Understanding and confidence in handling through data review
COVID-19 respiratory tract infections, viral syndrome
SARS-COV-2 INFECTION, ASYMPTOMATIC SARS-COV-2 INFECTION, COVID-19 PNEUMONIA, DISEASE, EVENT, COVID-19 INFECTION, POST VIRAL SYNDROME, EVENTS, VIRAL SYNDROME, EVENTS, POST VIRAL SYNDROME
The frequent interim data QC by the clinical, data management, and pharmacometric teams was a time-saving investment. All records were subject to check after entry into the eCRF, and the clinical QC and data management combined found in 20.9% of the records that a correction was needed when the eCRF was compared to the paper source document. A total of 201 queries accounting for 10.7% of total records were found by the pharmacometrics team and resolved while the trial was ongoing. The last data check after the trial completed only resulted in 4 additional queries which were resolved in two days, after which the data could be locked. In addition to saving time after study completion, addressing queries while the study is still ongoing was also found to be advantageous because an incorrect measurement (e.g. weight) can still be re-measured and recorded. Pharmacometric analysis (magenta hexagon in Fig. Data review was challenging because of the large size of the database. The full database consisted of 20,457 records. Figure Number of eCRFs submitted per master database. Dataset architecture consisted of 4 master databases (Screening/enrolment, Events, Lab results, and Follow-up) for which the number of records is shown.Most important in the pharmacometric data review was the understanding of the clinical meaning of the data entries. For example, COVID-19 was defined as a symptomatic disease with confirmed SARS-CoV-2 infection. As such, a COVID-19 event with a health status score of 0, or a polymerase chain reaction (PCR) confirmed asymptomatic SARS-CoV-2 infections with a health status of 1 or higher, would result in a query directed to the clinical team on how to interpret these results. The records would subsequently be corrected in the next data transfer for the health status score to reflect the event definition. Another example was post viral syndrome, i.e. long COVID. A record without a preceding COVID-19 event would also result in a query.The consistency review between the different databases and datasets mostly focussed on the Events master database. The weekly health status score was captured in two different datasets; in the original Events dataset for the first observation(s) and thereafter in the Follow-up dataset. In the integrated datasets, these weekly health status scores were merged and checked for consistency. Where different health status scores for a single week were reported, or where the number of weekly scores did not equal the number of weeks an event was ongoing, a query was opened. Each event had a unique event number, so duplicate event numbers were flagged to the data management team. Consistency between Follow-up and Events master databases was important because participants self-reported COVID-19 events during the follow-up contact, which would result in a record in the Events master database when symptomatic. Consistency of dates between the Lab, Follow-up, and Events master databases was checked to prevent ongoing events after trial completion.Records were checked for missing or not applicable (NA) values. Additionally, dates (negative timepoints, the same record with different dates), MedDRA event descriptions, and spelling were checked. Spelling was a noteworthy issue where COVID-19 was recorded with 63 different spelling alternatives, including COVID-19, COVID 19, COVID, COVID-19 infection, COVID-19 pneumonia, COVID-19 respiratory tract infections, while post COVID viral syndrome was recorded in 10 different alternatives. Therefore, the MedDRA term initially utilized, but unfortunately also contained two alternative spellings for both. From this insight, the MedDRA numerical codes were included into the data processing.The initial analysis workflow evolved over time with new information and methods arising during the pandemic which were unknown at database setup. Post viral syndrome after COVID-19, also coined long COVID