Dominik Felbel, Merten Prüser, Constanze Schmidt, Björn Schreiweis, Nicolai Spicher, Wolfgang Rottbauer, Julian Varghese, Andreas Zietzer, Stefan Störk, Christoph Dieterich, Dagmar Krefting, Eimo Martens, Martin Sedlmayr, Dario Bongiovanni, Christoph B Olivier, Hendrik Lapp, Hannes H J G Schmidt, Julius L Katzmann, Felix Nensa, Norbert Frey, Gudrun S Ulrich-Merzenich, Carina A Peter, Peter Heuschmann, Udo Bavendiek, Sven Zenker
{"title":"“通过结构化临床文件和生物信号衍生表型合成推进心血管风险识别”项目：概念设计、项目规划和首次实施经验。","authors":"Dominik Felbel, Merten Prüser, Constanze Schmidt, Björn Schreiweis, Nicolai Spicher, Wolfgang Rottbauer, Julian Varghese, Andreas Zietzer, Stefan Störk, Christoph Dieterich, Dagmar Krefting, Eimo Martens, Martin Sedlmayr, Dario Bongiovanni, Christoph B Olivier, Hendrik Lapp, Hannes H J G Schmidt, Julius L Katzmann, Felix Nensa, Norbert Frey, Gudrun S Ulrich-Merzenich, Carina A Peter, Peter Heuschmann, Udo Bavendiek, Sven Zenker","doi":"10.1093/ehjdh/ztaf075","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>Personalized risk assessment tools (PRTs) are recommended by cardiovascular guidelines to tailor prevention, diagnosis, and treatment. However, PRT implementation in clinical routine is poor. ACRIBiS (Advancing Cardiovascular Risk Identification with Structured Clinical Documentation and Biosignal Derived Phenotypes Synthesis) aims to establish interoperable infrastructures for standardized documentation of routine data and integration of high-resolution biosignals (HRBs) enabling data-based risk assessment.</p><p><strong>Methods and results: </strong>Established cardiovascular risk scores were selected by their predictive performance and served as basis for building a core cardiovascular dataset with risk-relevant clinical routine information. Data items not yet represented in the Medical Informatics Inititative (MII) Core Dataset (CDS) FHIR profiles will be added to an extension module 'Cardiology' allowing for maximum interoperability. HRB integration will be implemented at each site through a modular infrastructure for electrocardiography (ECG) processing. Predictive performance of PRTs and their dynamic recalibration through HRB integration will be evaluated within the ACRIBiS cohort consisting of 5250 prospectively recruited patients at 15 German academic cardiology departments with 12-month follow-up. The potential of visualising these risks to improve patient education will also be assessed and supported by the development of a self-assessment app.</p><p><strong>Discussion: </strong>The ACRIBiS project presents an innovative concept to harmonize clinical data documentation and integrate ECG data, ultimately facilitating personalized risk assessment to improve patient empowerment and prognosis. Importantly, the consensus-based documentation and interoperability specifications developed will support the standardisation of routine patient data collection at the national and international levels, while the ACRIBiS cohort dataset will be available for broad secondary use.</p><p><strong>Trial registration: </strong>The study is registered at the German study registry (DRKS): #DRKS00034792.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. 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Predictive performance of PRTs and their dynamic recalibration through HRB integration will be evaluated within the ACRIBiS cohort consisting of 5250 prospectively recruited patients at 15 German academic cardiology departments with 12-month follow-up. The potential of visualising these risks to improve patient education will also be assessed and supported by the development of a self-assessment app.</p><p><strong>Discussion: </strong>The ACRIBiS project presents an innovative concept to harmonize clinical data documentation and integrate ECG data, ultimately facilitating personalized risk assessment to improve patient empowerment and prognosis. 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The 'Advancing Cardiovascular Risk Identification with Structured Clinical Documentation and Biosignal Derived Phenotypes Synthesis' project: conceptual design, project planning, and first implementation experiences.
Aims: Personalized risk assessment tools (PRTs) are recommended by cardiovascular guidelines to tailor prevention, diagnosis, and treatment. However, PRT implementation in clinical routine is poor. ACRIBiS (Advancing Cardiovascular Risk Identification with Structured Clinical Documentation and Biosignal Derived Phenotypes Synthesis) aims to establish interoperable infrastructures for standardized documentation of routine data and integration of high-resolution biosignals (HRBs) enabling data-based risk assessment.
Methods and results: Established cardiovascular risk scores were selected by their predictive performance and served as basis for building a core cardiovascular dataset with risk-relevant clinical routine information. Data items not yet represented in the Medical Informatics Inititative (MII) Core Dataset (CDS) FHIR profiles will be added to an extension module 'Cardiology' allowing for maximum interoperability. HRB integration will be implemented at each site through a modular infrastructure for electrocardiography (ECG) processing. Predictive performance of PRTs and their dynamic recalibration through HRB integration will be evaluated within the ACRIBiS cohort consisting of 5250 prospectively recruited patients at 15 German academic cardiology departments with 12-month follow-up. The potential of visualising these risks to improve patient education will also be assessed and supported by the development of a self-assessment app.
Discussion: The ACRIBiS project presents an innovative concept to harmonize clinical data documentation and integrate ECG data, ultimately facilitating personalized risk assessment to improve patient empowerment and prognosis. Importantly, the consensus-based documentation and interoperability specifications developed will support the standardisation of routine patient data collection at the national and international levels, while the ACRIBiS cohort dataset will be available for broad secondary use.
Trial registration: The study is registered at the German study registry (DRKS): #DRKS00034792.
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