Diagnostic accuracy estimates for COVID-19 RT-PCR and Lateral flow immunoassay tests with Bayesian latent class models

Abstract

The objective was to estimate the diagnostic accuracy of real time polymerase chain reaction (RT-PCR) and lateral flow immunoassay (LFIA) tests for COVID-19, depending on the time post symptom onset. Based on the cross-classified results of RT-PCR and LFIA, we used Bayesian latent class models (BLCMs), which do not require a gold standard for the evaluation of diagnostics. Data were extracted from studies that evaluated LFIA (IgG and/or IgM) assays using RT-PCR as the reference method. SeRT−PCR was 0.68 (95% probability intervals: 0.63; 0.73). SeIgG/M was 0.32 (0.23; 0.41) for the first week and increased steadily. It was 0.75 (0.67; 0.83) and 0.93 (0.88; 0.97) for the second and third week post symptom onset, respectively. Both tests had a high to absolute Sp, with higher point median estimates for SpRT−PCR and narrower probability intervals: SpRT−PCR was 0.99 (0.98; 1.00) and SpIgG/M was 0.97 (0.92; 1.00), 0.98 (0.95; 1.00) and 0.98 (0.94; 1.00) for the first, second and third week post symptom onset. The diagnostic accuracy of LFIA varies with time post symptom onset. BLCMs provide a valid and efficient alternative for evaluating the rapidly evolving diagnostics for COVID-19, under various clinical settings and different risk profiles.