European heart journal. Digital health最新文献

{"title":"Detection of acute coronary occlusion with a novel mobile electrocardiogram device: a pilot study: reply.","authors":"George Thornton Ratcliffe, William John Wallace, Gishani Poopalasingam","doi":"10.1093/ehjdh/ztae043","DOIUrl":"10.1093/ehjdh/ztae043","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"6 1","pages":"1-2"},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025838","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":"Cardiac anatomic digital twins: findings from a single national centre.","authors":"Matthias Lippert, Karl-Andreas Dumont, Sigurd Birkeland, Varatharajan Nainamalai, Håvard Solvin, Kathrine Rydén Suther, Bjørn Bendz, Ole Jakob Elle, Henrik Brun","doi":"10.1093/ehjdh/ztae070","DOIUrl":"10.1093/ehjdh/ztae070","url":null,"abstract":"<p><strong>Aims: </strong>New three-dimensional cardiac visualization technologies are increasingly employed for anatomic digital twins in pre-operative planning. However, the role and influence of extended reality (virtual, augmented, or mixed) within heart team settings remain unclear. We aimed to assess the impact of mixed reality visualization of the intracardiac anatomy on surgical decision-making in patients with complex heart defects.</p><p><strong>Methods and results: </strong>Between September 2020 and December 2022, we recruited 50 patients and generated anatomic digital twins and visualized them in mixed reality. These anatomic digital twins were presented to the heart team after initial decisions were made using standard visualization methods. Changes in the surgical strategy were recorded. Additionally, heart team members rated their mixed reality experience through a questionnaire, and post-operative outcomes were registered. Anatomic digital twins changed the initially decided upon surgical strategies for 68% of cases. While artificial intelligence facilitated the rapid creation of digital anatomic twins, manual corrections were always necessary.</p><p><strong>Conclusion: </strong>In conclusion, mixed reality anatomic digital twins added information to standard visualization methods and significantly influenced surgical planning, with evidence that these strategies can be implemented safely without additional risk.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"5 6","pages":"725-734"},"PeriodicalIF":3.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11570384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677930","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":"Enhancing the interoperability and transparency of real-world data extraction in clinical research: evaluating the feasibility and impact of a ChatGLM implementation in Chinese hospital settings.","authors":"Bin Wang, Junkai Lai, Han Cao, Feifei Jin, Qiang Li, Mingkun Tang, Chen Yao, Ping Zhang","doi":"10.1093/ehjdh/ztae066","DOIUrl":"10.1093/ehjdh/ztae066","url":null,"abstract":"<p><strong>Aims: </strong>This study aims to assess the feasibility and impact of the implementation of the ChatGLM for real-world data (RWD) extraction in hospital settings. The primary focus of this research is on the effectiveness of ChatGLM-driven data extraction compared with that of manual processes associated with the electronic source data repository (ESDR) system.</p><p><strong>Methods and results: </strong>The researchers developed the ESDR system, which integrates ChatGLM, electronic case report forms (eCRFs), and electronic health records. The LLaMA (Large Language Model Meta AI) model was also deployed to compare the extraction accuracy of ChatGLM in free-text forms. A single-centre retrospective cohort study served as a pilot case. Five eCRF forms of 63 subjects, including free-text forms and discharge medication, were evaluated. Data collection involved electronic medical and prescription records collected from 13 departments. The ChatGLM-assisted process was associated with an estimated efficiency improvement of 80.7% in the eCRF data transcription time. The initial manual input accuracy for free-text forms was 99.59%, the ChatGLM data extraction accuracy was 77.13%, and the LLaMA data extraction accuracy was 43.86%. The challenges associated with the use of ChatGLM focus on prompt design, prompt output consistency, prompt output verification, and integration with hospital information systems.</p><p><strong>Conclusion: </strong>The main contribution of this study is to validate the use of ESDR tools to address the interoperability and transparency challenges of using ChatGLM for RWD extraction in Chinese hospital settings.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"5 6","pages":"712-724"},"PeriodicalIF":3.9,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11570364/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677944","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":"Prediction of cardiovascular markers and diseases using retinal fundus images and deep learning: a systematic scoping review.","authors":"Livie Yumeng Li, Anders Aasted Isaksen, Benjamin Lebiecka-Johansen, Kristian Funck, Vajira Thambawita, Stine Byberg, Tue Helms Andersen, Ole Norgaard, Adam Hulman","doi":"10.1093/ehjdh/ztae068","DOIUrl":"10.1093/ehjdh/ztae068","url":null,"abstract":"<p><p>Rapid development in deep learning for image analysis inspired studies to focus on predicting cardiovascular risk using retinal fundus images. This scoping review aimed to identify and describe studies using retinal fundus images and deep learning to predict cardiovascular risk markers and diseases. We searched MEDLINE and Embase on 17 November 2023. Abstracts and relevant full-text articles were independently screened by two reviewers. We included studies that used deep learning for the analysis of retinal fundus images to predict cardiovascular risk markers or cardiovascular diseases (CVDs) and excluded studies only using predefined characteristics of retinal fundus images. Study characteristics were presented using descriptive statistics. We included 24 articles published between 2018 and 2023. Among these, 23 (96%) were cross-sectional studies and eight (33%) were follow-up studies with clinical CVD outcomes. Seven studies included a combination of both designs. Most studies (96%) used convolutional neural networks to process images. We found nine (38%) studies that incorporated clinical risk factors in the prediction and four (17%) that compared the results to commonly used clinical risk scores in a prospective setting. Three of these reported improved discriminative performance. External validation of models was rare (21%). There is increasing interest in using retinal fundus images in cardiovascular risk assessment with some studies demonstrating some improvements in prediction. However, more prospective studies, comparisons of results to clinical risk scores, and models augmented with traditional risk factors can strengthen further research in the field.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"5 6","pages":"660-669"},"PeriodicalIF":3.9,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11570365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677953","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":"Digital solutions to optimize guideline-directed medical therapy prescription rates in patients with heart failure: a clinical consensus statement from the ESC Working Group on e-Cardiology, the Heart Failure Association of the European Society of Cardiology, the Association of Cardiovascular Nursing & Allied Professions of the European Society of Cardiology, the ESC Digital Health Committee, the ESC Council of Cardio-Oncology, and the ESC Patient Forum.","authors":"Mark Johan Schuuring, Roderick Willem Treskes, Teresa Castiello, Magnus Thorsten Jensen, Ruben Casado-Arroyo, Lis Neubeck, Alexander R Lyon, Nurgul Keser, Marcin Rucinski, Maria Marketou, Ekaterini Lambrinou, Maurizio Volterrani, Loreena Hill","doi":"10.1093/ehjdh/ztae064","DOIUrl":"10.1093/ehjdh/ztae064","url":null,"abstract":"<p><p>The 2021 European Society of Cardiology guideline on diagnosis and treatment of acute and chronic heart failure (HF) and the 2023 Focused Update include recommendations on the pharmacotherapy for patients with New York Heart Association (NYHA) class II-IV HF with reduced ejection fraction. However, multinational data from the EVOLUTION HF study found substantial prescribing inertia of guideline-directed medical therapy (GDMT) in clinical practice. The cause was multifactorial and included limitations in organizational resources. Digital solutions like digital consultation, digital remote monitoring, digital interrogation of cardiac implantable electronic devices, clinical decision support systems, and multifaceted interventions are increasingly available worldwide. The objectives of this Clinical Consensus Statement are to provide (i) examples of digital solutions that can aid the optimization of prescription of GDMT, (ii) evidence-based insights on the optimization of prescription of GDMT using digital solutions, (iii) current evidence gaps and implementation barriers that limit the adoption of digital solutions in clinical practice, and (iv) critically discuss strategies to achieve equality of access, with reference to patient subgroups. Embracing digital solutions through the use of digital consults and digital remote monitoring will future-proof, for example alerts to clinicians, informing them of patients on suboptimal GDMT. Researchers should consider employing multifaceted digital solutions to optimize effectiveness and use study designs that fit the unique sociotechnical aspects of digital solutions. Artificial intelligence solutions can handle larger data sets and relieve medical professionals' workloads, but as the data on the use of artificial intelligence in HF are limited, further investigation is warranted.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"5 6","pages":"670-682"},"PeriodicalIF":3.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11570396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677942","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-empowered treatment decision-making in patients with aortic stenosis via early detection of cardiac amyloidosis.","authors":"Joana M Ribeiro, Rutger Jan Nuis, Peter P T de Jaegere","doi":"10.1093/ehjdh/ztae053","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae053","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"5 5","pages":"505-506"},"PeriodicalIF":3.9,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11417482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333742","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 algorithm for predicting left ventricular systolic dysfunction in atrial fibrillation with rapid ventricular response.","authors":"Joo Hee Jeong, Sora Kang, Hak Seung Lee, Min Sung Lee, Jeong Min Son, Joon-Myung Kwon, Hyoung Seok Lee, Yun Young Choi, So Ree Kim, Dong-Hyuk Cho, Yun Gi Kim, Mi-Na Kim, Jaemin Shim, Seong-Mi Park, Young-Hoon Kim, Jong-Il Choi","doi":"10.1093/ehjdh/ztae062","DOIUrl":"10.1093/ehjdh/ztae062","url":null,"abstract":"<p><strong>Aims: </strong>Although evaluation of left ventricular ejection fraction (LVEF) is crucial for deciding the rate control strategy in patients with atrial fibrillation (AF), real-time assessment of LVEF is limited in outpatient settings. We aimed to investigate the performance of artificial intelligence-based algorithms in predicting LV systolic dysfunction (LVSD) in patients with AF and rapid ventricular response (RVR).</p><p><strong>Methods and results: </strong>This study is an external validation of a pre-existing deep learning algorithm based on residual neural network architecture. Data were obtained from a prospective cohort of AF with RVR at a single centre between 2018 and 2023. Primary outcome was the detection of LVSD, defined as a LVEF ≤ 40%, assessed using 12-lead electrocardiography (ECG). Secondary outcome involved predicting LVSD using 1-lead ECG (Lead I). Among 423 patients, 241 with available echocardiography data within 2 months were evaluated, of whom 54 (22.4%) were confirmed to have LVSD. Deep learning algorithm demonstrated fair performance in predicting LVSD [area under the curve (AUC) 0.78]. Negative predictive value for excluding LVSD was 0.88. Deep learning algorithm resulted competent performance in predicting LVSD compared with N-terminal prohormone of brain natriuretic peptide (AUC 0.78 vs. 0.70, <i>P</i> = 0.12). Predictive performance of the deep learning algorithm was lower in Lead I (AUC 0.68); however, negative predictive value remained consistent (0.88).</p><p><strong>Conclusion: </strong>Deep learning algorithm demonstrated competent performance in predicting LVSD in patients with AF and RVR. In outpatient setting, use of artificial intelligence-based algorithm may facilitate prediction of LVSD and earlier choice of drug, enabling better symptom control in AF patients with RVR.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"5 6","pages":"683-691"},"PeriodicalIF":3.9,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11570393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677938","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":"Prediction of presence and severity of metabolic syndrome using regional body volumes measured by a multisensor white-light 3D scanner and validation using a mobile technology.","authors":"Betsy J Medina Inojosa, Virend K Somers, Kyla Lara-Breitinger, Lynne A Johnson, Jose R Medina-Inojosa, Francisco Lopez-Jimenez","doi":"10.1093/ehjdh/ztae059","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae059","url":null,"abstract":"<p><strong>Aims: </strong>To test whether an index based on the combination of demographics and body volumes obtained with a multisensor 3D body volume (3D-BV) scanner and biplane imaging using a mobile application (myBVI®) will reliably predict the severity and presence of metabolic syndrome (MS).</p><p><strong>Methods and results: </strong>We enrolled 1280 consecutive subjects who completed study protocol measurements, including 3D-BV and myBVI®. Body volumes and demographics were screened using the least absolute shrinkage and selection operator to select features associated with an MS severity score and prevalence. We randomly selected 80% of the subjects to train the models, and performance was assessed in 20% of the remaining observations and externally validated on 133 volunteers who prospectively underwent myBVI® measurements. The mean ± SD age was 43.7 ± 12.2 years, 63.7% were women, body mass index (BMI) was 28.2 ± 6.2 kg/m², and 30.2% had MS and an MS severity <i>z</i>-score of -0.2 ± 0.9. Features <i>β</i> coefficients equal to zero were removed from the model, and 14 were included in the final model and used to calculate the body volume index (BVI), demonstrating an area under the receiving operating curve (AUC) of 0.83 in the validation set. The myBVI® cohort had a mean age of 33 ± 10.3 years, 61% of whom were women, 10.5% MS, an average MS severity <i>z</i>-score of -0.8, and an AUC of 0.88.</p><p><strong>Conclusion: </strong>The described BVI model was associated with an increased severity and prevalence of MS compared with BMI and waist-to-hip ratio. Validation of the BVI had excellent performance when using myBVI®. This model could serve as a powerful screening tool for identifying MS.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"5 5","pages":"582-590"},"PeriodicalIF":3.9,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11417481/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333751","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":"Evaluating the impacts of digital ECG denoising on the interpretive capabilities of healthcare professionals.","authors":"Stacey McKenna, Naomi McCord, Jordan Diven, Matthew Fitzpatrick, Holly Easlea, Austin Gibbs, Andrew R J Mitchell","doi":"10.1093/ehjdh/ztae063","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae063","url":null,"abstract":"<p><strong>Aims: </strong>Electrocardiogram (ECG) interpretation is an essential skill across multiple medical disciplines; yet, studies have consistently identified deficiencies in the interpretive performance of healthcare professionals linked to a variety of educational and technological factors. Despite the established correlation between noise interference and erroneous diagnoses, research evaluating the impacts of digital denoising software on clinical ECG interpretation proficiency is lacking.</p><p><strong>Methods and results: </strong>Forty-eight participants from a variety of medical professions and experience levels were prospectively recruited for this study. Participants' capabilities in classifying common cardiac rhythms were evaluated using a sequential blinded and semi-blinded interpretation protocol on a challenging set of single-lead ECG signals (42 × 10 s) pre- and post-denoising with robust, cloud-based ECG processing software. Participants' ECG rhythm interpretation performance was greatest when raw and denoised signals were viewed in a combined format that enabled comparative evaluation. The combined view resulted in a 4.9% increase in mean rhythm classification accuracy (raw: 75.7% ± 14.5% vs. combined: 80.6% ± 12.5%, <i>P</i> = 0.0087), a 6.2% improvement in mean five-point graded confidence score (raw: 4.05 ± 0.58 vs. combined: 4.30 ± 0.48, <i>P</i> < 0.001), and 9.7% reduction in the mean proportion of undiagnosable data (raw: 14.2% ± 8.2% vs. combined: 4.5% ± 2.4%, <i>P</i> < 0.001), relative to raw signals alone. Participants also had a predominantly positive perception of denoising as it related to revealing previously unseen pathologies, improving ECG readability, and reducing time to diagnosis.</p><p><strong>Conclusion: </strong>Our findings have demonstrated that digital denoising software improves the efficacy of rhythm interpretation on single-lead ECGs, particularly when raw and denoised signals are provided in a combined viewing format, warranting further investigation into the impact of such technology on clinical decision-making and patient outcomes.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"5 5","pages":"601-610"},"PeriodicalIF":3.9,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11417490/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333746","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 use of imputation in clinical decision support systems: a cardiovascular risk management pilot vignette study among clinicians.","authors":"Saskia Haitjema, Steven W J Nijman, Inge Verkouter, John J L Jacobs, Folkert W Asselbergs, Karel G M Moons, Ines Beekers, Thomas P A Debray, Michiel L Bots","doi":"10.1093/ehjdh/ztae058","DOIUrl":"https://doi.org/10.1093/ehjdh/ztae058","url":null,"abstract":"<p><strong>Aims: </strong>A major challenge of the use of prediction models in clinical care is missing data. Real-time imputation may alleviate this. However, to what extent clinicians accept this solution remains unknown. We aimed to assess acceptance of real-time imputation for missing patient data in a clinical decision support system (CDSS) including 10-year cardiovascular absolute risk for the individual patient.</p><p><strong>Methods and results: </strong>We performed a vignette study extending an existing CDSS with the real-time imputation method joint modelling imputation (JMI). We included 17 clinicians to use the CDSS with three different vignettes, describing potential use cases (missing data, no risk estimate; imputed values, risk estimate based on imputed data; complete information). In each vignette, missing data were introduced to mimic a situation as could occur in clinical practice. Acceptance of end-users was assessed on three different axes: clinical realism, comfortableness, and added clinical value. Overall, the imputed predictor values were found to be clinically reasonable and according to the expectations. However, for binary variables, use of a probability scale to express uncertainty was deemed inconvenient. The perceived comfortableness with imputed risk prediction was low, and confidence intervals were deemed too wide for reliable decision-making. The clinicians acknowledged added value for using JMI in clinical practice when used for educational, research, or informative purposes.</p><p><strong>Conclusion: </strong>Handling missing data in CDSS via JMI is useful, but more accurate imputations are needed to generate comfort in clinicians for use in routine care. Only then can CDSS create clinical value by improving decision-making.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"5 5","pages":"572-581"},"PeriodicalIF":3.9,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11417486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333752","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}