value in general populations. However, its clinical utility in high-risk patients with cardiovascular disease (CVD) or acute medical conditions remains insufficiently explored.

Methods and results: We analysed the first ECG of 48 950 consecutive patients presenting to a tertiary care centre with CVD or acute illness between 2000 and 2021. AI-ECG age was derived using a validated deep learning model. Δ-age, defined as the difference between AI-ECG and chronological age, was analysed categorically (±8 years) and continuously using multivariable Cox models adjusted for clinical and ECG variables. Primary endpoint was long-term total mortality (up to 10 years). Saliency map analysis was performed to identify input regions that the model was most sensitive to. AI-ECG age correlated strongly with chronological age (r = 0.72, P < 0.001), though this correlation weakened in patients with multiple comorbidities. Patients with a positive Δ-age (≥+8 years) had significantly higher 10 year mortality risk (HR: 1.45, P < 0.001), while those with a negative Δ-age (≤-8 years) had lower risk (HR: 0.88, P < 0.001). These associations were consistent across care settings and remained robust when Δ-age was analysed continuously. Saliency maps indicated that the AI model was most sensitive to the P-wave.

Conclusion: AI-ECG age is a strong and independent predictor of long-term mortality in cardiovascular and acute care patients.

Aims: Cardiovascular disease is the leading global cause of mortality. Traditional face-to-face cardiovascular care, while effective, poses challenges such as travel burdens and accessibility issues. Video consultations offer a modern solution, improving access and efficiency while reducing patient strain. This study investigates patient acceptance of video consultations in cardiovascular care using a survey-based approach, assessing key factors influencing their integration into routine practice.

Methods and results: A cross-sectional study including patients attending a cardiological university hospital was conducted. Acceptance of video consultations and its associated factors were assessed using a modified assessment instrument based on the unified theory of acceptance and use of technology. The study comprised 337 participants (M = 61.13 years, SD = 14.54), 54.6% male. Acceptance was moderate (M = 2.88, SD = 1.37), with 30.27% of the participants reporting high acceptance, 28.19% reporting moderate acceptance, and 41.54% low acceptance. Only 3% had used video consultations before. eHealth literacy was high, while digital confidence was moderate. Analysis showed that higher education, digital confidence, and eHealth literacy predicted greater acceptance of video consultations. Effort expectancy, performance expectancy (PE), and social influence (SI) accounted for most of the variance in acceptance (R ² = 0.724).

Conclusion: We identified moderate acceptance of video consultations in cardiology, with education, digital confidence, eHealth literacy, and PE as key factors associated with acceptance. Despite low prior use, perceived ease of use and SI were most strongly associated with acceptance. Addressing technical concerns and promoting digital literacy may enhance adoption, improving access to remote cardiac care.

Aims: Timely and accurate detection of pericardial effusion and assessment of cardiac tamponade remain challenging and highly operator dependent.

Objectives: Artificial intelligence has advanced many echocardiographic assessments, and we aimed to develop and validate a deep learning model to automate the assessment of pericardial effusion severity and cardiac tamponade from echocardiogram videos.

Methods and results: We developed a deep learning model (EchoNet-Pericardium) using temporal-spatial convolutional neural networks to automate pericardial effusion severity grading and tamponade detection from echocardiography videos. The model was trained using a retrospective dataset of 1 427 660 videos from 85 380 echocardiograms at Cedars-Sinai Medical Centre (CSMC) to predict PE severity and cardiac tamponade across individual echocardiographic views and an ensemble approach combining predictions from five standard views. External validation was performed on 33 310 videos from 1806 echocardiograms from Stanford Healthcare (SHC). In the held-out CSMC test set, EchoNet-Pericardium achieved an AUC of 0.900 (95% CI: 0.884-0.916) for detecting moderate or larger pericardial effusion, 0.942 (95% CI: 0.917-0.964) for large pericardial effusion, and 0.955 (95% CI: 0.939-0.968) for cardiac tamponade. In the SHC external validation cohort, the model achieved AUCs of 0.869 (95% CI: 0.794-0.933) for moderate or larger pericardial effusion, 0.959 (95% CI: 0.945-0.972) for large pericardial effusion, and 0.966 (95% CI: 0.906-0.995) for cardiac tamponade. Subgroup analysis demonstrated consistent performance across ages, sexes, left ventricular ejection fraction, and atrial fibrillation statuses.

Conclusion: Our deep learning-based framework accurately grades pericardial effusion severity and detects cardiac tamponade from echocardiograms, demonstrating consistent performance and generalizability across different cohorts. This automated tool has the potential to enhance clinical decision-making by reducing operator dependence and expediting diagnosis.

Aims: The aim of the current study was to assess the utility of a state-of-the-art large language model (LLM) based on curated, defined clinical practice recommendations to support clinicians in obtaining point-of-care guidelines for individual patient treatment while maintaining transparency.

Methods and results: We combined cloud-based and locally run LLMs with versatile open-source tools to form a multi-query, multimodal, retrieval-augmented generation chain that closely reflects European cardiology guidelines in its responses. We compared the performance of this generation chain to other LLMs including GPT-3.5 and GPT-4 on a 306-question multiple-choice exam with questions consisting of short patient vignettes from various cardiology subspecialties, originally written to prepare candidates for the European Exam in Core Cardiology. On the multiple-choice test, our system demonstrated overall accuracy of 73.53%, while GPT-3.5 and GPT-4 had overall accuracies of 44.03 and 62.26%, respectively. Our system outperformed GPT-3.5 and GPT-4 for the following categories of questions: coronary artery disease, arrhythmia, other, valvular heart disease, cardiomyopathies, endocarditis, adult congenital heart disease, pericardial disease, cardio-oncology, pulmonary hypertension, and non-cardiac surgery. For maximum transparency, the system also provided reference quotes for its recommendations.

Conclusion: Our system demonstrated superior performance in question-answering tasks on a set of core cardiology questions as compared with contemporary publicly available chat models. The current study illustrates that LLMs can be tailored to provide documented and accountable guideline recommendations towards actual clinical needs while ensuring recommendations are derived from up-to-date, trustable, and traceable documents.

Background: Echocardiographic image data accumulating in echo labs are a highly valuable but underutilized resource for cardiac imaging research. Despite the availability of large image databases, quantitative measurements required for clinical analysis and research remain limited. Retrospective manual measurements are highly time-consuming and susceptible to operator-related variability. Moreover, data curation and quality control metrics are needed to prepare real-world data for analysis.

Methods: Deep learning-based image analysis can provide fully automated, rapid, and consistent extraction of measurements, given that the data have been properly curated. In this work, we develop an automated pipeline for data curation of a large echo database of 14 326 exams from 9678 patients and evaluate automated measurements of left ventricular ejection fraction (LVEF) and left atrial volume index (LAVI) as a use case.

Results: In validation subsample of 1763 subjects with varying image quality and cardiac diseases and 1488 healthy subjects, the pipeline output was compared with manual measurements. Bland-Altman analysis revealed a bias [standard deviation (SD)] of -1.8% (7.6%) for LVEF and 3.3 mL/m² (8.1 mL/m²) for LAVI and demonstrated robust performance for varying image quality and pathological conditions. Additionally, in the large part of the database of 9678 exams without clinical measurements, the automated data curation and measurement quality control resulted in 79% measured data with high confidence.

Conclusion: This work highlights the potential of deep learning-based automated measurements in echocardiography for data mining in large real-world databases, paving the way for advancements in cardiac imaging research and diagnostics.