A new combination testing methodology to identify accurate and economical point-of-care testing strategies

Background: Quick, cheap and accurate point-of-care testing is urgently needed to enable frequent, large-scale testing to contain COVID-19. Lateral flow tests for antigen and antibody detection are an obvious candidate for use in community-wide testing, because they are quick and cheap relative to lab-processed tests. However, their low accuracy has limited their adoption. We develop a new methodology to increase the diagnostic accuracy of a combination of cheap, quick and inaccurate index tests with correlated or discordant outcomes, and illustrate its performance on commercially available lateral flow immunoassays (LFIAs) for Sars-CoV-2 antibody detection. Methods and Findings: We analyze laboratory test outcomes of 300 serum samples from health care workers detected with PCR-confirmed SARS-Cov-2 infection at least 21 days prior to sample collection, and 500 pre-pandemic serum samples, from a national seroprevalence survey, tested using eight LFIAs (Abbott, Biosure/Mologic, Orientgene-Menarini, Fortress, Biopanda I, Biopanda II, SureScreen and Wondfo) and Hybrid DABA as reference test. For each of 14 two-test combinations (e.g., Abbott, Fortress) and 16 three-test combinations (e.g., Abbott, Fortress, Biosure/Mologic) used on at least 100 positive and 100 negative samples, we classify an outcome sequence -- e.g., (+,-) for (Abbott, Fortress) -- as positive if its combination positive predictive value (CPPV) exceeds a given threshold, set between 0 and 1. Our main outcome measures are the sensitivity and specificity of different classification rules for classifying the outcomes of a combination test. We define testing possibility frontiers which represent sensitivity and false positive rates for different thresholds. The envelope of frontiers further enables test selection. The eight index tests individually meet neither the UK Medicines and Healthcare Products Regulatory Agency's 98% sensitivity and 98% specificity criterion, nor the US Center for Disease Control's 99.5% specificity criterion. Among these eight tests, the highest single-test LFIA specificity is 99.4% (with a sensitivity of 65.2%) and the highest single-test LFIA sensitivity is 93.4% (with a specificity of 97.4%). Using our methodology, a two-test combination meets the UK Medicines and Healthcare Products Regulatory Agency's criterion, achieving sensitivity of 98.4% and specificity of 98.0%. While two-test combinations meeting the US Center for Disease Control's 99.5% specificity criterion have sensitivity below 83.6%, a three-test combination delivers a specificity of 99.6% and a sensitivity of 95.8%. Conclusions: Current CDC guidelines suggest combining tests, noting that 'performance of orthogonal testing algorithms has not been systematically evaluated' and highlighting discordant outcomes. Our methodology combines available LFIAs to meet desired accuracy criteria, by identifying testing possibility frontiers which encompass benchmarks, enabling cost savings. Our methodology applies equally to antigen testing and can greatly expand testing capacity through combining less accurate tests, especially for use cases needing quick, accurate tests, e.g., entry to public spaces such as airports, nursing homes or hospitals.