Post-market surveillance of six COVID-19 point-of-care tests using pre-Omicron and Omicron SARS-CoV-2 variants
Bibliographic record
Abstract
Post-market surveillance of test performance is a critical function of public health agencies and clinical researchers that ensures tests maintaining diagnostic characteristics following their regulatory approval. Changes in product quality, manufacturing processes over time, or the evolution of new variants may impact product performance. During the COVID-19 pandemic, a plethora of point-of-care tests (POCTs) was released onto the Canadian market. This study evaluated the performance characteristics of several of the most widely distributed POCTs in Canada, including four rapid antigen tests (Abbott Panbio, BTNX Rapid Response, SD Biosensor, and Quidel QuickVue) and two molecular tests (Abbott ID NOW and Lucira Check IT). All tests were challenged with 149 SARS-CoV-2 clinical positives, including multiple variants up to and including Omicron XBB.1.5, as well as 29 clinical negatives. Results were stratified based on whether the isolate was Omicron or pre-Omicron as well as by reverse transcriptase quantitative PCR Ct value. The test performance of each POCT was consistent with the manufacturers' claims and showed no significant decline in clinical performance against any of the variants tested. These findings provide continued confidence in the results of these POCTs as they continue to be used to support decentralized COVID-19 testing. This work demonstrates the essential role of post-market surveillance in ensuring reliability in diagnostic tools.IMPORTANCEPost-market surveillance of diagnostic test performance is critical to ensure their reliability after regulatory approval. This is especially critical in the context of the COVID-19 pandemic as the use of point-of-care tests (POCTs) became widespread. Our study focused on four rapid antigen tests (Abbott Panbio, BTNX Rapid Response, SD Biosensor, and Quidel QuickVue) and two molecular tests (Abbott ID NOW and Lucira Check IT) that were widely distributed across Canada, assessing their performance using many SARS-CoV-2 variants, including up to Omicron subvariant XBB.1.5. Overall, we found no significant difference in performance against any variant, reinforcing confidence in their use. As concerns in test efficacy have been raised by news outlets, particularly regarding the BTNX Rapid Response, this work is even more timely and crucial. Our research offers insights into the performance of widely used COVID-19 POCTs but also highlights the necessity for post-market surveillance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".