TY - JOUR
T1 - Optimising Clinical Epidemiology in Disease Outbreaks
T2 - Analysis of ISARIC-WHO COVID-19 Case Report Form Utilisation
AU - ISARIC Clinical Characterisation Group
AU - CCP UK
AU - Mazankowski Heart Institute
AU - PHOSP Collaborative Group
AU - The Western Australian COVID-19 Research Response
AU - Merson, Laura
AU - Duque, Sara
AU - Garcia-Gallo, Esteban
AU - Yeabah, Trokon Omarley
AU - Rylance, Jamie
AU - Diaz, Janet
AU - Flahault, Antoine
AU - Abdalasalam, Sabriya
AU - Abdalhadi, Alaa Abdalfattah
AU - Abdalla, Walaa
AU - Abdalla, Naana Reyam
AU - Abdalrheem, Almthani Hamza
AU - Abdalsalam, Ashraf
AU - Abdeewi, Saedah
AU - Abdelgaum, Esraa Hassan
AU - Abdelhalim, Mohamed
AU - Abdelkabir, Mohammed
AU - Abdukahil, Sheryl Ann
AU - Abdulbaqi, Lamees Adil
AU - Abdulhamid, Widyan
AU - Abdulhamid, Salaheddin
AU - Abdulkadir, Nurul Najmee
AU - Abdulwahed, Eman
AU - Abdunabi, Rawad
AU - Abe, Ryuzo
AU - Abel, Laurent
AU - Abodina, Ahmed Mohammed
AU - Abouelmagd, Khaled
AU - Abrous, Amal
AU - Abu Jabal, Kamal
AU - Abu Salah, Nashat
AU - Abukhalaf, Salsabeel M.A.
AU - Abusalama, Abdurraouf
AU - Abuzaid, Tareg Abdallah
AU - Acharya, Subhash
AU - Acker, Andrew
AU - Adem, Safia
AU - Ademnou, Manuella
AU - Adewhajah, Francisca
AU - Adhikari, Neill K.J.
AU - Adrião, Diana
AU - Yaw Adu, Samuel
AU - Afum-Adjei Awuah, Anthony
AU - Agbogbatey, Melvin
AU - Ageel, Saleh Al
AU - Ahmed, Musaab Mohammed
AU - Ahmed, Aya Mustafa
AU - Ahmed, Shakeel
AU - Alaraji, Zainab Ahmed
AU - Luque, Nestor
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/9
Y1 - 2024/9
N2 - Standardised forms for capturing clinical data promote consistency in data collection and analysis across research sites, enabling faster, higher-quality evidence generation. ISARIC and the World Health Organization have developed case report forms (CRFs) for the clinical characterisation of several infectious disease outbreaks. To improve the design and quality of future forms, we analysed the inclusion and completion rates of the 243 fields on the ISARIC-WHO COVID-19 CRF. Data from 42 diverse collaborations, covering 1886 hospitals and 950,064 patients, were analysed. A mean of 129.6 fields (53%) were included in the adapted CRFs implemented across the sites. Consistent patterns of field inclusion and completion aligned with globally recognised research priorities in outbreaks of novel infectious diseases. Outcome status was the most highly included (95.2%) and completed (89.8%) field, followed by admission demographics (79.1% and 91.6%), comorbidities (77.9% and 79.0%), signs and symptoms (68.9% and 78.4%), and vitals (70.3% and 69.1%). Mean field completion was higher in severe patients (70.2%) than in all patients (61.6%). The results reveal how clinical characterisation CRFs can be streamlined to reduce data collection time, including the modularisation of CRFs, to offer a choice of data volume collection and the separation of critical care interventions. This data-driven approach to designing CRFs enhances the efficiency of data collection to inform patient care and public health response.
AB - Standardised forms for capturing clinical data promote consistency in data collection and analysis across research sites, enabling faster, higher-quality evidence generation. ISARIC and the World Health Organization have developed case report forms (CRFs) for the clinical characterisation of several infectious disease outbreaks. To improve the design and quality of future forms, we analysed the inclusion and completion rates of the 243 fields on the ISARIC-WHO COVID-19 CRF. Data from 42 diverse collaborations, covering 1886 hospitals and 950,064 patients, were analysed. A mean of 129.6 fields (53%) were included in the adapted CRFs implemented across the sites. Consistent patterns of field inclusion and completion aligned with globally recognised research priorities in outbreaks of novel infectious diseases. Outcome status was the most highly included (95.2%) and completed (89.8%) field, followed by admission demographics (79.1% and 91.6%), comorbidities (77.9% and 79.0%), signs and symptoms (68.9% and 78.4%), and vitals (70.3% and 69.1%). Mean field completion was higher in severe patients (70.2%) than in all patients (61.6%). The results reveal how clinical characterisation CRFs can be streamlined to reduce data collection time, including the modularisation of CRFs, to offer a choice of data volume collection and the separation of critical care interventions. This data-driven approach to designing CRFs enhances the efficiency of data collection to inform patient care and public health response.
KW - ISARIC
KW - clinical epidemiology
KW - common data elements
KW - data collection
KW - data management
KW - infectious disease outbreaks
UR - http://www.scopus.com/inward/record.url?scp=85205074166&partnerID=8YFLogxK
U2 - 10.3390/epidemiologia5030039
DO - 10.3390/epidemiologia5030039
M3 - Article
AN - SCOPUS:85205074166
SN - 2673-3986
VL - 5
SP - 557
EP - 580
JO - Epidemiologia
JF - Epidemiologia
IS - 3
ER -