Diagnostic Accuracy of Novel and Traditional Rapid Tests for Influenza Infection Compared With Reverse Transcriptase Polymerase Chain Reaction

Influenza viruses cause yearly epidemics of acute respiratory illness affecting 5% to 30% of the population (1, 2). Diagnosing influenza on the basis of only clinical symptoms is difficult because manifestations vary and are nonspecific (3). Consequently, results of diagnostic tests are useful to guide clinical management. Potential benefits of rapid and accurate diagnosis of influenza infection include prompt initiation of antiviral therapy (46), fewer ancillary diagnostic tests (7, 8), fewer hospitalizations (4, 9), prompt institution of hospital infection control measures (10), and less unnecessary antibiotic use (7, 11). Diagnostic tests for influenza identify the virus in a patient's respiratory secretions by isolation in cell culture, detection of viral RNA by nucleic acid amplification, or detection of viral antigens by immunoassay (10). Reverse transcriptase polymerase chain reaction (RT-PCR) has replaced viral culture as the gold standard for influenza diagnosis because of its superior analytic and clinical sensitivity (12, 13). However, specimens for RT-PCR are typically sent to specialized laboratories, and testing is done in batches, resulting in turnaround times that extend beyond the clinical encounter. Rapid influenza diagnostic tests (RIDTs) that detect viral antigens by immunoassay are widely used because they are simple enough to do at the point of care and provide results in less than 30 minutes. A 2012 systematic review and meta-analysis by Chartrand and colleagues evaluated 159 diagnostic accuracy studies of RIDTs published to December 2011 (14). They showed that commercially available RIDTs had high specificity (98.2% [95% CI, 97.5% to 98.7%]) but poor sensitivity (62.3% [CI, 57.9% to 66.6%]). In light of these findings, regulators and professional societies have questioned the utility of RIDTs (13, 1517). Since 2011, the following 2 novel classes of rapid influenza diagnostic assays (that is, with results available in <30 minutes) have been commercialized, with claims of improved sensitivities based on technological improvements: automated immunochromatographic antigen detection tests (digital immunoassays [DIAs]) and rapid nucleic acid amplification tests (NAATs). Digital immunoassays use an instrument-based digital scan of the test strip to enhance antigen detection accuracy by eliminating the need for an operator to visualize and subjectively interpret test results (16). Rapid NAATs use a modified RT-PCR (18) or isothermal amplification technology (19) to greatly reduce analytic times. In February 2017, the U.S. Food and Drug Administration (FDA) reclassified and instituted minimum performance standards for rapid influenza tests (17). Therefore, an updated and comprehensive synthesis of the evidence on their accuracy is warranted. The primary objective of this systematic review and meta-analysis was to estimate and compare the diagnostic accuracy of commercialized RIDTs, DIAs, and rapid NAATs for detecting influenza A and B infection in patients with suspected influenza, compared with an RT-PCR reference standard. We also aimed to evaluate patient, test, and methodological factors associated with test accuracy within each of the 3 classes of rapid tests. Methods We used methods recommended by the Cochrane Diagnostic Test Accuracy Working Group (20, 21), including the preparation of a prespecified protocol and analysis plan developed according to the PRISMA-P (Preferred Reporting Items for Systematic reviews and Meta-analyses Protocols) statement (22). The PRISMA guidelines were used for preparing this report (23). Data Sources and Searches On the basis of the PubMed search strategy in Chartrand and colleagues' systematic review (14) and in collaboration with a medical librarian (G.G.), we searched PubMed, Embase, BIOSIS Previews, Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials on 18 August 2015, with an update on 21 May 2017 (Supplement Table 1). We used EndNote (Clarivate Analytics) libraries from Chartrand and colleagues' systematic review (14) to exclude records they had screened and excluded while keeping studies they had included, provided the reference standard used was RT-PCR. In addition, we hand-searched recent guidelines, narrative reviews, and citations of included articles. Supplement. Supplementary Material Study Selection We included peer-reviewed studies in English, French, or Spanish providing original data on the diagnostic accuracy of rapid influenza tests against an RT-PCR reference standard. Eligible participants were children and adults with clinically suspected influenza during periods of influenza activity. Editorials, letters to the editor, and conference abstracts were excluded because they contain insufficient information on important data items for investigating sources of heterogeneity and ascertaining methodological quality. Studies using a casecontrol design (spectrum bias) and those performing the reference standard depending on index test results (partial verification bias) were also excluded. We attempted to contact authors if studies provided insufficient information to construct a 22 table. Rapid influenza tests were defined as commercially developed assays that detect influenza A, B, or A/B within 30 minutes by identifying influenza viral antigen or RNA directly from an unprocessed specimen. Acceptable specimens included nasopharyngeal aspirates, swabs, or washes; nasal aspirates, swabs, or washes; and throat swabs. For a study to be eligible, the index test and comparator needed to test the same clinical specimen or 2 specimens taken concurrently from the same anatomical site. Commercial and laboratory-developed RT-PCR assays were acceptable reference tests. When more than 1 RT-PCR assay was used as a reference standard, preference was given to the commercial assay with the best reported analytic sensitivity for influenza A. We excluded studies if the rapid test itself was part of a composite reference standard (incorporation bias). Two reviewers (R.W. and J.M.) independently screened citations (titles and abstracts) identified by our search strategy and not already screened by Chartrand and colleagues. Potentially relevant articles were retrieved in full and screened for eligibility by the 2 reviewers. Disagreements were resolved by consensus or by involvement of a third reviewer (J.P.). Data Extraction and Quality Assessment A data extraction sheet (Supplement Table 2) based on the form used by Chartrand and colleagues was created in Google Forms to minimize the risk for transcriptional errors (24). It was then pilot-tested on a subset of included articles by 2 reviewers (J.M. and R.W.) before being finalized. Two reviewers independently extracted data. Disagreements were resolved by consensus or by a third reviewer (J.P.). Articles that assessed several index tests against a reference standard were counted as several studies; a separate extraction form was completed for each index test. The study population was considered pediatric or adult if 85% of persons were below or above, respectively, an age cutoff of 18 to 21 years (as defined by the study). In studies that provided separate results for children and adults, we used the age cutoff applied by the investigators. Point-of-care testing was defined as index testing done outside the traditional laboratory setting by persons other than trained laboratory personnel. We considered a study to have been industry-sponsored if a commercial entity funded it or provided index tests. Two reviewers independently assessed the quality of individual studies using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) criteria (25). Data Synthesis and Analysis For each study, we calculated sensitivity, specificity, and positive and negative likelihood ratios (LRs) along with 95% CIs. Results are presented separately for influenza A and B and for each of the 3 index test types (RIDTs, DIAs, and rapid NAATs). We considered influenza A and B to be separate diagnostic targets; studies that reported only combined influenza A/B data were not included in the quantitative synthesis. We calculated the pooled accuracy estimates (sensitivity, specificity, and LRs) across studies with 95% credible intervals (CrIs) using Bayesian bivariate random-effects meta-analysis models (details in the Supplement). The bivariate random-effects approach deals with potential sources of variation caused by imprecision of sensitivity and specificity estimates within individual studies, correlation between sensitivity and specificity across studies, and variation in sensitivity and specificity between studies (26). Because heterogeneity is expected in meta-analysis of diagnostic accuracy studies, a random-effects model is preferred (21). Analyses were done using noninformative priors. We also used the model to create a plot depicting the pooled estimates with the credible and prediction regions and a hierarchical summary receiver-operating characteristic (HSROC) curve (20, 27, 28). We first assessed heterogeneity by visual inspection of the HSROC curves and the credible and prediction regions (20). Subgroup analyses were planned to further investigate heterogeneity for covariates that provided at least 3 studies per stratum by index test type. Variables selected a priori as potential sources of heterogeneity were population age (children vs. adults), duration of symptoms before testing, type of respiratory specimen, point-of-care testing, commercial brand, infecting virus subtype, study quality, and industry sponsorship. Summary sensitivity and specificity estimates were calculated for each level of a covariate, along with their 95% CrIs. Differences in accuracy were then compared across levels of a covariate. Analyses were done using STATA, version 13 (StataCorp); R, version 3.2. 1 (R Foundation; www.r-project.org); and WinBUGS, version 1.4. 3 (29). Researchers usually assume that RT-PCR is a perfec