Sommer Kim K, Amr Ali, Bavendiek Udo, Beierle Felix, Brunecker Peter, Dathe Henning, Eils Jürgen, Ertl Maximilian, Fette Georg, Gietzelt Matthias, Heidecker Bettina, Hellenkamp Kristian, Heuschmann Peter, Hoos Jennifer D E, Kesztyüs Tibor, Kerwagen Fabian, Kindermann Aljoscha, Krefting Dagmar, Landmesser Ulf, Marschollek Michael, Meder Benjamin, Merzweiler Angela, Prasser Fabian, Pryss Rüdiger, Richter Jendrik, Schneider Philipp, Störk Stefan, Dieterich Christoph
Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Carl-Neuberg-Straße 1, 30625 Hannover, Germany.
Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany.
Life (Basel). 2022 May 18;12(5):749. doi: 10.3390/life12050749.
Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.
心力衰竭（HF）患者的风险预测对于改善针对个体患者的预防、诊断和治疗策略的定制，以及有效利用医疗保健资源至关重要。从对照临床研究中得出的风险评分可用于计算死亡率和HF住院风险。然而，这些评分在常规护理中实施情况不佳，主要原因是其计算在实践中需要相当大的工作量，且必要数据通常无法以可互操作的格式获取。在这项工作中，我们展示了一种多站点解决方案的可行性，该方案可从临床常规数据中得出并计算两个示例性HF评分（具有六个连续变量和八个分类变量的MAGGIC评分；具有五个连续变量和六个分类变量的巴塞罗那生物-HF评分）。在德国医学信息学倡议联盟HiGHmed内，我们实施了一种可互操作的解决方案，在openEHR框架内收集统一的HF表型核心数据集（CDS）。我们的方法通过从主要系统自动检索数据，最大限度地减少了手动数据输入的需求。我们展示了，在五个参与的医疗中心，为执行专用数据查询、随后进行统一数据处理和评分计算而实施的结构在实践中运行良好。总之，我们证明了跨多个合作站点使用临床常规数据来计算HF风险评分的可行性。该解决方案可扩展到临床护理中的广泛应用。
