TY  - JOUR
A1  - Sommer, Kim K.
A1  - Amr, Ali
A1  - Bavendiek, Udo
A1  - Beierle, Felix
A1  - Brunecker, Peter
A1  - Dathe, Henning
A1  - Eils, Jürgen
A1  - Ertl, Maximilian
A1  - Fette, Georg
A1  - Gietzelt, Matthias
A1  - Heidecker, Bettina
A1  - Hellenkamp, Kristian
A1  - Heuschmann, Peter
A1  - Hoos, Jennifer D. E.
A1  - Kesztyüs, Tibor
A1  - Kerwagen, Fabian
A1  - Kindermann, Aljoscha
A1  - Krefting, Dagmar
A1  - Landmesser, Ulf
A1  - Marschollek, Michael
A1  - Meder, Benjamin
A1  - Merzweiler, Angela
A1  - Prasser, Fabian
A1  - Pryss, Rüdiger
A1  - Richter, Jendrik
A1  - Schneider, Philipp
A1  - Störk, Stefan
A1  - Dieterich, Christoph
T1  - Structured, harmonized, and interoperable integration of clinical routine data to compute heart failure risk scores
JF  - Life
N2  - 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.
KW  - medical informatics initiative
KW  - HiGHmed
KW  - medical data integration center
KW  - clinical routine data
KW  - heart failure
KW  - risk prediction scores
KW  - semantic interoperability
KW  - openEHR
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-275239
SN  - 2075-1729
VL  - 12
IS  - 5
ER  -