European heart journal. Digital health最新文献

Aims: Stress echocardiography (SE) is widely used for assessing coronary artery disease, but volumetric chamber analysis during SE is limited by time-consuming manual tracings and operator-dependent variability. Automated evaluation may overcome these barriers and enhance efficiency.

Methods and results: This multi-centre study included 240 participants undergoing pharmacological SE for ischaemic heart disease evaluation from five sites in four countries. SE imaging data from apical four-chamber and two-chamber views were acquired during rest and stress phases. Expert cardiologists manually traced endocardial borders for left ventricular (LV), left atrial (LA) and right ventricular (RV), right atrial (RA) areas, which were compared to machine learning (ML) derived measurements. Image quality was categorized as optimal, good, fair, or poor, and its influence on ML performance was analysed. Statistical methods included Intraclass Correlation Coefficients (ICCs), Bland-Altman testing, and within-patient coefficient of variation. The yield of the ML algorithm demonstrated consistency across rest and stress phases. It demonstrated strong agreement with cardiologists for LV and LA volumes, with ICCs ranging from 0.84 to 0.93 across rest and stress conditions. RA and RV areas measurements showed moderate correlations, with better agreement at rest than during stress phases. Image quality significantly influenced ML performance, as poor-quality images reduced diagnostic yield.

Conclusion: AI-driven volumetric analysis is a reliable method for quantifying left-sided heart chambers during pharmacological SE, with results closely matching expert measurements. Moderate reliability for right-sided chambers highlights the need for high-quality imaging and standardized protocols. AI integration may streamline SE workflows and support improved clinical decision-making.

Aims: Coded healthcare data are now commonly used in clinical research. This study aimed to assess the transparency of reporting within heart failure studies and employ machine learning to facilitate larger-scale evaluation.

Methods & results: A systematic search of EMBASE and MEDLINE (2015-2020) identified 4279 heart failure studies with accessible Extensible Markup Language published in the top 25 journals by impact factor. Manual extraction in a random sample of 170 studies by independent human reviewers characterized 40 studies (23.5%) that used coded healthcare data, with 34 of these (85%) reporting doing so and only 19 (47.5%) providing clear descriptions of dataset construction and linkage. Another 420 studies underwent manual annotation to further train a Natural Language Processing (NLP) model designed for this study to automate and upscale review. The NLP model processed 3689 studies with a high level of internal accuracy (area under the receiver operating characteristic curve 0.97 and F1 score 0.96). Overall, the NLP approach identified 782 studies (21.2%) that reported coded healthcare data usage (95% CI 19.8-20.9%). No correlation was found between the reporting of coded healthcare data use and the publication year (r = ^-0.05; P = 0.21) or citation count (r = ^-0.13; P = 0.12).

Conclusion: One-fifth of contemporary heart failure research articles are already reporting the use of coded healthcare data, with at-scale evaluation facilitated by a machine-learning model. The limited transparency on how coded healthcare data were used in studies highlights the need for quality standards such as the CODE-EHR framework for the use of healthcare data in research.

Aims: To propose a novel deep learning-based method, the eLVMass-Net, for the estimation of left ventricular mass (LVM) based on 12-lead electrocardiograms (ECGs).

Methods and results: We developed a deep learning model for LVM estimation using raw ECG signals, demographic data, and ECG parameters as input by using TW-CVAI dataset (n = 1459). Synchronized single-heartbeat waveforms were processed using a temporal convolutional network (TCN). Ground-truth LVM values were obtained from coronary computed tomography angiography. We performed external validation on an independent NTUH dataset (n = 2579). To account for sex-specific differences in left ventricular remodelling and body habitus, we further developed separate models for males and females. We compared the performance of the eLVMass-Net, with two state-of-the-art (SOTA) models.Non-sex-specific eLVMass-Net achieved a mean absolute error (MAE) of 14.3 ± 0.7 g and a mean absolute percentage error (MAPE) of 12.9 ± 1.1% between predicted and ground-truth LVM values under five-fold cross-validation. The eLVMass-Net outperformed two SOTA models in terms of both LVM estimation and left ventricular hypertrophy (LVH) classification. Sex-specific design was superior in LVH classification based on estimated LVM (c-statistic: 0.77 ± 0.05 for male model; 0.75 ± 0.05 for female model; 0.70 ± 0.02 for non-sex-specific model; P  < 0.01 between both sex-specific models vs. non-sex-specific model). The saliency maps revealed gender-specific differences in how the model weighted ST-T segment features for LVM prediction.

Conclusion: The proposed eLVMass-Net outperformed previously published approaches by ECG pre-processing with synchronized single heartbeat extraction and TCN as ECG encoder. Additionally, the development of sex-specific models proved to be a rational approach.

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: There is a lack of studies investigating the pathophysiologic and phenotypic distinctiveness of aortic stenosis (AS). This heterogeneity has important implications for identifying optimal intervention timing and potential medical management. This study seeks to identify phenogroups of AS using unsupervised machine learning to improve risk stratification.

Methods and results: A total of 349 patients with asymptomatic AS from the PROGRESSA study were included in this analysis. Echocardiographic, clinical and blood sample data were used in the unsupervised clustering process. Longitudinal echocardiographic data were used to evaluate AS progression. Five clusters of patients were revealed using 18 variables selected by an unsupervised machine learning algorithm. Amongst them, aortic valvular phenotype, mean gradient, peak jet velocity (V_peak), and left ventricle stroke volume were selected as discriminatory variables. Following the clustering process, characteristics differed between clusters, including age, body mass index, and sex ratio (all P < 0.001). Of note, cluster 1 showed higher AS severity at baseline with significantly higher initial V_peak (344 [314; 376] cm/s) and calcium score (1257 [806; 1837] UA) (P < 0.001). Patients from cluster 1 had a faster AS progression (progression of V_peak = 22 [9; 39] cm/s/year), and calcium score (213 [111; 307] UA/year) (P < 0.001). Cluster 1 was also associated with a higher composite risk of mortality and aortic valve replacement when adjusted for age, sex, and baseline AS severity (P < 0.001).

Conclusion: Artificial intelligence-guided phenotypic classification revealed 5 distinct groups and enhanced risk stratification of patients with AS. This approach may be useful to optimize and individualize medical and interventional management of AS.

Aims: Non-ischaemic dilated cardiomyopathy (NIDCM) is a major cause of heart failure (HF) and heart transplantation (HTx), characterized by heterogeneity in aetiology, clinical phenotype, and disease progression. Nevertheless, precision medicine-based diagnostics and treatment strategies for NIDCM remain lacking. This proof-of-concept study aimed to stratify NIDCM patients by pathological features and identify those at high-risk for malignant arrhythmia (MA) and rapid progression to end-stage HF.

Methods and results: 293 NIDCM-HTx patients were included in this study. A total of 3516 heart tissue slides from six representative sites of each patient were analyzed using deep learning-based computational pathology (DL-CPath) and unsupervised clustering to identify pathological subgroups (PGs): PGA, PGB, and PGC. PGA was characterized by interstitial fibrosis, cardiomyocyte vacuolization, microvascular intimal hyperplasia, and myocyte disarray, and had the highest rates of MA (P = 0.03) and the shortest interval from diagnosis to HTx (P = 0.03). PGB showed focal fibrosis, whereas PGC demonstrated the mildest histopathological alterations. For clinical features, PGA showed elevated levels of blood biomarkers indicative of myocardial and secondary organ injury. PGB was associated with extensive fibrosis and significant impairment of ejection fraction. PGC presented with the mildest clinical abnormalities. Although LMNA mutation was a significant non-DL-CPath high-risk factor for MA and rapid NIDCM progression, its distribution did not differ significantly across PGs (P = 0.786).

Conclusion: DL-based pathological classification effectively extracted clinically-meaningful imaging features and enabled the identification of high-risk NIDCM subgroup. Each PG exhibited unique histopathological and clinical characteristics, highlighting distinct phenotypes and risk profiles.

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.