Sunday, 26 Mar 2023

Some COVID-19 tests available on the market could miss a majority of cases

A research team at Beth Israel Deaconess Medical Center has developed a mathematical model to estimate the false negative rate for COVID-19 tests.  

According to a study published earlier this month in Clinical Infectious Diseases, the assay limit of detection (or the smallest amount of viral DNA detectable that a test will catch 95% or more of the time) meaningfully affects clinical performance of COVID-19 tests.  

The highest LoDs on the market will miss a majority of infected patients, the researchers found.  

“For getting back to business as usual, we all agree we’ve got to massively ramp up testing to figure out who’s negative and who’s infectious –  but that’s only going to work optimally if you can catch all the positive cases,” said co-corresponding author Dr. James E. Kirby, director of the Clinical Microbiology Laboratories at BIDMC, in a press release announcing the findings.

“We found that clinical sensitivities vary widely, which has clear implications for patient care, epidemiology and the social and economic management of the ongoing pandemic,” Kirby continued.  


The team used data from 27,098 tests performed at Beth Israel Lahey Health hospital sites between March 26 and May 2 of this past year. They noted that viral loads from patients can vary, and the lower the viral load, the greater the likelihood the infection will be missed.  

“Concerningly, some of these missed patients are, have been, or will become infectious, and such misses will undermine public health efforts and put patients and their contacts at risk,” wrote the researchers in the study.  

The team analyzed repeat test results for the nearly 5,000 patients who tested positive and determined that the in-house test provided a false negative in about 10 percent of cases.  

According to the study, the team then developed a sliding scale relationship to predict the clinical sensitivities of assays based on their limits of detection.  

“We confirmed the validity of our mathematical model by comparison with a calibration curve established through testing of serial dilutions of an inactivated SARS-CoV-2 virus reference material,” wrote the researchers.  

They found that each 10-fold increase in LoD is expected to lower assay sensitivity by 13%. An assay with an LoD of 1,000 copies viral DNA per ML will provide one out of every four people with a false negative. 

The press release notes that the team showed that one test available today could miss as many as one in three infected individuals, while another may miss up to 60 percent of positive cases.   

“These misses will undermine public health efforts and put patients and their contacts at risk,” said said co-corresponding author Dr. Ramy Arnaout, associate director of the Clinical Microbiology Laboratories at BIDMC. “This must give us pause, and we really need to benchmark each new test even in our rush to increase testing capacity to understand how well they support our testing goals.”  


Many policy leaders – including President Joe Biden – have pointed to increased testing as an integral part of continued COVID-19 response strategy over the coming year. The U.S. Food and Drug Administration has also approved a slew of COVID-19 tests over the past year, many aimed at allowing individuals to test themselves in their own home.  

Other health systems have turned to alternate methods, such as an artificial intelligence test developed by the University of Oxford that correctly predicted the COVID-19 status of 92.3% of patients coming to emergency departments at two English hospitals over a two-week period.  

But the study results suggest – as other experts have – that testing alone will not be enough to stop the continued spread of COVID-19.  


“These results are especially important as we transition from testing mostly symptomatic individuals to more regular screening across the community,” said Arnaout.  

“How many people will be missed – the false negative rate – depends on which test is used. With our model, we are better informed to ask how likely these people are to be infectious,” Arnaout continued.

Kat Jercich is senior editor of Healthcare IT News.
Twitter: @kjercich
Email: [email protected]
Healthcare IT News is a HIMSS Media publication.

Source: Read Full Article