European heart journal. Digital health最新文献

{"title":"Comprehensive full-vessel segmentation and volumetric plaque quantification for intracoronary optical coherence tomography using deep learning.","authors":"Rick H J A Volleberg, Ruben G A van der Waerden, Thijs J Luttikholt, Joske L van der Zande, Pierandrea Cancian, Xiaojin Gu, Jan-Quinten Mol, Silvan Quax, Mathias Prokop, Clara I Sánchez, Bram van Ginneken, Ivana Išgum, Jos Thannhauser, Simone Saitta, Kensuke Nishimiya, Tomasz Roleder, Niels van Royen","doi":"10.1093/ehjdh/ztaf021","DOIUrl":"10.1093/ehjdh/ztaf021","url":null,"abstract":"<p><strong>Aims: </strong>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).</p><p><strong>Methods and results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 3","pages":"404-416"},"PeriodicalIF":3.9,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of real-world application of cardiac implantable electronic device-based multi-sensor algorithm for heart failure management.","authors":"Jennifer Llewellyn, Rachel Goode, Matthew Kahn, Sergio Valsecchi, Archana Rao","doi":"10.1093/ehjdh/ztaf010","DOIUrl":"10.1093/ehjdh/ztaf010","url":null,"abstract":"<p><strong>Aims: </strong>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.</p><p><strong>Methods and results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 3","pages":"427-434"},"PeriodicalIF":3.9,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying heart failure dynamics using multi-point electrocardiograms and deep learning.","authors":"Yu Nishihara, Makoto Nishimori, Satoki Shibata, Masakazu Shinohara, Ken-Ichi Hirata, Hidekazu Tanaka","doi":"10.1093/ehjdh/ztaf016","DOIUrl":"10.1093/ehjdh/ztaf016","url":null,"abstract":"<p><strong>Aims: </strong>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.</p><p><strong>Methods and results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p><p><strong>Irb information: </strong>Kobe University Hospital Clinical & Translational Research Center (reference number: B220208).</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 3","pages":"447-455"},"PeriodicalIF":3.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Eco-conscious healthcare: merging clinical efficacy with sustainability.","authors":"Niels T B Scholte, Robert M A van der Boon","doi":"10.1093/ehjdh/ztaf017","DOIUrl":"10.1093/ehjdh/ztaf017","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 3","pages":"313-314"},"PeriodicalIF":3.9,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expertly used unsupervised clustering provides clinical tools as well as insight.","authors":"Johan De Bie","doi":"10.1093/ehjdh/ztaf015","DOIUrl":"10.1093/ehjdh/ztaf015","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 3","pages":"311-312"},"PeriodicalIF":3.9,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility, safety and patient perceptions of exercise-based cardiac telerehabilitation in a multicentre real-world setting after myocardial infarction-the remote exercise SWEDEHEART study.","authors":"Maria Bäck, Margret Leosdottir, Mattias Ekström, Kristina Hambraeus, Annica Ravn-Fischer, Sabina Borg, Madeleine Brosved, Marcus Flink, Kajsa Hedin, Charlotta Lans, Jessica Olovsson, Charlotte Urell, Birgitta Öberg, Stefan James","doi":"10.1093/ehjdh/ztaf014","DOIUrl":"10.1093/ehjdh/ztaf014","url":null,"abstract":"<p><strong>Aims: </strong>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).</p><p><strong>Methods and results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p><p><strong>Trial registration number: </strong>NCT04260958.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 3","pages":"508-518"},"PeriodicalIF":3.9,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examination of the performance of machine learning-based automated coronary plaque characterization by near-infrared spectroscopy-intravascular ultrasound and optical coherence tomography with histology.","authors":"Retesh Bajaj, Ramya Parasa, Alexander Broersen, Thomas Johnson, Mohil Garg, Francesco Prati, Murat Çap, Nathan Angelo Lecaros Yap, Medeni Karaduman, Carol Ann Glorioso Rexen Busk, Stephanie Grainger, Steven White, Anthony Mathur, Hector M García-García, Jouke Dijkstra, Ryo Torii, Andreas Baumbach, Helle Precht, Christos V Bourantas","doi":"10.1093/ehjdh/ztaf009","DOIUrl":"10.1093/ehjdh/ztaf009","url":null,"abstract":"<p><strong>Aims: </strong>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.</p><p><strong>Methods and results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 3","pages":"359-371"},"PeriodicalIF":3.9,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The environmental impact of telemonitoring vs. on-site cardiac follow-up: a mixed-method study.","authors":"Egid M van Bree, Lynn E Snijder, Sophie Ter Haak, Douwe E Atsma, Evelyn A Brakema","doi":"10.1093/ehjdh/ztaf012","DOIUrl":"10.1093/ehjdh/ztaf012","url":null,"abstract":"<p><strong>Aims: </strong>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.</p><p><strong>Methods and results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 3","pages":"496-507"},"PeriodicalIF":3.9,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Siamese neural network-enhanced electrocardiography can re-identify anonymized healthcare data.","authors":"Krzysztof Macierzanka, Arunashis Sau, Konstantinos Patlatzoglou, Libor Pastika, Ewa Sieliwonczyk, Mehak Gurnani, Nicholas S Peters, Jonathan W Waks, Daniel B Kramer, Fu Siong Ng","doi":"10.1093/ehjdh/ztaf011","DOIUrl":"10.1093/ehjdh/ztaf011","url":null,"abstract":"<p><strong>Aims: </strong>Many research databases with anonymized patient data contain electrocardiograms (ECGs) from which traditional identifiers have been removed. We evaluated the ability of artificial intelligence (AI) methods to determine the similarity between ECGs and assessed whether they have the potential to be misused to re-identify individuals from anonymized datasets.</p><p><strong>Methods and results: </strong>We utilized a convolutional Siamese neural network (SNN) architecture, which derives a Euclidean distance similarity metric between two input ECGs. A secondary care dataset of 864 283 ECGs (72 455 subjects) was used. Siamese neural network-electrocardiogram (SNN-ECG) achieves an accuracy of 91.68% when classifying between 2 689 124 same-subject pairs and 2 689 124 different-subject pairs. This performance increases to 93.61% and 95.97% in outpatient and normal ECG subsets. In a simulated 'motivated intruder' test, SNN-ECG can identify individuals from large datasets. In datasets of 100, 1000, 10 000, and 20 000 ECGs, where only one ECG is also from the reference individual, it achieves success rates of 79.2%, 62.6%, 45.0%, and 40.0%, respectively. If this was random, the success would be 1%, 0.1%, 0.01%, and 0.005%, respectively. Additional basic information, like subject sex or age-range, enhances performance further. We also found that, on the subject level, ECG pair similarity is clinically relevant; greater ECG dissimilarity associates with all-cause mortality [hazard ratio, 1.22 (1.21-1.23), <i>P</i> < 0.0001] and is additive to an AI-ECG model trained for mortality prediction.</p><p><strong>Conclusion: </strong>Anonymized ECGs retain information that may facilitate subject re-identification, raising privacy and data protection concerns. However, SNN-ECG models also have positive uses and can enhance risk prediction of cardiovascular disease.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 3","pages":"417-426"},"PeriodicalIF":3.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sudden cardiac arrest prediction via deep learning electrocardiogram analysis.","authors":"Matt T Oberdier, Luca Neri, Alessandro Orro, Richard T Carrick, Marco S Nobile, Sujai Jaipalli, Mariam Khan, Stefano Diciotti, Claudio Borghi, Henry R Halperin","doi":"10.1093/ehjdh/ztae088","DOIUrl":"10.1093/ehjdh/ztae088","url":null,"abstract":"<p><strong>Aims: </strong>Sudden cardiac arrest (SCA) is a commonly fatal event that often occurs without prior indications. To improve outcomes and enable preventative strategies, the electrocardiogram (ECG) in conjunction with deep learning was explored as a potential screening tool.</p><p><strong>Methods and results: </strong>A publicly available data set containing 10 s of 12-lead ECGs from individuals who did and did not have an SCA, information about time from ECG to arrest, and age and sex was utilized for analysis to individually predict SCA or not using deep convolution neural network models. The base model that included age and sex, ECGs within 1 day prior to arrest, and data sampled from windows of 720 ms around the R-waves from 221 individuals with SCA and 1046 controls had an area under the receiver operating characteristic curve of 0.77. With sensitivity set at 95%, base model specificity was 31%, which is not clinically applicable. Gradient-weighted class activation mapping showed that the model mostly relied on the QRS complex to make predictions. However, models with ECGs recorded between 1 day to 1 month and 1 month to 1 year prior to arrest demonstrated predictive capabilities.</p><p><strong>Conclusion: </strong>Deep learning models processing ECG data are a promising means of screening for SCA, and this method explains differences in SCAs due to age and sex. Model performance improved when ECGs were nearer in time to SCAs, although ECG data up to a year prior had predictive value. Sudden cardiac arrest prediction was more dependent upon QRS complex data compared to other ECG segments.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 2","pages":"170-179"},"PeriodicalIF":3.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914729/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143665621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}