Testing Can't Promise You a Rose Garden. But What Can It Promise?
Michael A. Gelman
Developing a pandemic preparedness strategyfor the workplace is critical in control of COVID-19. Today's guest author, Michael Gelman, MD PhD, discusses the how the application of the latest medical information can achieve that goal.
That's why I'm excited to read this preprint. It was written by researchers at Johns Hopkins and employees of Becton Dickinson, which makes a rapid antigen test. The key figure is on page 21 of the PDF. In the top panel, 38 PCR-positive samples were sorted into positives and negatives by viral culture in TEMPRSS2-expressing cells, which seem to be the best surrogate for infectiousness that we have at present. Antigen tests were positive in all but one of 28 culture-positive samples, and negative in all but two of 10 culture-negative samples. The Y-axis on the graph is viral load as computed from PCR time-to-positivity. What this tells us is that, in this group of just over three dozen PCR-positive samples, antigen predicted culture almost all the time.
The bottom panel of the figure, using statistical modeling, makes this case more strongly. The red band shows that PCR is sensitive down to very low levels of RNA, but the disadvantage is that PCR identifies residual RNA in people who are no longer infectious. On the other hand, the yellow band (antigen) and the purple band (TMPRSS2-expressing cell culture) overlap reasonably well, suggesting that antigen reflects infectiousness. That's a very encouraging result. But what's still missing?
This study does have only a few dozen data points. The PCR-negative samples were not cultured, which means we didn't get the opportunity to see if any of the PCR-negatives were false negatives. There are no antibody-curve data, which could pick up infections that were missed because of poor sample collection technique - and could give us a sense of the timing of the infection. And the data points are not resolved out on the time axis. That means that we aren't sure whether those low-positive RNA's with negative antigen and negative culture are from two months after infection, or from one day before someone became infectious.
Time-resolved data would allow us to separate out the upslope of infectivity, where the goal is to minimize false negatives and capture everyone who may be on the way to a superspreader state, from the downslope of infectivity, where the goal is to clear patients who are no longer infectious. And just to note, New York State still requires a negative PCR before someone can be discharged from a hospital to a nursing home - that equals a lot of people stuck at a higher level of care than they need, waiting weeks to months for their PCR to become negative. Time-resolved data would also give us valuable information on the relationship between testing frequency and risk reduction - how often do we need to test in order to catch 90% of positives? 95%? 99%? I would ask about 100% - but that would be a promise that we would have another Rose Garden.
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