Kim K. Sommer, Ali Amr, Udo Bavendiek, Felix Beierle, Peter Brunecker, Henning Dathe, Jürgen Eils, Maximilian Ertl, Georg Fette, Matthias Gietzelt, Bettina Heidecker, Kristian Hellenkamp, Peter Heuschmann, Jennifer D. E. Hoos, Tibor Kesztyüs, Fabian Kerwagen, Aljoscha Kindermann, Dagmar Krefting, Ulf Landmesser, Michael Marschollek, Benjamin Meder, Angela Merzweiler, Fabian Prasser, Rüdiger Pryss, Jendrik Richter, Philipp Schneider, Stefan Störk, Christoph Dieterich

• 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 interoperableRisk 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.…