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Imaging Quality Control, Methodology Harmonization and Clinical Data Management in Stress Echo 2030

Stress echo (SE) 2030 study is an international, prospective, multicenter cohort study that will include >10,000 patients from ≥20 centers from ≥10 countries. It represents the logical and chronological continuation of the SE 2020 study, which developed, validated, and disseminated the “ABCDE pro...

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Detalles Bibliográficos
Autores principales: Bartolacelli, Ylenia, Barbieri, Andrea, Antonini-Canterin, Francesco, Pepi, Mauro, Monte, Ines Paola, Trocino, Giuseppe, Barchitta, Agata, Cresti, Alberto, Miceli, Sofia, Petrella, Licia, Benedetto, Frank, Zito, Concetta, Benfari, Giovanni, Bursi, Francesca, Malagoli, Alessandro, Mantovani, Francesca, Ciampi, Quirino, Zagatina, Angela, Palinkas, Eszter Dalma, Palinkas, Attila, Toth, Szilvia Rostasne, Wierzbowska-Drabik, Karina, Djordievic-Dikic, Ana, Pellikka, Patricia A., Picano, Eugenio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305320/
https://www.ncbi.nlm.nih.gov/pubmed/34300186
http://dx.doi.org/10.3390/jcm10143020
Descripción
Sumario:Stress echo (SE) 2030 study is an international, prospective, multicenter cohort study that will include >10,000 patients from ≥20 centers from ≥10 countries. It represents the logical and chronological continuation of the SE 2020 study, which developed, validated, and disseminated the “ABCDE protocol” of SE, more suitable than conventional SE to describe the complex vulnerabilities of the contemporary patient within and beyond coronary artery disease. SE2030 was started with a recruitment plan from 2021 to 2025 (and follow-up to 2030) with 12 subprojects (ranging from coronary artery disease to valvular and post-COVID-19 patients). With these features, the study poses particular challenges on quality control assurance, methodological harmonization, and data management. One of the significant upgrades of SE2030 compared to SE2020 was developing and implementing a Research Electronic Data Capture (REDCap)-based infrastructure for interactive and entirely web-based data management to integrate and optimize reproducible clinical research data. The purposes of our paper were: first, to describe the methodology used for quality control of imaging data, and second, to present the informatic infrastructure developed on RedCap platform for data entry, storage, and management in a large-scale multicenter study.