European heart journal. Digital health最新文献

{"title":"Reinventing 3D echocardiography: could AI-powered 3D reconstruction from 2D echocardiographic views serve as a viable alternative to 3D probes?","authors":"Márton Tokodi, Attila Kovács","doi":"10.1093/ehjdh/ztae078","DOIUrl":"10.1093/ehjdh/ztae078","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"3-4"},"PeriodicalIF":3.9,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025869","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":"Performance of Chat Generative Pre-trained Transformer-4o in the Adult Clinical Cardiology Self-Assessment Program.","authors":"Abdulaziz Malik, Christopher Madias, Benjamin S Wessler","doi":"10.1093/ehjdh/ztae077","DOIUrl":"10.1093/ehjdh/ztae077","url":null,"abstract":"<p><strong>Aims: </strong>This study evaluates the performance of OpenAI's latest large language model (LLM), Chat Generative Pre-trained Transformer-4o, on the Adult Clinical Cardiology Self-Assessment Program (ACCSAP).</p><p><strong>Methods and results: </strong>Chat Generative Pre-trained Transformer-4o was tested on 639 ACCSAP questions, excluding 45 questions containing video clips, resulting in 594 questions for analysis. The questions included a mix of text-based and static image-based [electrocardiogram (ECG), angiogram, computed tomography (CT) scan, and echocardiogram] formats. The model was allowed one attempt per question. Further evaluation of image-only questions was performed on 25 questions from the database. Chat Generative Pre-trained Transformer-4o correctly answered 69.2% (411/594) of the questions. The performance was higher for text-only questions (73.9%) compared with those requiring image interpretation (55.3%, <i>P</i> < 0.001). The model performed worse on questions involving ECGs, with a correct rate of 56.5% compared with 73.3% for non-ECG questions (<i>P</i> < 0.001). Despite its capability to interpret medical images in the context of a text-based question, the model's accuracy varied, demonstrating strengths and notable gaps in diagnostic accuracy. It lacked accuracy in reading images (ECGs, echocardiography, and angiograms) with no context.</p><p><strong>Conclusion: </strong>Chat Generative Pre-trained Transformer-4o performed moderately well on ACCSAP questions. However, the model's performance remains inconsistent, especially in interpreting ECGs. These findings highlight the potential and current limitations of using LLMs in medical education and clinical decision-making.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"155-158"},"PeriodicalIF":3.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025860","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":"Artificial intelligence evaluation of electrocardiographic characteristics and interval changes in transgender patients on gender-affirming hormone therapy.","authors":"Fadi W Adel, Philip Sang, Connor Walsh, Arvind Maheshwari, Paige Cummings, Zachi Attia, Kathryn Mangold, Caroline Davidge-Pitts, Francisco Lopez-Jimenez, Paul Friedman, Peter A Noseworthy, Rekha Mankad","doi":"10.1093/ehjdh/ztae076","DOIUrl":"10.1093/ehjdh/ztae076","url":null,"abstract":"<p><strong>Aims: </strong>Gender-affirming hormone therapy (GAHT) is used by some transgender individuals (TG), who comprise 1.4% of US population. However, the effects of GAHT on electrocardiogram (ECG) remain unknown. The objective is to assess the effects of GAHT on ECG changes in TG.</p><p><strong>Methods and results: </strong>Twelve-lead ECGs of TG on GAHT at the Mayo Clinic were inspected using a validated artificial intelligence (AI) algorithm. The algorithm assigns a patient's ECG male pattern probability on a scale of 0 (female) to 1 (male). In the primary analysis, done separately for transgender women (TGW) and transgender men (TGM), 12-lead ECGs were used to estimate the male pattern probability before and after GAHT. In a subanalysis, only patients with both pre- and post-GAHT EGCs were included. Further, the autopopulated PR, QRS, and QTc intervals were compared before and after GAHT. Among TGW (<i>n</i> = 86), the probability (mean ± SD) of an ECG male pattern was 0.84 ± 0.25 in the pre-GAHT group, and it was lowered to 0.59 ± 0.36 in the post-GAHT group (<i>n</i> = 173, <i>P</i> < 7.8 × 10^-10). Conversely, among TGM, male pattern probability was 0.16 ± 0.28 (<i>n</i> = 47) in the pre-GAHT group, and it was higher at 0.41 ± 0.38 in the post-GAHT group (<i>n</i> = 53, <i>P</i> < 2.4×10^-4). The trend persisted in the subanalysis. Furthermore, both the PR (<i>P</i> = 5.68 × 10^-4) and QTc intervals (<i>P</i> = 6.65×10^-6) prolonged among TGW. Among TGM, the QTc interval shortened (<i>P</i> = 4.8 × 10^-2).</p><p><strong>Conclusion: </strong>Among TG, GAHT is associated with ECG changes trending towards gender congruence, as determined by the AI algorithm and ECG intervals. Prospective studies are warranted to understand GAHT effects on cardiac structure and function.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"55-62"},"PeriodicalIF":3.9,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025795","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":"Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis.","authors":"Ammar Zaka, Daud Mutahar, James Gorcilov, Aashray K Gupta, Joshua G Kovoor, Brandon Stretton, Naim Mridha, Gopal Sivagangabalan, Aravinda Thiagalingam, Clara K Chow, Sarah Zaman, Rohan Jayasinghe, Pramesh Kovoor, Stephen Bacchi","doi":"10.1093/ehjdh/ztae074","DOIUrl":"10.1093/ehjdh/ztae074","url":null,"abstract":"<p><strong>Aims: </strong>Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.</p><p><strong>Methods and results: </strong>This study was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis guidelines. PubMed, EMBASE, Web of Science, and Cochrane databases were searched until 1 November 2023 for studies comparing ML models with traditional statistical methods for event prediction after PCI. The primary outcome was comparative discrimination measured by <i>C</i>-statistics with 95% confidence intervals (CIs) between ML models and traditional methods in estimating the risk of all-cause mortality, major bleeding, and the composite outcome major adverse cardiovascular events (MACE). Thirty-four models were included across 13 observational studies (4 105 916 patients). For all-cause mortality, the pooled <i>C</i>-statistic for top-performing ML models was 0.89 (95%CI, 0.84-0.91), compared with 0.86 (95% CI, 0.80-0.93) for traditional methods (<i>P</i> = 0.54). For major bleeding, the pooled <i>C</i>-statistic for ML models was 0.80 (95% CI, 0.77-0.84), compared with 0.78 (95% CI, 0.77-0.79) for traditional methods (<i>P</i> = 0.02). For MACE, the <i>C</i>-statistic for ML models was 0.83 (95% CI, 0.75-0.91), compared with 0.71 (95% CI, 0.69-0.74) for traditional methods (<i>P</i> = 0.007). Out of all included models, only one model was externally validated. Calibration was inconsistently reported across all models. Prediction Model Risk of Bias Assessment Tool demonstrated a high risk of bias across all studies.</p><p><strong>Conclusion: </strong>Machine learning models marginally outperformed traditional risk scores in the discrimination of MACE and major bleeding following PCI. While integration of ML algorithms into electronic healthcare systems has been hypothesized to improve peri-procedural risk stratification, immediate implementation in the clinical setting remains uncertain. Further research is required to overcome methodological and validation limitations.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"23-44"},"PeriodicalIF":3.9,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750198/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025844","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":"Advanced electrocardiography heart age: a prognostic, explainable machine learning approach applicable to sinus and non-sinus rhythms.","authors":"Zaidon S Al-Falahi, Todd T Schlegel, Israel Palencia-Lamela, Annie Li, Erik B Schelbert, Louise Niklasson, Maren Maanja, Thomas Lindow, Martin Ugander","doi":"10.1093/ehjdh/ztae075","DOIUrl":"10.1093/ehjdh/ztae075","url":null,"abstract":"<p><strong>Aims: </strong>An explainable advanced electrocardiography (A-ECG) Heart Age gap is the difference between A-ECG Heart Age and chronological age. This gap is an estimate of accelerated cardiovascular aging expressed in years of healthy human aging, and can intuitively communicate cardiovascular risk to the general population. However, existing A-ECG Heart Age requires sinus rhythm. We aim to develop and prognostically validate a revised, explainable A-ECG Heart Age applicable to both sinus and non-sinus rhythms.</p><p><strong>Methods and results: </strong>An A-ECG Heart Age excluding P-wave measures was derived from the 10-s 12-lead ECG in a derivation cohort using multivariable regression machine learning with Bayesian 5-min 12-lead A-ECG Heart Age as reference. The Heart Age was externally validated in a separate cohort of patients referred for cardiovascular magnetic resonance imaging by describing its association with heart failure hospitalization or death using Cox regression, and its association with comorbidities. In the derivation cohort (<i>n</i> = 2771), A-ECG Heart Age agreed with the 5-min Heart Age (<i>R</i> ² = 0.91, bias 0.0 ± 6.7 years), and increased with increasing comorbidity. In the validation cohort [<i>n</i> = 731, mean age 54 ± 15 years, 43% female, <i>n</i> = 139 events over 5.7 (4.8-6.7) years follow-up], increased A-ECG Heart Age gap (≥10 years) associated with events [hazard ratio, HR (95% confidence interval, CI) 2.04 (1.38-3.00), C-statistic 0.58 (0.54-0.62)], and the presence of hypertension, diabetes mellitus, hypercholesterolaemia, and heart failure (<i>P</i> ≤ 0.009 for all).</p><p><strong>Conclusion: </strong>An explainable A-ECG Heart Age gap applicable to both sinus and non-sinus rhythm associates with cardiovascular risk, cardiovascular morbidity, and survival.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"45-54"},"PeriodicalIF":3.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750191/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025690","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":"Introducing online multi-language video animations to support patients' understanding of cardiac procedures in a high-volume tertiary centre.","authors":"Jude Almutawa, Peter Calvert, David Wald, Vishal Luther","doi":"10.1093/ehjdh/ztae073","DOIUrl":"10.1093/ehjdh/ztae073","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"5 6","pages":"653-655"},"PeriodicalIF":3.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11570367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677949","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":"Deep-learning-driven optical coherence tomography analysis for cardiovascular outcome prediction in patients with acute coronary syndrome.","authors":"Tomoyo Hamana, Makoto Nishimori, Satoki Shibata, Hiroyuki Kawamori, Takayoshi Toba, Takashi Hiromasa, Shunsuke Kakizaki, Satoru Sasaki, Hiroyuki Fujii, Yuto Osumi, Seigo Iwane, Tetsuya Yamamoto, Shota Naniwa, Yuki Sakamoto, Yuta Fukuishi, Koshi Matsuhama, Hiroshi Tsunamoto, Hiroya Okamoto, Kotaro Higuchi, Tatsuya Kitagawa, Masakazu Shinohara, Koji Kuroda, Masamichi Iwasaki, Amane Kozuki, Junya Shite, Tomofumi Takaya, Ken-Ichi Hirata, Hiromasa Otake","doi":"10.1093/ehjdh/ztae067","DOIUrl":"10.1093/ehjdh/ztae067","url":null,"abstract":"<p><strong>Aims: </strong>Optical coherence tomography (OCT) can identify high-risk plaques indicative of worsening prognosis in patients with acute coronary syndrome (ACS). However, manual OCT analysis has several limitations. In this study, we aim to construct a deep-learning model capable of automatically predicting ACS prognosis from patient OCT images following percutaneous coronary intervention (PCI).</p><p><strong>Methods and results: </strong>Post-PCI OCT images from 418 patients with ACS were input into a deep-learning model comprising a convolutional neural network (CNN) and transformer. The primary endpoint was target vessel failure (TVF). Model performances were evaluated using Harrell's <i>C</i>-index and compared against conventional models based on human observation of quantitative (minimum lumen area, minimum stent area, average reference lumen area, stent expansion ratio, and lesion length) and qualitative (irregular protrusion, stent thrombus, malapposition, major stent edge dissection, and thin-cap fibroatheroma) factors. GradCAM activation maps were created after extracting attention layers by using the transformer architecture. A total of 60 patients experienced TVF during follow-up (median 961 days). The <i>C</i>-index for predicting TVF was 0.796 in the deep-learning model, which was significantly higher than that of the conventional model comprising only quantitative factors (<i>C</i>-index: 0.640) and comparable to that of the conventional model, including both quantitative and qualitative factors (<i>C</i>-index: 0.789). GradCAM heat maps revealed high activation corresponding to well-known high-risk OCT features.</p><p><strong>Conclusion: </strong>The CNN and transformer-based deep-learning model enabled fully automatic prognostic prediction in patients with ACS, with a predictive ability comparable to a conventional survival model using manual human analysis.</p><p><strong>Clinical trial registration: </strong>The study was registered in the University Hospital Medical Information Network Clinical Trial Registry (UMIN000049237).</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"5 6","pages":"692-701"},"PeriodicalIF":3.9,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11570387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677940","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":"Validation of machine learning-based risk stratification scores for patients with acute coronary syndrome treated with percutaneous coronary intervention.","authors":"Mitchel A Molenaar, Jasper L Selder, Amand F Schmidt, Folkert W Asselbergs, Jelle D Nieuwendijk, Brigitte van Dalfsen, Mark J Schuuring, Berto J Bouma, Steven A J Chamuleau, Niels J Verouden","doi":"10.1093/ehjdh/ztae071","DOIUrl":"10.1093/ehjdh/ztae071","url":null,"abstract":"<p><strong>Aims: </strong>This study aimed to validate the machine learning-based Global Registry of Acute Coronary Events (GRACE) 3.0 score and PRAISE (Prediction of Adverse Events following an Acute Coronary Syndrome) in patients with acute coronary syndrome (ACS) treated with percutaneous coronary intervention (PCI) for predicting mortality.</p><p><strong>Methods and results: </strong>Data of consecutive patients with ACS treated with PCI in a tertiary centre in the Netherlands between 2014 and 2021 were used for external validation. The GRACE 3.0 score for predicting in-hospital mortality was evaluated in 2759 patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) treated with PCI. The PRAISE score for predicting one-year mortality was evaluated in 4347 patients with ACS treated with PCI. Both risk scores were compared with the GRACE 2.0 score. The GRACE 3.0 score showed excellent discrimination [c-statistic 0.90 (95% CI 0.84, 0.94)] for predicting in-hospital mortality, with well-calibrated predictions (calibration-in-the large [CIL] -0.19 [95% CI -0.45, 0.07]). The PRAISE score demonstrated moderate discrimination [c-statistic 0.75 (95% CI 0.70, 0.80)] and overestimated the one-year risk of mortality [CIL -0.56 (95% CI -0.73, -0.39)]. Decision curve analysis demonstrated that the GRACE 3.0 score offered improved risk prediction compared with the GRACE 2.0 score, while the PRAISE score did not.</p><p><strong>Conclusion: </strong>This study in ACS patients treated with PCI provides suggestive evidence that the GRACE 3.0 score effectively predicts in-hospital mortality beyond the GRACE 2.0 score. The PRAISE score demonstrated limited potential for predicting one-year mortality risk. Further external validation studies in larger cohorts including patients without PCI are warranted.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"5 6","pages":"702-711"},"PeriodicalIF":3.9,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11570391/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677955","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":"CardiacField: computational echocardiography for automated heart function estimation using two-dimensional echocardiography probes.","authors":"Chengkang Shen, Hao Zhu, You Zhou, Yu Liu, Si Yi, Lili Dong, Weipeng Zhao, David J Brady, Xun Cao, Zhan Ma, Yi Lin","doi":"10.1093/ehjdh/ztae072","DOIUrl":"10.1093/ehjdh/ztae072","url":null,"abstract":"<p><strong>Aims: </strong>Accurate heart function estimation is vital for detecting and monitoring cardiovascular diseases. While two-dimensional echocardiography (2DE) is widely accessible and used, it requires specialized training, is prone to inter-observer variability, and lacks comprehensive three-dimensional (3D) information. We introduce CardiacField, a computational echocardiography system using a 2DE probe for precise, automated left ventricular (LV) and right ventricular (RV) ejection fraction (EF) estimations, which is especially easy to use for non-cardiovascular healthcare practitioners. We assess the system's usability among novice users and evaluate its performance against expert interpretations and advanced deep learning (DL) tools.</p><p><strong>Methods and results: </strong>We developed an implicit neural representation network to reconstruct a 3D cardiac volume from sequential multi-view 2DE images, followed by automatic segmentation of LV and RV areas to calculate volume sizes and EF values. Our study involved 127 patients to assess EF estimation accuracy against expert readings and two-dimensional (2D) video-based DL models. A subset of 56 patients was utilized to evaluate image quality and 3D accuracy and another 50 to test usability by novice users and across various ultrasound machines. CardiacField generated a 3D heart from 2D echocardiograms with <2 min processing time. The LVEF predicted by our method had a mean absolute error (MAE) of <math><mn>2.48</mn> <mtext>%</mtext></math> , while the RVEF had an MAE of <math><mn>2.65</mn> <mtext>%</mtext></math> .</p><p><strong>Conclusion: </strong>Employing a straightforward apical ring scan with a cost-effective 2DE probe, our method achieves a level of EF accuracy for assessing LV and RV function that is comparable to that of three-dimensional echocardiography probes.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"137-146"},"PeriodicalIF":3.9,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750196/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025831","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":"On the detection of acute coronary occlusion with the miniECG.","authors":"Alejandra Zepeda-Echavarria, Rutger R van de Leur, Pieter A Doevendans","doi":"10.1093/ehjdh/ztae065","DOIUrl":"10.1093/ehjdh/ztae065","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"5 6","pages":"656-657"},"PeriodicalIF":3.9,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11570361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677952","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}