Patients know best: DESTINIES Patient Panel appraisal of a novel, risk-stratified approach to COVID-19 disease surveillance in the adult immunosuppressed

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ID: 329202
2026
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Abstract
Abstract Objectives The immunosuppressed are poorly represented in disease surveillance and pharmacovigilance dataflows, proving especially consequential during the COVID-19 pandemic. This mixed methods study recruited a panel of immunosuppressed patients (DESTINIES Patient Panel) to quality assure a new digital phenotype for COVID-19 risk in the adult immunosuppressed (DESTINIES phenotype) prior to its implementation in real-world surveillance. Methods Applying their lived experiences, the DESTINIES Patient Panel met to critique the strategy and structure of the provisional phenotype before voting on its adoption in UK disease surveillance. They did so anonymously. This convening was hosted and recorded on Zoom; Google Forms collected panellist feedback and their final ratifying vote. Feedback was inductively content analysed. Results 33 patients that adhered to the DESTINIES phenotype definition of immunosuppression submitted their feedback and final vote. Despite obtaining expressions of interest, no person with drug-managed HIV participated; this was a significant limitation given their unique positioning in the phenotype. However, 88% of panellists approved the DESTINIES phenotype for adoption in the UK, 91% agreed it represented a step forward for COVID-19 disease surveillance and 91% agreed to see it trialled in other surveillance pathways (RSV, pneumonia etc.). Panellist feedback was catalogued by themes of ‘Abandonment’, ‘Accessibility’, ‘Accuracy’, and ‘Omission’. Conclusion The patient voice is often absent in the dataflows purporting to be in their interest. This is an oversight; certain limitations captured in this exercise were unlikely to have surfaced without patient engagement. The DESTINIES phenotype is made more robust by the collective criticism of expert clinicians and patients alike.
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openalex_W7213633605 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors
Journal Oxford Open Immunology
Year 2026
DOI
10.1093/oxfimm/iqag015
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