European heart journal. Digital health最新文献

{"title":"Does ChatGPT succeed in the European Exam in Core Cardiology?","authors":"Chris Plummer, Danny Mathysen, Clive Lawson","doi":"10.1093/ehjdh/ztad040","DOIUrl":"10.1093/ehjdh/ztad040","url":null,"abstract":"success in this or similar exams. The EECC’s remote proctoring","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 5","pages":"362-363"},"PeriodicalIF":0.0,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/7e/ba/ztad040.PMC10545492.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41180625","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":"An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases.","authors":"Jorge Mariscal-Harana, Clint Asher, Vittoria Vergani, Maleeha Rizvi, Louise Keehn, Raymond J Kim, Robert M Judd, Steffen E Petersen, Reza Razavi, Andrew P King, Bram Ruijsink, Esther Puyol-Antón","doi":"10.1093/ehjdh/ztad044","DOIUrl":"10.1093/ehjdh/ztad044","url":null,"abstract":"<p><strong>Aims: </strong>Artificial intelligence (AI) techniques have been proposed for automating analysis of short-axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR analysis tool exists to automatically analyse large (unstructured) clinical CMR datasets. We develop and validate a robust AI tool for start-to-end automatic quantification of cardiac function from SAX cine CMR in large clinical databases.</p><p><strong>Methods and results: </strong>Our pipeline for processing and analysing CMR databases includes automated steps to identify the correct data, robust image pre-processing, an AI algorithm for biventricular segmentation of SAX CMR and estimation of functional biomarkers, and automated post-analysis quality control to detect and correct errors. The segmentation algorithm was trained on 2793 CMR scans from two NHS hospitals and validated on additional cases from this dataset (<i>n</i> = 414) and five external datasets (<i>n</i> = 6888), including scans of patients with a range of diseases acquired at 12 different centres using CMR scanners from all major vendors. Median absolute errors in cardiac biomarkers were within the range of inter-observer variability: <8.4 mL (left ventricle volume), <9.2 mL (right ventricle volume), <13.3 g (left ventricular mass), and <5.9% (ejection fraction) across all datasets. Stratification of cases according to phenotypes of cardiac disease and scanner vendors showed good performance across all groups.</p><p><strong>Conclusion: </strong>We show that our proposed tool, which combines image pre-processing steps, a domain-generalizable AI algorithm trained on a large-scale multi-domain CMR dataset and quality control steps, allows robust analysis of (clinical or research) databases from multiple centres, vendors, and cardiac diseases. This enables translation of our tool for use in fully automated processing of large multi-centre databases.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 5","pages":"370-383"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41157893","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":"ChatGPT fails the test of evidence-based medicine.","authors":"Wilhelm Haverkamp,&nbsp;Jonathan Tennenbaum,&nbsp;Nils Strodthoff","doi":"10.1093/ehjdh/ztad043","DOIUrl":"10.1093/ehjdh/ztad043","url":null,"abstract":"* Corresponding author. Email: wilhelm.haverkamp@dhzc-charite.de © The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Commentary article to: ‘Use of large language models for evidencebased cardiovascular medicine’, by I. Skalidis et al. https://doi.org/10.1093/ehjdh/ztad041.","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 5","pages":"366-367"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f7/43/ztad043.PMC10545496.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41160582","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":"Determine atrial fibrillation burden with a photoplethysmographic mobile sensor: the atrial fibrillation burden trial: detection and quantification of episodes of atrial fibrillation using a cloud analytics service connected to a wearable with photoplethysmographic sensor.","authors":"Pamela Reissenberger,&nbsp;Peter Serfözö,&nbsp;Diana Piper,&nbsp;Norman Juchler,&nbsp;Sara Glanzmann,&nbsp;Jasmin Gram,&nbsp;Karina Hensler,&nbsp;Hannah Tonidandel,&nbsp;Elena Börlin,&nbsp;Marcus D'Souza,&nbsp;Patrick Badertscher,&nbsp;Jens Eckstein","doi":"10.1093/ehjdh/ztad039","DOIUrl":"10.1093/ehjdh/ztad039","url":null,"abstract":"<p><strong>Aims: </strong>Recent studies suggest that atrial fibrillation (AF) burden (time AF is present) is an independent risk factor for stroke. The aim of this trial was to study the feasibility and accuracy to identify AF episodes and quantify AF burden in patients with a known history of paroxysmal AF with a photoplethysmography (PPG)-based wearable.</p><p><strong>Methods and results: </strong>In this prospective, single-centre trial, the PPG-based estimation of AF burden was compared with measurements of a conventional 48 h Holter electrocardiogram (ECG), which served as the gold standard. An automated algorithm performed PPG analysis, while a cardiologist, blinded for the PPG data, analysed the ECG data. Detected episodes of AF measured by both methods were aligned timewise.Out of 100 patients recruited, 8 had to be excluded due to technical issues. Data from 92 patients were analysed [55.4% male; age 73.3 years (standard deviation, SD: 10.4)]. Twenty-five patients presented AF during the study period. The intraclass correlation coefficient of total AF burden minutes detected by the two measurement methods was 0.88. The percentage of correctly identified AF burden over all patients was 85.1% and the respective parameter for non-AF time was 99.9%.</p><p><strong>Conclusion: </strong>Our results demonstrate that a PPG-based wearable in combination with an analytical algorithm appears to be suitable for a semiquantitative estimation of AF burden in patients with a known history of paroxysmal AF.</p><p><strong>Trial registration number: </strong>NCT04563572.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 5","pages":"402-410"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/47/42/ztad039.PMC10545505.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41156367","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":"Needs and demands for mHealth cardiac health promotion among individuals with cardiac diseases: a patient-centred design approach.","authors":"Lisa Maria Jahre,&nbsp;Julia Lortz,&nbsp;Tienush Rassaf,&nbsp;Christos Rammos,&nbsp;Charlotta Mallien,&nbsp;Eva-Maria Skoda,&nbsp;Martin Teufel,&nbsp;Alexander Bäuerle","doi":"10.1093/ehjdh/ztad038","DOIUrl":"10.1093/ehjdh/ztad038","url":null,"abstract":"<p><strong>Aims: </strong>Cardiovascular diseases are one of the main contributors to disability and mortality worldwide. Meanwhile, risk factors can be modified by lifestyle changes. mHealth is an innovative and effective way to deliver cardiac health promotion. This study aims to examine the needs and demands regarding the design and contents of an mHealth intervention for cardiac health promotion among individuals with cardiac diseases. Different clusters were determined and analysed in terms of the intention to use an mHealth intervention.</p><p><strong>Methods and results: </strong>A cross-sectional study was conducted via a web-based survey. Three hundred and four individuals with coronary artery diseases (CADs) and/or congestive heart failure (CHF) were included in the data analysis. Descriptive statistics were applied to evaluate needs and demands regarding an mHealth intervention. A <i>k</i>-medoids cluster analysis was performed. Individuals with CAD and CHF favoured an mHealth intervention that supports its users permanently and is easily integrated into everyday life. Handheld devices and content formats that involve active user participation and regular updates were preferred. Three clusters were observed and labelled high, moderate, and low burden, according to their psychometric properties. The high burden cluster indicated higher behavioural intention towards use of an mHealth intervention than the other clusters.</p><p><strong>Conclusion: </strong>The results of the study are a valuable foundation for the development of an mHealth intervention for cardiac health promotion following a user-centred design approach. Individuals with cardiac diseases report positive attitudes in the form of high usage intention regarding mHealth. Highly burdened individuals report a high intention to use such interventions.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 5","pages":"393-401"},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5c/36/ztad038.PMC10545514.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41159404","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":"European Society of Cardiology and Radical Health Festival Helsinki join forces to transform healthcare as we know it.","authors":"Gerhard Hindricks","doi":"10.1093/ehjdh/ztad036","DOIUrl":"10.1093/ehjdh/ztad036","url":null,"abstract":"","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 5","pages":"359-361"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10545521/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41171637","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":"Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using machine learning.","authors":"Hafiz Naderi, Julia Ramírez, Stefan van Duijvenboden, Esmeralda Ruiz Pujadas, Nay Aung, Lin Wang, Choudhary Anwar Ahmed Chahal, Karim Lekadir, Steffen E Petersen, Patricia B Munroe","doi":"10.1093/ehjdh/ztad037","DOIUrl":"10.1093/ehjdh/ztad037","url":null,"abstract":"<p><strong>Aims: </strong>Left ventricular hypertrophy (LVH) is an established, independent predictor of cardiovascular disease. Indices derived from the electrocardiogram (ECG) have been used to infer the presence of LVH with limited sensitivity. This study aimed to classify LVH defined by cardiovascular magnetic resonance (CMR) imaging using the 12-lead ECG for cost-effective patient stratification.</p><p><strong>Methods and results: </strong>We extracted ECG biomarkers with a known physiological association with LVH from the 12-lead ECG of 37 534 participants in the UK Biobank imaging study. Classification models integrating ECG biomarkers and clinical variables were built using logistic regression, support vector machine (SVM) and random forest (RF). The dataset was split into 80% training and 20% test sets for performance evaluation. Ten-fold cross validation was applied with further validation testing performed by separating data based on UK Biobank imaging centres. QRS amplitude and blood pressure (<i>P</i> < 0.001) were the features most strongly associated with LVH. Classification with logistic regression had an accuracy of 81% [sensitivity 70%, specificity 81%, Area under the receiver operator curve (AUC) 0.86], SVM 81% accuracy (sensitivity 72%, specificity 81%, AUC 0.85) and RF 72% accuracy (sensitivity 74%, specificity 72%, AUC 0.83). ECG biomarkers enhanced model performance of all classifiers, compared to using clinical variables alone. Validation testing by UK Biobank imaging centres demonstrated robustness of our models.</p><p><strong>Conclusion: </strong>A combination of ECG biomarkers and clinical variables were able to predict LVH defined by CMR. Our findings provide support for the ECG as an inexpensive screening tool to risk stratify patients with LVH as a prelude to advanced imaging.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 4","pages":"316-324"},"PeriodicalIF":3.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10393938/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9935781","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":"A comparative study of model-centric and data-centric approaches in the development of cardiovascular disease risk prediction models in the UK Biobank.","authors":"Mohammad Mamouei, Thomas Fisher, Shishir Rao, Yikuan Li, Ghomalreza Salimi-Khorshidi, Kazem Rahimi","doi":"10.1093/ehjdh/ztad033","DOIUrl":"10.1093/ehjdh/ztad033","url":null,"abstract":"<p><strong>Aims: </strong>A diverse set of factors influence cardiovascular diseases (CVDs), but a systematic investigation of the interplay between these determinants and the contribution of each to CVD incidence prediction is largely missing from the literature. In this study, we leverage one of the most comprehensive biobanks worldwide, the UK Biobank, to investigate the contribution of different risk factor categories to more accurate incidence predictions in the overall population, by sex, different age groups, and ethnicity.</p><p><strong>Methods and results: </strong>The investigated categories include the history of medical events, behavioural factors, socioeconomic factors, environmental factors, and measurements. We included data from a cohort of 405 257 participants aged 37-73 years and trained various machine learning and deep learning models on different subsets of risk factors to predict CVD incidence. Each of the models was trained on the complete set of predictors and subsets where each category was excluded. The results were benchmarked against QRISK3. The findings highlight that (i) leveraging a more comprehensive medical history substantially improves model performance. Relative to QRISK3, the best performing models improved the discrimination by 3.78% and improved precision by 1.80%. (ii) Both model- and data-centric approaches are necessary to improve predictive performance. The benefits of using a comprehensive history of diseases were far more pronounced when a neural sequence model, BEHRT, was used. This highlights the importance of the temporality of medical events that existing clinical risk models fail to capture. (iii) Besides the history of diseases, socioeconomic factors and measurements had small but significant independent contributions to the predictive performance.</p><p><strong>Conclusion: </strong>These findings emphasize the need for considering broad determinants and novel modelling approaches to enhance CVD incidence prediction.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 4","pages":"337-346"},"PeriodicalIF":3.9,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/0e/a6/ztad033.PMC10393888.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9929224","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":"Development of artificial intelligence tools for invasive Doppler-based coronary microvascular assessment.","authors":"Henry Seligman, Sapna B Patel, Anissa Alloula, James P Howard, Christopher M Cook, Yousif Ahmad, Guus A de Waard, Mauro Echavarría Pinto, Tim P van de Hoef, Haseeb Rahman, Mihir A Kelshiker, Christopher A Rajkumar, Michael Foley, Alexandra N Nowbar, Samay Mehta, Mathieu Toulemonde, Meng-Xing Tang, Rasha Al-Lamee, Sayan Sen, Graham Cole, Sukhjinder Nijjer, Javier Escaned, Niels Van Royen, Darrel P Francis, Matthew J Shun-Shin, Ricardo Petraco","doi":"10.1093/ehjdh/ztad030","DOIUrl":"10.1093/ehjdh/ztad030","url":null,"abstract":"<p><strong>Aims: </strong>Coronary flow reserve (CFR) assessment has proven clinical utility, but Doppler-based methods are sensitive to noise and operator bias, limiting their clinical applicability. The objective of the study is to expand the adoption of invasive Doppler CFR, through the development of artificial intelligence (AI) algorithms to automatically quantify coronary Doppler quality and track flow velocity.</p><p><strong>Methods and results: </strong>A neural network was trained on images extracted from coronary Doppler flow recordings to score signal quality and derive values for coronary flow velocity and CFR. The outputs were independently validated against expert consensus. Artificial intelligence successfully quantified Doppler signal quality, with high agreement with expert consensus (Spearman's rho: 0.94), and within individual experts. Artificial intelligence automatically tracked flow velocity with superior numerical agreement against experts, when compared with the current console algorithm [AI flow vs. expert flow bias -1.68 cm/s, 95% confidence interval (CI) -2.13 to -1.23 cm/s, <i>P</i> < 0.001 with limits of agreement (LOA) -4.03 to 0.68 cm/s; console flow vs. expert flow bias -2.63 cm/s, 95% CI -3.74 to -1.52, <i>P</i> < 0.001, 95% LOA -8.45 to -3.19 cm/s]. Artificial intelligence yielded more precise CFR values [median absolute difference (MAD) against expert CFR: 4.0% for AI and 7.4% for console]. Artificial intelligence tracked lower-quality Doppler signals with lower variability (MAD against expert CFR 8.3% for AI and 16.7% for console).</p><p><strong>Conclusion: </strong>An AI-based system, trained by experts and independently validated, could assign a quality score to Doppler traces and derive coronary flow velocity and CFR. By making Doppler CFR more automated, precise, and operator-independent, AI could expand the clinical applicability of coronary microvascular assessment.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 4","pages":"291-301"},"PeriodicalIF":3.9,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/09/27/ztad030.PMC10393887.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9929220","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":"Automatic screening of patients with atrial fibrillation from 24-h Holter recording using deep learning.","authors":"Peng Zhang,&nbsp;Fan Lin,&nbsp;Fei Ma,&nbsp;Yuting Chen,&nbsp;Siyi Fang,&nbsp;Haiyan Zheng,&nbsp;Zuwen Xiang,&nbsp;Xiaoyun Yang,&nbsp;Qiang Li","doi":"10.1093/ehjdh/ztad018","DOIUrl":"https://doi.org/10.1093/ehjdh/ztad018","url":null,"abstract":"<p><strong>Aims: </strong>As the demand for atrial fibrillation (AF) screening increases, clinicians spend a significant amount of time identifying AF signals from massive amounts of data obtained during long-term dynamic electrocardiogram (ECG) monitoring. The identification of AF signals is subjective and depends on the experience of clinicians. However, experienced cardiologists are scarce. This study aimed to apply a deep learning-based algorithm to fully automate primary screening of patients with AF using 24-h Holter monitoring.</p><p><strong>Methods and results: </strong>A deep learning model was developed to automatically detect AF episodes using RR intervals and was trained and evaluated on 23 621 (2297 AF and 21 324 non-AF) 24-h Holter recordings from 23 452 patients. Based on the AF episode detection results, patients with AF were automatically identified using the criterion of at least one AF episode lasting 6 min or longer. Performance was assessed on an independent real-world hospital-scenario test set (19 227 recordings) and a community-scenario test set (1299 recordings). For the two test sets, the model obtained high performance for the identification of patients with AF (sensitivity: 0.995 and 1.000; specificity: 0.985 and 0.997, respectively). Moreover, it obtained good and consistent performance (sensitivity: 1.000; specificity: 0.972) for an external public data set.</p><p><strong>Conclusion: </strong>Using the criterion of at least one AF episode of 6 min or longer, the deep learning model can fully automatically screen patients for AF with high accuracy from long-term Holter monitoring data. This method may serve as a powerful and cost-effective tool for primary screening for AF.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":"4 3","pages":"216-224"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/42/c9/ztad018.PMC10232289.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9568845","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}