Developing a minimum dataset for a national patient registry on Long COVID in Canada: a Delphi consensus-based study

To develop survey items for a national patient registry on Long COVID using a modified Delphi process.

This study was based on a modified Delphi process involving three rounds of anonymous, online surveys to develop consensus on and prioritise survey elements to be included in a minimum dataset for use in a national patient registry in Canada. Initial Long COVID items were identified through an environmental scan of the literature.

This study focused on healthcare systems in Canada and was conducted online.

A panel of 52 experts (patients, caregivers, clinicians and researchers) participated in all three rounds of the online survey. These participants were recruited through the Long COVID Web network and word of mouth.

In total, 243 survey elements related to care, quality of life and symptoms were included in round 1 of the survey. 200 reached consensus and moved to round 2 with two additional elements being developed based on open-ended responses. In round 2, participants ranked these survey elements and 34 advanced. In round 3, 33 survey elements met the threshold of consensus with one added a priori. The 33 survey elements were then used to develop a Long COVID minimum dataset, which consists of 48 items.

The findings affirm broad consensus for collecting data related to fatigue, post-exertional malaise, cardiovascular issues, respiratory problems and cognitive issues. This highlighted the desire for quality-of-life indicators and information related to care utilisation, quality and access.