Data privacy management and data quality monitoring in the German Centre for Cardiovascular Research's multicentre TranslatiOnal Registry for CardiomyopatHies (DZHK-TORCH)

• Verfasst von: Schwaneberg, Thea [VerfasserIn]
• Verfasst von: Katus, Hugo [VerfasserIn]
• Titel: Data privacy management and data quality monitoring in the German Centre for Cardiovascular Research's multicentre TranslatiOnal Registry for CardiomyopatHies (DZHK-TORCH)
• Verf.angabe: Thea Schwaneberg, Kerstin Weitmann, Andreas Dösch, Claudia Seyler, Thomas Bahls, Lars Geidel, Dana Stahl, Mahsa Lee, Monika Kraus, Hugo A. Katus and Wolfgang Hoffmann
• Umfang: 8 S.
• Fussnoten: Gesehen am 14.05.2018
• Titel Quelle: Enthalten in: European Society of Cardiology: ESC heart failure
• Jahr Quelle: 2017
• Band/Heft Quelle: 4(2017), 4, S. 440-447
• ISSN Quelle: 2055-5822
• DOI: doi:10.1002/ehf2.12168
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1575013088

Abstract

Aims The multicentric TranslatiOnal Registry for CardiomyopatHies (TORCH) of the German Centre for Cardiovascular Research aims to recruit 2300 patients with non-ischemic cardiomyopthies. Methods and results The investigations were performed after standard operating procedures. The data are collected in standardized electronic case report forms provided by the data holding of the central data management of the German Centre for Cardiovascular Research using secuTrial (interActive Systems GmbH, Berlin, Germany). The personal-identifying data and informed consent are collected, stored, and quality-checked by the independent Trusted Third Party in Greifswald. The quality management of the medical data is performed by the data and quality centre Greifswald. In December 2014, the recruitment for TORCH has started. Currently, data and biomaterial from about 1397 patients and more than 74 500 biomaterial aliquots were collected. Regular study centre-specific quality reports address completeness and plausibility of data and provide detailed information about current missing or implausible data entries to improve the data quality by using a query management in addition. Conclusions A regular quality control and reporting improve the data quality in TORCH and will support high-quality data analysis and the translation of research results into routine care.