{"id":11343,"date":"2024-08-14T08:36:33","date_gmt":"2024-08-14T08:36:33","guid":{"rendered":"https:\/\/www.friendly-bartik.94-130-216-58.plesk.page\/maestria\/"},"modified":"2025-12-19T11:42:23","modified_gmt":"2025-12-19T11:42:23","slug":"maestria","status":"publish","type":"page","link":"https:\/\/www.af-net.eu\/en\/maestria\/","title":{"rendered":"MAESTRIA"},"content":{"rendered":"\n
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MAESTRIA <\/strong>– AFNET 10<\/h2>\n\n\n\n
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M<\/strong>achine Learning A<\/strong>rtificial Intelligence E<\/strong>arly Detection ST<\/strong>roke Atri<\/strong>al Fibrilla<\/strong>tion.<\/p>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

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the maestria ANNUAL REVIEW<\/strong> 2025<\/strong><\/h2>\n\n\n\n

the maestria ANNUAL REVIEW<\/strong> 2025<\/strong><\/h2>\n\n\n\n
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You are currently viewing a placeholder content from YouTube<\/strong>. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.<\/p> More Information<\/a> <\/div>

Unblock content<\/a> Accept required service and unblock content<\/a> <\/div> <\/div> <\/div><\/div>\r\n<\/div><\/figure>\n<\/div>\n\n\n\n
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Study Design<\/strong><\/h2>\n\n\n\n
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The MAESTRIA study is an international, multi-centre, non-interventional, observational registry. The main goal is to enrol a representative group of European patients diagnosed with Atrial Fibrillation (AF) to analyse clinical and relevant parameters (digitalised ECG, echocardiograms, cardiac CTs, MRIs and blood biomarkers) that could be used during clinical practise for the diagnosis of atrial cardiomyopathy. The AF patients will be distributed in 3 groups according to the different manifestation of AF: paroxysmal, persistent and permanent AF.<\/p>\n<\/div>\n\n\n\n

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You are currently viewing a placeholder content from YouTube<\/strong>. To access the actual content, click the button below. Please note that doing so will share data with third-party providers.<\/p> More Information<\/a> <\/div>

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Inclusion Criteria<\/strong><\/h5>\n\n\n\n

\"\" Patients with paroxysmal AF, persistent AF or permanent AF.<\/p>\n\n\n\n

\"\" Patients (or legally acceptable representative if applicable to country specific regulations) provide written informed consent to participate in the study. The patient has the option to give separate consent to donate extra volume of blood during the routine blood collection, that can be used for biomedical research.<\/p>\n\n\n\n

\"\" Patient is at least 18 years old.<\/p>\n\n\n\n

\"\" Patient must own a smartphone with Apple iOS version 14.5 (or higher) or with Android version 8.0 (or higher).<\/p>\n<\/div>\n\n\n\n

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Exclusion Criteria<\/strong><\/h5>\n\n\n\n

\"\" Any disease that limits life expectancy to less than 1 year.<\/p>\n\n\n\n

\"\" All persons unable to provide informed consent.<\/p>\n\n\n\n

\"\"All persons exempt from participation in a study or trial by law.<\/p>\n\n\n\n

\"\" Any medical or psychiatric condition which, in the investigator\u00b4s opinion, would preclude the participant from adhering to the protocol or completing the study per protocol.<\/p>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

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Objective<\/strong><\/strong><\/h2>\n\n\n\n
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The MAESTRIA project aims to develop and validate the first integrative diagnostic digital platform for atrial cardiomyopathy diagnosis. This platform will be designed to provide support for improved diagnostic accuracy that increases effectiveness and efficiency of treatments, as well as prevention of the complications of atrial cardiomyopathy, such as atrial fibrillation and stroke.<\/p>\n\n\n\n

AFNET is responsible of the development of a novel specific prospective cohort (MAESTRIA-AFNET 10<\/strong>) which will include approximately 600 patients with different clinical manifestation of Atrial Fibrillation (AF): paroxysmal, persistent and permanent AF. <\/p>\n<\/div>\n\n\n\n

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PATIENTS<\/strong><\/strong><\/p>\n\n\n\n

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sites<\/strong><\/strong><\/p>\n\n\n\n

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Study duration<\/strong><\/h2>\n\n\n\n
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Start of study<\/strong><\/h4>\n\n\n\n

2021<\/strong><\/p>\n<\/div>\n\n\n\n

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estimated end of study<\/strong><\/h4>\n\n\n\n

2026<\/strong><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Study information for download<\/strong><\/h2>\n\n\n\n
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\n\tStudy design<\/strong>
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\n\tPatient newsletter<\/strong>
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Publications<\/strong><\/h2>\n\n\n\n
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Fabritz Larissa, Connolly D., Czarnecki E., Dudek D., Zlahoda-Huzior A., Guasch E., Haase D., Huebner T., Jolly K., Kirchhof P., Schotten Ulrich, Zapf Antonia, Schnabel Renate B: Remote Design of a Smartphone and Wearable Detected Atrial Arrhythmia in Older Adults Case Finding Study: Smart in OAC \u2013 AFNET 9<\/strong>. Frontiers in Cardiovascular Medicine, (2022), 9 https:\/\/www.frontiersin.org\/article\/10.3389\/fcvm.2022.839202<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n

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Coordinating Investigator<\/strong><\/strong><\/h2>\n\n\n\n
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Prof. Andreas G\u00f6tte<\/strong><\/h5>\n\n\n\n

Paderborn<\/p>\n<\/div>\n\n\n\n

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More information<\/strong><\/h2>\n\n\n\n
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Registration<\/h4>\n\n\n\n

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support<\/h4>\n\n\n\n

Funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 965286.<\/a><\/p>\n<\/div>\n\n\n\n

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COORDINATOR MAESTRIA<\/h4>\n\n\n\n

Sorbonne University, Paris, France<\/p>\n\n\n\n

LEADER of WORKPACKAGE 4 of MAESTRIA (MAESTRIA \u2013 AFNET 10): AFNET<\/p>\n\n\n\n

MAESTRIA PROJECT WEBSITE<\/h4>\n\n\n\n

Maestria H2020 – European research project<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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MAESTRIA – AFNET 10 Machine Learning Artificial Intelligence Early Detection STroke Atrial Fibrillation. the maestria ANNUAL REVIEW 2025 the maestria ANNUAL REVIEW 2025 You are currently viewing a placeholder content from YouTube. To access the actual content, click the button below. Please note that doing so will share data with third-party providers. More Information Unblock content Accept required … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","iawp_total_views":25,"_vp_format_video_url":"","_vp_image_focal_point":[],"footnotes":""},"class_list":["post-11343","page","type-page","status-publish"],"acf":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"timeline-express":false,"timeline-express-thumbnail":false,"1536x1536":false,"2048x2048":false,"vp_sm":false,"vp_md":false,"vp_lg":false,"vp_xl":false,"vp_sm_popup":false,"vp_md_popup":false,"vp_xl_popup":false},"uagb_author_info":{"display_name":"admin","author_link":"https:\/\/www.af-net.eu\/en\/author\/cre8ors\/"},"uagb_comment_info":0,"uagb_excerpt":"MAESTRIA – AFNET 10 Machine Learning Artificial Intelligence Early Detection STroke Atrial Fibrillation. the maestria ANNUAL REVIEW 2025 the maestria ANNUAL REVIEW 2025 You are currently viewing a placeholder content from YouTube. To access the actual content, click the button below. Please note that doing so will share data with third-party providers. More Information Unblock content Accept required…","_links":{"self":[{"href":"https:\/\/www.af-net.eu\/en\/wp-json\/wp\/v2\/pages\/11343","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.af-net.eu\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.af-net.eu\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.af-net.eu\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.af-net.eu\/en\/wp-json\/wp\/v2\/comments?post=11343"}],"version-history":[{"count":27,"href":"https:\/\/www.af-net.eu\/en\/wp-json\/wp\/v2\/pages\/11343\/revisions"}],"predecessor-version":[{"id":15910,"href":"https:\/\/www.af-net.eu\/en\/wp-json\/wp\/v2\/pages\/11343\/revisions\/15910"}],"wp:attachment":[{"href":"https:\/\/www.af-net.eu\/en\/wp-json\/wp\/v2\/media?parent=11343"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}