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Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores
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 ris...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147139/ https://www.ncbi.nlm.nih.gov/pubmed/35629415 http://dx.doi.org/10.3390/life12050749 |
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author | 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 |
author_facet | 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 |
author_sort | Sommer, Kim K. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9147139 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91471392022-05-29 Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores 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 Life (Basel) Article 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. MDPI 2022-05-18 /pmc/articles/PMC9147139/ /pubmed/35629415 http://dx.doi.org/10.3390/life12050749 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article 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 Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores |
title | Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores |
title_full | Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores |
title_fullStr | Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores |
title_full_unstemmed | Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores |
title_short | Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores |
title_sort | structured, harmonized, and interoperable integration of clinical routine data to compute heart failure risk scores |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147139/ https://www.ncbi.nlm.nih.gov/pubmed/35629415 http://dx.doi.org/10.3390/life12050749 |
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