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Background:  Point-of-care tests (POCTs) for influenza have been criticised for their diagnostic accuracy, with clinical use limited by low sensitivity. These criticisms are based on diagnostic-accuracy studies that often use the questionable assumption of an infallible gold standard. Bayesian latent class modelling can estimate diagnostic performance without this assumption.  Methods:  Data extracted from published diagnostic-accuracy studies comparing the QuickVue® influenza A+B influenza POCT to reverse-transcriptase polymerase chain reaction (RT-PCR) in two different populations were re-analysed. Classical and Bayesian latent class methods were applied using the Modelling for Infectious diseases CEntre (MICE) web-based application.  Results:  Under classical analyses the estimated sensitivity and specificity of the QuickVue® were 66.9% (95% confidence interval (CI) 61.4-71.9) and 97.8% (95% CI 95.7-98.9), respectively. Bayesian latent class models estimated sensitivity of 97.8% (95% credible interval (CrI) 82.1-100) and specificity of 98.5% (95% CrI 96.5-100).  Conclusions:  Data from studies comparing the QuickVue® point-of-care test to RT-PCR are compatible with better diagnostic performance than previously reported.

Better than we thought? The diagnostic performance of an influenza point-of-care test in children, a Bayesian re-analysis