Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores
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
Affiliations
Kim K. Sommer
Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Carl-Neuberg-Straße 1, 30625 Hannover, Germany
Ali Amr
Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
Udo Bavendiek
Department of Cardiology and Angiology, Hannover Medical School, Carl-Neuberg-Straße, 130625 Hannover, Germany
Felix Beierle
Institute of Clinical Epidemiology and Biometry, University of Würzburg, Am Schwarzenberg 15, 97078 Würzburg, Germany
Peter Brunecker
Core Facility IT, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
Henning Dathe
Department of Medical Informatics, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075 Göttingen, Germany
Jürgen Eils
Center Digital Health, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
Department of Cardiology and Pneumology/Heart Center, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075 Göttingen, Germany
Peter Heuschmann
Institute of Clinical Epidemiology and Biometry, University of Würzburg, Am Schwarzenberg 15, 97078 Würzburg, Germany
Jennifer D. E. Hoos
Core Facility IT, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
Tibor Kesztyüs
Department of Medical Informatics, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075 Göttingen, Germany
Fabian Kerwagen
Department Klinische Forschung und Epidemiologie, Deutsches Zentrum für Herzinsuffizienz, Am Schwarzenberg 15, 97078 Würzburg, Germany
Aljoscha Kindermann
Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
Dagmar Krefting
Department of Medical Informatics, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075 Göttingen, Germany
Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Carl-Neuberg-Straße 1, 30625 Hannover, Germany
Benjamin Meder
Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
Angela Merzweiler
Institute of Medical Informatics, Heidelberg University Hospital, Im Neuenheimer Feld 130.3, 69120 Heidelberg, Germany
Fabian Prasser
Medical Informatics Group, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
Rüdiger Pryss
Institute of Clinical Epidemiology and Biometry, University of Würzburg, Am Schwarzenberg 15, 97078 Würzburg, Germany
Jendrik Richter
Department of Medical Informatics, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075 Göttingen, Germany
Philipp Schneider
Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
Stefan Störk
Department Klinische Forschung und Epidemiologie, Deutsches Zentrum für Herzinsuffizienz, Am Schwarzenberg 15, 97078 Würzburg, Germany
Christoph Dieterich
Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
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.
