European heart journal. Digital health最新文献

Aims: Coronary artery disease (CAD) incidence continues to rise with an increasing burden of chronic coronary disease (CCD). Current probability-based risk assessment for obstructive CAD (oCAD) lacks sufficient diagnostic accuracy. We aimed to develop and validate a deep learning (DL) algorithm utilizing electrocardiogram (ECG) waveforms and clinical features to predict oCAD in patients with suspected CCD.

Methods and results: The study includes subjects undergoing invasive angiography for evaluation of CCD over a 4-year period at a quaternary care centre. oCAD was defined as performance of percutaneous coronary intervention (PCI) based on assessment by interventional cardiologists during elective angiography. DL models were developed for ECG waveforms alone (DL-ECG), clinical features from standard risk scores (DL-Clinical), and the combination of ECG waveforms and clinical features (DL-MM); a commonly used pre-test probability estimation tool from the CAD Consortium study was used for comparison (CAD2) [3]. The CAD2 model [AUC 0.733 (0.717-0.750)] had similar performance as the DL-Clinical model [AUC 0.762 (0.746-0.778)]. The DL-ECG model [AUC 0.741 (0.726-0.758)] had similar performance as both the clinical feature models. The DL-MM model [AUC 0.807 (0.793-0.822)] had a superior performance. Validation in an external cohort demonstrated similar performance in the DL-MM [AUC 0.716 (0.707-0.726)] and CAD2 risk score [AUC 0.715 (0.705-0.724)].

Conclusion: A multi-modality DL model utilizing ECG waveforms and clinical risk factors can improve prediction of oCAD in CCD compared with risk-factor based models. Prospective research is warranted to determine whether incorporating DL methods in ECG analysis improves diagnosis of oCAD and outcomes in CCD.

Aims: Clinical consequences of coronary artery disease (CAD) are varied [e.g. atrial fibrillation (AF) and heart failure (HF)], and current risk stratification tools are ineffective. We aimed to identify clusters of individuals with CAD exhibiting unique patterns on the electrocardiogram (ECG) in an unsupervised manner and assess their association with cardiovascular risk.

Methods and results: Twenty-one ECG markers were derived from single-lead median-beat ECGs of 1928 individuals with CAD without a previous diagnosis of AF, HF, or ventricular arrhythmia (VA) from the imaging study in UK Biobank (CAD-IMG-UKB). An unsupervised clustering algorithm was used to group these markers into distinct clusters. We characterized each cluster according to their demographic and ECG characteristics, as well as their prevalent and incident risk of AF, HF, and VA (4-year median follow-up). Validation and association with prevalent diagnoses were performed in an independent cohort of 1644 individuals. The model identified two clusters within the CAD-IMG-UKB cohort. Cluster 1 (n = 359) exhibited prolonged QRS duration and QT intervals, along with greater morphological variations in QRS and T-waves, compared with Cluster 2 (n = 1569). Cluster 1, relative to Cluster 2, had a significantly higher risk of incident HF [hazard ratio (HR): 2.40, 95% confidence interval (CI): 1.51-3.83], confirmed by independent validation (HR: 1.77, CI: 1.31-2.41). It also showed a higher association with prevalent HF (odds ratio: 4.10, CI: 2.02-8.29), independent of clinical risk factors.

Conclusion: Our approach identified a cluster of individuals with CAD sharing ECG characteristics indicating HF risk, holding significant implications for targeted treatment and prevention enabling accessible large-scale screening.

Aims: Intracoronary optical coherence tomography (OCT) provides detailed information on coronary lesions, but interpretation of OCT images is time-consuming and subject to interobserver variability. The aim of this study was to develop and validate a deep learning-based multiclass semantic segmentation algorithm for OCT (OCT-AID).

Methods and results: A reference standard was obtained through manual multiclass annotation (guidewire artefact, lumen, side branch, intima, media, lipid plaque, calcified plaque, thrombus, plaque rupture, and background) of OCT images from a representative subset of pullbacks from the PECTUS-obs study. Pullbacks were randomly divided into a training and internal test set. An additional independent dataset was used for external testing. In total, 2808 frames were used for training and 218 for internal testing. The external test set comprised 392 frames. On the internal test set, the mean Dice score across nine classes was 0.659 overall and 0.757 on the true-positive frames, ranging from 0.281 to 0.989 per class. Substantial to almost perfect agreement was achieved for frame-wise identification of both lipid (κ=0.817, 95% CI 0.743-0.891) and calcified plaques (κ=0.795, 95% CI 0.703-0.887). For plaque quantification (e.g. lipid arc, calcium thickness), intraclass correlations of 0.664-0.884 were achieved. In the external test set, κ-values for lipid and calcified plaques were 0.720 (95% CI 0.640-0.800) and 0.851 (95% CI 0.794-0.908), respectively.

Conclusion: The developed multiclass semantic segmentation method for intracoronary OCT images demonstrated promising capabilities for various classes, while having included difficult frames, such as those containing artefacts or destabilized plaques. This algorithm is an important step towards comprehensive and standardized OCT image interpretation.

Aims: Remote monitoring of cardiac implantable electronic devices enables pre-emptive management of heart failure (HF) without additional patient engagement. The HeartLogic™ algorithm in implantable cardioverter defibrillators (ICDs) combines physiological parameters to predict HF events, facilitating earlier interventions. This study evaluated its diagnostic performance and resource implications within an HF management service.

Methods and results: In a single-centre study, 212 patients with cardiac resynchronization therapy ICDs (CRT-Ds) were monitored for 12-months. During follow-up, 18 (8%) patients died, and 15 HF hospitalizations occurred in 13 (6%) patients. Outpatient visits totalled 37 in 34 (16%) patients. HeartLogic™ alerts occurred in 58% of patients, with 100% sensitivity for HF-related hospitalizations. The positive predictive value was 29% including only alerts associated with HF events, while it was 51% including HF events and explained alerts. Unexplained alert rate was 0.46 per patient-year. Clinical interventions, mainly medication adjustments, followed 82 alerts. Total management time was 257 h/year, equivalent to 0.57 full-time equivalents for managing 1000 CRT-D patients.

Conclusion: The integration of HeartLogic™ into routine care demonstrated its utility in optimizing HF management, improving healthcare resource allocation. The algorithm can enhance proactive patient management and provide holistic care within the existing healthcare infrastructure.

Aims: Heart failure (HF) hospitalizations are associated with poor survival outcomes, emphasizing the need for early intervention. Deep learning algorithms have shown promise in HF detection through electrocardiogram (ECG). However, their utility in ongoing HF monitoring remains uncertain. This study developed a deep learning model using 12-lead ECGs collected at 2 different time points to evaluate HF status changes, aiming to enhance early intervention and continuous monitoring in various healthcare settings.

Methods and results: We analysed 30 171 ECGs from 6531 adult patients at Kobe University Hospital. The participants were randomly assigned to training, validation, and test datasets. A Transformer-based model was developed to classify HF status into deteriorated, improved, and no-change classes based on ECG waveform signals at two different time points. Performance metrics, such as the area under the receiver operating characteristic curve (AUROC) and accuracy, were calculated, and attention mapping via gradient-weighted class activation mapping was utilized to interpret the model's decision-making ability. The patients had an average age of 64.6 years (±15.4 years) and brain natriuretic peptide of 66.3 pg/mL (24.6-175.1 pg/mL). For HF status classification, the model achieved an AUROC of 0.889 [95% confidence interval (CI): 0.879-0.898] and an accuracy of 0.871 (95% CI: 0.864-0.878).

Conclusion: Transformer-based deep learning model demonstrated high accuracy in detecting HF status changes, highlighting its potential as a non-invasive, efficient tool for HF monitoring. The reliance of the model on ECGs reduces the need for invasive, costly diagnostics, aligning with clinical needs for accessible HF management.

Irb information: Kobe University Hospital Clinical & Translational Research Center (reference number: B220208).

Aims: Cardiac telerehabilitation addresses common barriers for attendance at exercise-based cardiac rehabilitation (EBCR). Pragmatic real-world studies are however lacking, limiting generalizability of available evidence. We aimed to evaluate feasibility, safety, and patient perceptions of remotely delivered EBCR in a multicentre clinical practice setting after myocardial infarction (MI).

Methods and results: This study included 232 post-MI patients (63.7 years, 77.5% men) from 23 cardiac rehabilitation centres in Sweden (2020-22). Exercise was delivered twice per week for 3 months through a real-time group-based video meeting connecting a physiotherapist to patients exercising at home. Outcomes were assessed before and after remote EBCR completion and comprised assessment of physical fitness, self-reported physical activity and exercise, physical capacity, kinesiophobia, health-related quality of life (HRQoL), self-efficacy for exercise, exercise adherence, patient acceptance. Safety monitoring in terms of adverse events (AE) and serious adverse events (SAE) was recorded. A total of 67.2% of the patients attended ≥ 75% of prescribed exercise sessions. Significant improvements in physical fitness, self-reported exercise, physical capacity, kinesiophobia, and HRQoL were observed. Patients agreed that remote EBCR improved health care access (83%), was easy to use (94%) and found exercise performance and interaction acceptable (95%). Sixteen exercise-related AEs (most commonly dizziness and musculoskeletal symptoms) were registered, all of which were resolved. Two SAEs requiring hospitalization were reported, both unrelated to exercise.

Conclusion: This multicentre study supports remote EBCR post-MI as feasible and safe with a high patient acceptance in a real-world setting. The clinical effectiveness needs to be confirmed in a randomized controlled trial.

Trial registration number: NCT04260958.

Aims: Near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) and optical coherence tomography (OCT) can assess coronary plaque pathology but are limited by time-consuming and expertise-driven image analysis. Recently introduced machine learning (ML)-classifiers have expedited image processing, but their performance in assessing plaque pathology against histological standards remains unclear. The aim of this study is to assess the performance of NIRS-IVUS-ML-based and OCT-ML-based plaque characterization against a histological standard.

Methods and results: Matched histological and NIRS-IVUS/OCT frames from human cadaveric hearts were manually annotated and fibrotic (FT), calcific (Ca), and necrotic core (NC) regions of interest (ROIs) were identified. Near-infrared spectroscopy-intravascular ultrasound and OCT frames were processed by their respective ML classifiers to segment and characterize plaque components. The histologically defined ROIs were overlaid onto corresponding NIRS-IVUS/OCT frames and the ML classifier estimations were compared with histology. In total, 131 pairs of NIRS-IVUS/histology and 184 pairs of OCT/histology were included. The agreement of NIRS-IVUS-ML with histology [concordance correlation coefficient (CCC) 0.81 and 0.88] was superior to OCT-ML (CCC 0.64 and 0.73) for the plaque area and burden. Plaque compositional analysis showed a substantial agreement of the NIRS-IVUS-ML with histology for FT, Ca, and NC ROIs (CCC: 0.73, 0.75, and 0.66, respectively) while the agreement of the OCT-ML with histology was 0.42, 0.62, and 0.13, respectively. The overall accuracy of NIRS-IVUS-ML and OCT-ML for characterizing atheroma types was 83% and 72%, respectively.

Conclusion: NIRS-IVUS-ML plaque compositional analysis has a good performance in assessing plaque components while OCT-ML performs well for the FT, moderately for the Ca, and has weak performance in detecting NC tissue. This may be attributable to the limitations of standalone intravascular imaging and to the fact that the OCT-ML classifier was trained on human experts rather than histological standards.

Aims: Digital health technologies are considered promising innovations to reduce healthcare's environmental footprint. However, this assumption remains largely unstudied. We compared the environmental impact of telemonitoring and care on site (CoS) in post-myocardial infarction (MI) follow-up and explored how it influenced patients' and healthcare professionals' (HPs) perceptions of using telemonitoring.

Methods and results: We conducted a mixed-method study; a standardized life cycle assessment, and qualitative interviews and focus groups. We studied the environmental impact of resource use per patient for 1-year post-MI follow-up in a Dutch academic hospital, as CoS or partially via telemonitoring. We used the Environmental Footprint 3.1 method. Qualitative data were analysed using Thematic Analysis. The environmental impact of telemonitoring was larger than CoS for all impact categories, including global warming (+480%) and mineral/metal resource use (+4390%). Production of telemonitoring devices contributed most of the environmental burden (89%). Telemonitoring and CoS achieved parity in most impact categories at 65 km one-way patient car commute. Healthcare professionals and patients did not consider the environmental impact in their preference for telemonitoring, as the patient's individual health was their primary concern-especially after a cardiac event. However, patients and HPs were generally positive towards sustainable healthcare and willing to use telemonitoring more sustainably.

Conclusion: Telemonitoring had a substantially bigger environmental impact than CoS in the studied setting. Patient commute distance, reuse of devices, and tailored use of devices should be considered when implementing telemonitoring for clinical follow-up. Patients and HPs supported these solutions to enhance sustainability-informed cardiovascular care as the default option.