External quality assessment of molecular testing of 9 viral encephalitis-related viruses in China

Abstract

BackgroundEastern equine encephalitis virus (EEEV), Western equine encephalitis virus (WEEV), Venezuelan equine encephalitis virus (VEEV), Hendra virus (HeV), Nipah virus (NiV), Yellow fever virus (YFV), West Nile virus (WNV), Saint Louis encephalitis virus (SLEV) and Tick-borne encephalitis virus (TBEV) have been detected in travelers returning to China and potentially pose a serious threat to public health. Real-time reverse transcription polymerase chain reaction (rRT-PCR) plays an important role in the detection of these viruses. Although these viruses are not mainly prevalent in China, occasionally imported cases have been reported with the increase in population mobility and entry-exit activities. Therefore, it is necessary to monitor the ability of major domestic laboratories to detect and identify exotic arbovirus infections in travelers. MethodsAn external quality assessment program for the molecular detection of EEEV, VEEV, WEEV, SLEV, WNV, YFV, TBEV, HeV and NiV was organized. The assessment panel included 26 negative and positive samples with different concentrations of virus-like particles and distributed to 31 laboratories to evaluate the accuracy of virus detection. ResultsAt the laboratory level, 87.5% (7/8, EEEV), 85.7% (12/14, WEEV), 100% (13/13, VEEV), 87.5% (7/8, HeV), 76.5% (13/17, NiV), 92.6% (25/27, YFV), 81.3% (13/16, WNV), 100% (5/5, SLEV) and 75.0% (6/8, TBEV) of the participants were considered “competent”. Of all the results, the false-positive and false-negative rates were 0.3% and 0.7%, respectively. The sensitivity of most detection assays (15/17, 88.2%) was more than 90%. In addition, we observed significantly different cycle threshold values when using primer-probe sets in different target regions to detect EEEV and SLEV. ConclusionsMost laboratories have reliable virus detection capabilities. However, laboratory testing capabilities need to be improved to avoid cross-contamination and to better manage undetected false-negative samples.