ABSTRACTCaused by the tick-borne spirochete, Borrelia burgdorferi, Lyme disease (LD) is the most common vector-borne infectious disease in North America and Europe. Though timely diagnosis and treatment are effective in preventing disease progression, current tests are insensitive in early-stage LD, with a sensitivity <50%. Additionally, the serological testing currently recommended by the US Center for Disease Control has high costs (>$400/test) and extended sample-to-answer timelines (>24 hours). To address these challenges, we created a cost-effective and rapid point-of-care (POC) test for early-stage LD that assays for antibodies specific to seven Borrelia antigens and a synthetic peptide in a paper-based multiplexed vertical flow assay (xVFA). We trained a deep learning-based diagnostic algorithm to select an optimal subset of antigen/peptide targets, and then blindly-tested our xVFA using human samples (N(+) = 42, N(−)= 54), achieving an area-under-the-curve (AUC), sensitivity, and specificity of 0.950, 90.5%, and 87.0% respectively, outperforming previous LD POC tests. With batch-specific standardization and threshold tuning, the specificity of our blind-testing performance improved to 96.3%, with an AUC and sensitivity of 0.963 and 85.7%, respectively.
Point-of-care serodiagnostic test for early-stage Lyme disease using a multiplexed paper-based immunoassay and machine learning
