Metagenomic Sequencing as a Pathogen-Agnostic Clinical Diagnostic Tool for Infectious Diseases: a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies

Metagenomic sequencing is frequently claimed to have the potential to revolutionize microbiology through rapid species identification and antimicrobial resistance (AMR) prediction. We assess the progress toward these developments. We perform a systematic review and meta-analysis of all published literature on culture-independent metagenomic sequencing for pathogen-agnostic infectious disease diagnostics up to 12 August 2020. Methodologic bias and applicability were assessed using the tool Quadas-2. (Prospero CRD42020163777). A total of 2,023 clinical samples from 13/21 eligible diagnostic test accuracy studies were included in the meta-analysis. Reference standards were culture, molecular testing, clinical decision, or a composite measure. Sensitivity and specificity in the most widely investigated sample types were 90% (95% confidence interval (Cl], 78% to 96%) and 86% (45% to 98%) for blood, 75% (54% to 89%) and 96% (72% to 100%) for cerebrospinal fluid (CSF), and 84% (79% to 88%) and 67% (38% to 87%) for orthopedic samples, respectively. We identified a limited use of controls, especially negative controls which were used in only 62% (13/21) of studies. AMR prediction and comparison to phenotypic results were undertaken in four studies; categorical agreement was 88% (80% to 97%), and very major and major error rates were 24% (8% to 40%) and 5% (0% to 12%), respectively. Better human DNA depletion methods are required; a median 91% (interquartile range (IQR], 82% to 98%; range, 76% to 98%) of sequences was classified as human. The median (IQR; range) time from sample to result was 29 hours (24 to 94; 4 to 144 hours). The reported consumable cost per sample ranged from $130 to $685. There is scope for improving the quality of reporting in clinical metagenomic studies. Although our results are limited by the heterogeneity displayed, our results reflect a promising outlook for clinical metagenomics. Methodological improvements and convergence around protocols and best practices may improve performance in the future.