“通过结构化临床文档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、项目规划及首次实施经验

The 'Advancing Cardiovascular Risk Identification with Structured Clinical Documentation and Biosignal Derived Phenotypes Synthesis' project: conceptual design, project planning, and first implementation experiences.

作者信息

Felbel Dominik, Prüser Merten, Schmidt Constanze, Schreiweis Björn, Spicher Nicolai, Rottbauer Wolfgang, Varghese Julian, Zietzer Andreas, Störk Stefan, Dieterich Christoph, Krefting Dagmar, Martens Eimo, Sedlmayr Martin, Bongiovanni Dario, Olivier Christoph B, Lapp Hendrik, Schmidt Hannes H J G, Katzmann Julius L, Nensa Felix, Frey Norbert, Ulrich-Merzenich Gudrun S, Peter Carina A, Heuschmann Peter, Bavendiek Udo, Zenker Sven

机构信息

Department of Cardiology, Ulm University Heart Center, Ulm, Germany.

Department of Cardiology, University Hospital Heidelberg, Heidelberg, Germany.

出版信息

Eur Heart J Digit Health. 2025 Jun 30;6(5):1084-1093. doi: 10.1093/ehjdh/ztaf075. eCollection 2025 Sep.

DOI:10.1093/ehjdh/ztaf075

PMID:40984993

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12450505/

Abstract

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.

摘要

目的

心血管疾病指南推荐使用个性化风险评估工具（PRT）来定制预防、诊断和治疗方案。然而，PRT在临床常规实践中的应用情况不佳。ACRIBiS（通过结构化临床文档和生物信号衍生表型合成推进心血管风险识别）旨在建立可互操作的基础设施，用于常规数据的标准化记录以及高分辨率生物信号（HRB）的整合，以实现基于数据的风险评估。

方法与结果

根据其预测性能选择已有的心血管疾病风险评分，并以此为基础构建包含与风险相关的临床常规信息的核心心血管数据集。尚未在医学信息学倡议（MII）核心数据集（CDS）FHIR配置文件中体现的数据项将被添加到一个“心脏病学”扩展模块中，以实现最大程度的互操作性。HRB整合将通过用于心电图（ECG）处理的模块化基础设施在每个站点实施。将在ACRIBiS队列中评估PRT的预测性能及其通过HRB整合进行的动态重新校准，该队列由德国15个学术心脏病学部门前瞻性招募的5250名患者组成，随访期为12个月。还将通过开发一个自我评估应用程序来评估并支持将这些风险可视化以改善患者教育的潜力。