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Accuracy of Rapid Influenza Diagnostic Tests: A Meta-analysis FREE

Caroline Chartrand, MD, MSc; Mariska M.G. Leeflang, DVM, PhD; Jessica Minion, MD, MSc; Timothy Brewer, MD, MPH; and Madhukar Pai, MD, PhD
[+] Article and Author Information

Acknowledgment: The authors thank all of the study authors who provided additional information or clarifications.

Grant Support: The study was supported in part by the Canadian Institutes of Health Research. Dr. Pai is a recipient of a New Investigator Career Award from the Canadian Institutes of Health Research. He also receives research support from Grand Challenges Canada and European and Developing Countries Clinical Trials Partnership. Dr. Leeflang is supported by the Netherlands Organization for Scientific Research (NWO); project 916.10.034.

Potential Conflicts of Interest: None disclosed. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M11-2863.

Requests for Single Reprints: Madhukar Pai, MD, PhD, Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Respiratory Epidemiology & Clinical Research Unit, Montreal Chest Institute, 1020 Pine Avenue West, Montreal, Quebec H3A 1A2, Canada; e-mail, mailto:madhukar.pai@mcgill.ca.

Current Author Addresses: Dr. Chartrand: Department of Pediatrics, CHU Sainte-Justine, 3175 Chemin Côte Sainte-Catherine, Montreal, Quebec H3T 1C5, Canada.

Dr. Leeflang: Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, PO Box 22700, Amsterdam 1100, the Netherlands.

Dr. Minion: Department of Medical Microbiology & Immunology, University of Alberta, Faculty of Medicine & Dentistry, 2B3.09 Walter Mackenzie Centre, Edmonton, Alberta T6G 2H7, Canada.

Dr. Brewer: Director, Global Health Programs, McGill University Medical School, 1020 Pine Avenue West, Room 42, Montreal, Quebec H3A 1A2, Canada.

Dr. Pai: Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Respiratory Epidemiology & Clinical Research Unit, Montreal Chest Institute, 1020 Pine Avenue West, Montreal, Quebec H3A 1A2, Canada.

Author Contributions: Conception and design: C. Chartrand, J. Minion, M. Pai.

Analysis and interpretation of the data: C. Chartrand, M.M.G. Leeflang, J. Minion, T. Brewer, M. Pai.

Drafting of the article: C. Chartrand, M. Pai.

Critical revision of the article for important intellectual content: M.M.G. Leeflang, J. Minion, T. Brewer, M. Pai.

Final approval of the article: C. Chartrand, M.M.G. Leeflang, J. Minion, T. Brewer, M. Pai.

Statistical expertise: M.M.G. Leeflang.

Obtaining of funding: T. Brewer, M. Pai.

Collection and assembly of data: C. Chartrand, J. Minion.


From CHU Sainte-Justine, Université de Montréal, Montreal Chest Institute, and McGill University, Montreal, Quebec, Canada; Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands; and University of Alberta, Edmonton, Alberta, Canada.


Ann Intern Med. 2012;156(7):500-511. doi:10.7326/0003-4819-156-7-201204030-00403
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Background: Timely diagnosis of influenza can help clinical management.

Purpose: To examine the accuracy of rapid influenza diagnostic tests (RIDTs) in adults and children with influenza-like illness and evaluate factors associated with higher accuracy.

Data Sources: PubMed and EMBASE through December 2011; BIOSIS and Web of Science through March 2010; and citations of articles, guidelines, reviews, and manufacturers.

Study Selection: Studies that compared RIDTs with a reference standard of either reverse transcriptase polymerase chain reaction (first choice) or viral culture.

Data Extraction: Reviewers abstracted study data by using a standardized form and assessed quality by using Quality Assessment of Diagnostic Accuracy Studies criteria.

Data Synthesis: 159 studies evaluated 26 RIDTs, and 35% were conducted during the H1N1 pandemic. Failure to report whether results were assessed in a blinded manner and the basis for patient recruitment were important quality concerns. The pooled sensitivity and specificity were 62.3% (95% CI, 57.9% to 66.6%) and 98.2% (CI, 97.5% to 98.7%), respectively. The positive and negative likelihood ratios were 34.5 (CI, 23.8 to 45.2) and 0.38 (CI, 0.34 to 0.43), respectively. Sensitivity estimates were highly heterogeneous, which was partially explained by lower sensitivity in adults (53.9% [CI, 47.9% to 59.8%]) than in children (66.6% [CI, 61.6% to 71.7%]) and a higher sensitivity for influenza A (64.6% [CI, 59.0% to 70.1%) than for influenza B (52.2% [CI, 45.0% to 59.3%).

Limitation: Incomplete reporting limited the ability to assess the effect of important factors, such as specimen type and duration of influenza symptoms, on diagnostic accuracy.

Conclusion: Influenza can be ruled in but not ruled out through the use of RIDTs. Sensitivity varies across populations, but it is higher in children than in adults and for influenza A than for influenza B.

Primary Funding Source: Canadian Institutes of Health Research.

Editors' Notes
Context

  • Rapid influenza diagnostic tests (RIDTs) are immunochromatographic assays that detect influenza viral antigens.

Contribution

  • This systematic review of 159 studies involving 26 RIDTs found that RIDTs have a high specificity and positive likelihood ratio and modest and highly variable sensitivity for detecting influenza.

Caution

  • Studies that assessed the effect of ordering RIDTs on clinical outcomes were not reviewed.

Implication

  • Positive RIDT results rule in but negative results do not rule out influenza. Whether routine use of these tests is warranted is unclear.

—The Editors


Worldwide, 3 to 5 million individuals develop severe influenza each year and 250 000 to 500 000 die of influenza-related causes (1). Even in developed countries, such as the United States, influenza is responsible for more than 200 000 hospitalizations annually and 3000 to 49 000 deaths (23). Moreover, as illustrated by the 2009 H1N1 pandemic that affected 214 countries (4), influenza has the potential to rapidly spread globally.

Early identification of influenza is important for optimal patient management and infection control. However, the case definition of influenza-like illness, defined by the Centers for Disease Control and Prevention and the World Health Organization as fever (temperature >37.8 °C) and cough or sore throat (56), has modest sensitivity (64% to 65%) and specificity (67%) (78). For this reason, physicians sometimes use tests to diagnose influenza.

Viral culture was the time-honored gold standard for influenza diagnosis. However, 3- to 10-day turnaround times for results reduce its utility for patient management, although shell vial culture can produce results in 48 hours with similar accuracy (910). More recently, reverse transcriptase polymerase chain reaction (RT-PCR) has replaced viral culture as the gold standard. It is considered the most sensitive and specific test for influenza, with a 2% to 13% higher detection rate than culture and results that can be obtained within hours (11). It is also the most expensive and least widely available test because of the specialized equipment and expertise required, and results may be delayed because samples are usually run in batches (910, 12).

Rapid influenza diagnostic tests (RIDTs) attempt to overcome some of these problems. They are simple to use; give results in 15 to 30 minutes; and, in some cases, can be used at the point of care in a routine clinical setting, such as a physician's office or an emergency department. These tests are usually immunochromatographic assays that detect specific influenza viral antigens in respiratory specimens (11). Their costs (approximately $15 to $20 per test for kit and reagents [13]) are similar to those of laboratory-based influenza tests, such as RT-PCR.

Unfortunately, RIDTs may have inconsistent accuracy, with reported sensitivity ranging from 10% to 80% (1012, 14), whereas specificity usually exceeds 90%. Even so, the Infectious Diseases Society of America, the Centers for Disease Control and Prevention, and the World Health Organization still consider them part of their guidelines, recognizing their usefulness in patient and outbreak management—especially when other tests, such as RT-PCR or immunofluorescence, are not readily available—while cautioning against potential misdiagnosis associated with their use (1011, 14). In light of these recommendations and the availability of many RIDTs approved for point-of-care use, it is important for health care providers to better understand the accuracy of these tests. Previous systematic reviews have been limited to pediatric studies (15) or have addressed only 1 commercial RIDT (8) and were conducted before the emergence of the influenza A(H1N1) 2009 strain (8, 15).

We developed and followed a protocol based on standard guidelines for the systematic review of diagnostic studies (1617) and used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (18) as the template for reporting the review.

Data Sources and Searches

We searched 4 electronic databases: PubMed (January 1950 to December 2011), EMBASE (January 1980 to December 2011), BIOSIS (January 1969 to March 2010), and Web of Science (January 1980 to March 2010). The databases were searched in March 2010, and an updated search of PubMed and EMBASE was conducted in December 2011. Bibliographies of included studies, recent narrative reviews on RIDTs, and guidelines on influenza were hand-searched for additional relevant studies. Diagnostic manufacturers were also contacted to get additional or unpublished studies.

The search strategy was designed with the help of a medical librarian and contained search terms for the influenza disease or virus combined with search terms for rapid diagnostic immunoassays, including brand names for the most common commercial RIDTs. Search terms for influenza included: “Influenza, Human” [MeSh] OR “Influenza A virus” [MeSh] OR “Influenza B virus” [MeSh] OR “influenza” OR “flu” OR “grippe.” Search terms for the tests included: “rapid test*” OR “rapid diagnos*” OR “rapid diagnostic test*” OR “point-of-care test*” OR “antigen detection test*” OR “antigen detection” OR “rapid antigen test*” OR “immunoassay*” OR “immunochromatographic test*” OR “Binax NOW” OR “Directigen Flu” OR “Flu OIA” OR “QuickVue Influenza” OR “Rapid Detection Flu” OR “SAS Influenza” OR “ TRU FLU” OR “ XPECT flu” OR “Zstat flu.” Studies published in either English or French were considered.

Study Selection

Studies were included if they assessed the accuracy of an RIDT against 1 of the 2 accepted reference standards. For this review, RIDTs were defined as any commercially available assay that identified influenza viral antigens or neuraminidase activity in respiratory specimens through simple immunochromatographic formats. In-house tests and precommercial versions were excluded. Acceptable reference standards included viral culture or RT-PCR. If both were available, data on RT-PCR were chosen because of the test's superior sensitivity and specificity.

Studies were excluded if they compared RIDTs with immunofluorescence or enzyme-linked immunosorbent assay (because those are not widely acknowledged reference standards for influenza diagnosis), if they used the result of the RIDTs as part of a composite reference standard (incorporation bias), or if they performed the reference standard only on samples with negative RIDT results (partial verification bias). We also excluded conference abstracts and case–control studies (testing with the RIDT of known positive or negative samples), which, by creating spectrum bias, can overestimate the accuracy of a test (19). If a selected publication included more than 1 RIDT, each test comparison was included as a separate “study.”

One reviewer screened titles and abstracts for relevance and examined full-text articles of those judged to be potentially eligible. When there was uncertainty about eligibility, a second reviewer was involved and consensus was reached.

Data Extraction and Quality Assessment

A data extraction form was piloted on a subset of included articles by 2 reviewers before being finalized. One reviewer extracted data from all of the articles. A second reviewer extracted data from a randomly chosen sample of 22 articles (approximately 20% of all included articles). The numbers in the extracted 2 × 2 tables matched exactly in 20 of the 22 articles, with minor differences for the other 2 articles.

Attempts were made to contact the authors if information was lacking to construct the main 2 × 2 table or for 1 of the prespecified subgroups (see Data Synthesis and Analysis section). Of the 25 authors contacted by e-mail, 13 provided new data or information.

For the reference standards, both traditional viral culture and shell vial culture were considered together, regardless of the cell line used or variation in techniques. Similarly, RT-PCR was considered as a whole, independent of the specific assay protocol used.

Children were defined as individuals younger than 18 years. The study population was considered to be mostly pediatric or mostly adults if 85% of individuals were below or above that cutoff, respectively. In mixed-study populations with separate results for children and adults, we used the cutoff used by the authors.

Point-of-care testing was defined as a test conducted at the patient's bedside (or in a clinic or office setting), immediately after specimen acquisition. When studies failed to mention when and where the RIDT was done, it was presumed not to have been done at the point of care. Methodological quality of the included studies was assessed by using Quality Assessment of Diagnostic Accuracy Studies criteria (20).

Data Synthesis and Analysis

Data were extracted to construct 2 × 2 tables, which were used to calculate sensitivity and specificity. Some articles (26 of 119) tested samples from the same patient with different commercial RIDTs. To avoid double counting of results from the same patient, we included only one 2 × 2 table from each article, unless results clearly came from different patients (for example, adults and children or persons infected with influenza A or B). The sensitivity and specificity estimates were pooled by using bivariate random-effects regression models, as recommended by the Cochrane Diagnostic Test Accuracy Working Group (16). The bivariate model takes into consideration the potential tradeoff between sensitivity and specificity by explicitly incorporating this negative correlation in the analysis (2122). The model was also used to draw hierarchical summary receiver-operating characteristic (HSROC) curves (23). The closer the curve is to the upper left-hand corner of the HSROC curve plot, the better the overall accuracy of the test. Positive and negative likelihood ratios were directly computed from pooled sensitivity and specificity estimates.

We expected substantial heterogeneity in test accuracy and used random-effects models that also allow for the addition of covariates to account for that heterogeneity. The following variables were selected a priori as potential sources of heterogeneity: population age (children vs. adults), virus type (influenza A vs. influenza B and subtypes of influenza A), reference standard used (viral culture or RT-PCR), commercial brand of RIDT, type of specimen, duration of symptoms before testing, point-of-care versus laboratory testing, and methodological quality (such as lack of blinding and clear definition of influenza-like illness). These variables were added to the bivariate model, provided that at least 5 studies were identified for each subgroup.

Summary sensitivity and specificity estimates for each covariate were generated, along with their 95% CIs. A P value below 0.050 for sensitivity or specificity was used to determine whether there was a statistically significant difference in sensitivity, specificity, or both among the levels of a particular covariate. Because the effects of some of these prespecified covariates may influence each other, multivarite meta-regression was also done to take into account the possible interrelations among the variables. All analyses were conducted by using PROC NLMIXED in SAS, version 9.2 (SAS Institute, Cary, North Carolina) (22).

Role of the Funding Source

This study was supported in part by the Canadian Institutes of Health Research. The funding source had no involvement in study design, conduct, analysis, or publication.

Study Selection

After the titles and abstracts were screened, 286 articles were eligible for full-text review. Of these, 119 were included (Appendix Figure) (24142). Because some articles evaluated more than 1 RIDT, the final analysis included 159 studies. A list of excluded studies with reasons for exclusions is available from the authors on request.

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Appendix Figure. Summary of evidence search and selection.

ILI = influenza-like illness; RIDT = rapid influenza diagnostic test.

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Characteristics of Included Studies

The Appendix Table describes the key characteristics and results of all 159 included studies, and Table 1 summarizes their main study-level characteristics. Most studies (52%) included both adults and children, although 34% and 14% included only children and adults, respectively. Only 33% of the studies defined the basis on which patients or specimens were recruited, and even fewer (13%) gave any information on duration of patients' clinical symptoms before testing. Approximately 35% of the included studies were conducted during the H1N1 2009 pandemic.

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Appendix Table. Study Characteristics

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Table 1. Characteristics of the 159 Included Studies

The included studies evaluated 26 commercial RIDTs. Of these, the most frequently studied tests were the Binax tests (BinaxNOW Flu A and Flu B [6 studies] and BinaxNOW Influenza A & B [22 studies]; Inverness Medical Innovations, Portland, Maine), the Directigen tests (Directigen Flu A [11 studies] and Directigen Flu A+B [30 studies]; Becton, Dickinson and Company, Franklin Lakes, New Jersey), and the QuickVue tests (QuickVue Influenza [18 studies] and QuickVue Influenza A+B [23 studies]; Quidel Corporation, San Diego, California). Both reference standards were used with almost equal frequency.

Quality of Included Studies

Figure 1 presents an overview of the quality of included studies. Because of our inclusion criteria, most studies were free of partial verification, differential verification, and incorporation bias and used an appropriate reference standard. However, only 33% of the included studies gave a clear rationale for patient or specimen inclusion (selection criteria), and only 41% reported blinding of the evaluation of the result of the RIDTs (mostly because they were evaluated at the point of care).

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Figure 1. Quality Assessment of Diagnostic Accuracy Studies assessments of the quality of included studies.

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Overall Accuracy of RIDTs

As shown in Figure 2, specificity seemed to be more consistent across studies than sensitivity, with sensitivity estimates ranging from 4.4% to 100% and specificity estimates ranging from 50.5% to 100%. Overall, for all RIDTs (119 studies) compared with 1 of the 2 acceptable reference standards, the pooled sensitivity from bivariate random-effects regression was 62.3% (95% CI, 57.9% to 66.6%) and the pooled specificity was 98.2% (CI, 97.5% to 98.7%). This corresponds to a positive likelihood ratio of 34.5 (CI, 23.8 to 45.2) and a negative likelihood ratio of 0.38 (CI, 0.34 to 0.43). Figure 2 shows the HSROC, which shows greater variation in sensitivity than in specificity, with only 17 studies (10.7%) reporting specificity estimates below 85%.

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Figure 2. Hierarchical summary receiver-operating characteristic curve plot of rapid influenza diagnostic test studies.

Individual studies (n = 159) are shown as open circles whose size is proportionate to the size of the study. Summary point is shown as a closed circle, representing sensitivity estimates pooled by using bivariate random-effects regression model. The hierarchical summary receiver-operating characteristic curve is shown as a dashed line and is truncated outside the area for which data exist.

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Investigation of Heterogeneity

Subgroup analyses were conducted to investigate heterogeneity in sensitivity, and to a lesser degree, in specificity (Table 2). Rapid influenza diagnostic tests showed a higher pooled sensitivity in children (66.6% [CI, 61.6% to 71.7%]) than in adults (53.9% [CI, 47.9% to 59.8%]) that was statistically significant (P < 0.001), whereas specificities in the 2 groups were similar. The difference in pooled sensitivity between children and adults remained statistically significant when adjusted for brand of RIDT, specimen type, or reference standard (results not shown).

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Table 2. Accuracy Estimates From Subgroup Analyses

Virus type also had an effect on the accuracy of RIDTs. Rapid influenza diagnostic tests had increased sensitivity for detecting influenza A (64.6% [CI, 59.0% to 70.1%]) compared with influenza B (52.2% [CI, 45.0% to 59.3%]; P = 0.050). They did not perform markedly worse in studies during the recent outbreak of pandemic influenza A(H1N1) 2009: There was no statistically significant difference in sensitivity estimates from studies conducted during the pandemic and those conducted before it (P = 0.065). The difference, which was not statistically significant, disappeared when adjusted for the reference standard used (P = 0.54 and 0.46 for sensitivity and specificity, respectively; results not shown).

There was considerable overlap among the accuracy estimates for the RIDTs (Table 2). Directigen Flu A had the highest pooled sensitivity (76.7% [CI, 63.8% to 86.0%]), followed by QuickVue Influenza test, although the difference from the overall estimate was not statistically significant. However, BinaxNOW, Directigen Flu A+B, and QuickVue Influenza A+B had a lower sensitivity compared with the overall estimate (57.0%, 57.2%, and 48.8%, respectively). Specificity was consistent among most RIDTs.

Rapid influenza diagnostic tests performed better when assessed against viral culture rather than RT-PCR (pooled sensitivity, 72.3% [CI, 66.8% to 77.9%] for culture. 53.9% [CI, 48.2% to 59.6%] for RT-PCR; P < 0.001), because of the increased accuracy of the latter.

Neither the type of specimen collected from patients nor whether the RIDT was performed at the point of care had a noticeable effect on their accuracy. Also, the quality criteria investigated (patient selection, blinding, and handling of uninterpretable results) did not have a statistically significant effect on pooled accuracy estimates, with the exception of a higher sensitivity for the few studies for which the timing (during or outside the influenza season) was unclear. Industry-sponsored studies showed a higher sensitivity (73.3% [CI, 65.3% to 81.3%]) than studies not sponsored by industry (59.4% [CI, 54.6% to 64.2%]). Although this difference was statistically significant, sensitivity analysis revealed that the overall estimates did not change when sponsored studies were removed from the analyses, which was probably due to the small number of sponsored studies (n = 23). Only 7 studies gave comparative information on duration of symptoms before testing. As shown in Table 3, there was a tendency toward lower accuracy on the first day of symptoms, with highest sensitivity on days 2 and 3 and a rapid decline thereafter.

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Table 3. Studies That Provided Data on Effect of Duration of Symptoms on Test Accuracy

Overall, RIDTs have high specificity, with modest and highly variable sensitivity. For the clinician, this means that a positive test result is unlikely to be false positive. In the presence of a positive RIDT result in a patient with influenza-like illness, a clinician can confidently diagnose influenza and begin appropriate infection-control measures and antiviral therapy, if indicated, while forgoing unnecessary additional diagnostic testing and antibiotic prescription. However, a negative RIDT result has a reasonable likelihood of being false negative and should be confirmed by other laboratory diagnostic tests if the result is likely to affect patient management.

An important finding is that RIDTs perform better in children than in adults, with approximately 13% higher sensitivity in children. This is plausible because young children have higher viral loads and longer viral shedding than adults (12). After adjustment for other factors, such as reference standard used, brand of RIDT, and type of specimen, RIDTs still show increased accuracy in children compared with adults.

Rapid influenza diagnostic tests have a higher sensitivity for detecting influenza A than influenza B. Studies have shown that infection with influenza A(H3N2) (the most common circulating subtype of influenza A in North America in past decades) leads to more severe disease and higher annual rates of influenza-associated hospitalization and death than infection with influenza B. Conversely, influenza A(H1N1) has been shown to have the lowest severity index and the lowest morbidity and mortality (2, 143144). More severe disease usually means higher viral load and, thus, better sensitivity. During the H1N1 2009 pandemic, there were reports of even lower sensitivity of RIDTs for this new strain, compared with published accuracy estimates (145). However, we found no important difference in the accuracy of the RIDTs between studies conducted during the influenza A(H1N1) 2009 pandemic and those conducted before, with any small difference disappearing after adjustment for the reference standard used.

Overall, no single commercial brand of RIDT seemed to perform markedly better or worse than others, but this finding should be interpreted cautiously because head-to-head comparisons were not done in most studies. No difference in accuracy was found among the respiratory specimens, although these analyses were limited by the absence of stratification by specimen type in most studies and the inconsistent reporting of many other factors known to affect specimen quality, such as the type of swab and the operator. Although common practice guidelines have held nasopharyngeal specimens as the best specimen type (10, 12), followed by nasal specimens and throat swabs, other studies have not shown a difference among them (146148).

Point-of-care testing also showed no effect on the accuracy of RIDTs. Thus, in this analysis, administration of the RIDTs by personnel other than a trained laboratory technician does not seem to adversely influence the performance of these tests. This could be good news, because it is likely that they find their most useful application and have the most effect in the diagnostic work-up for influenza when they are used as first-line tests, outside of the laboratory setting. However, no study directly compared accuracy between RIDTs performed at the point of care versus in a laboratory setting or made a distinction between who collected and who processed the specimen.

The strengths of our systematic review are that we followed a standard protocol and used a comprehensive search strategy. By contacting several authors, we were able to gather information that was missing from the original publications. We used rigorous methods of data analysis, including bivariate random-effects regression models and HSROC curve analyses. We also added predefined covariates to the bivariate model to explain heterogeneity in accuracy estimates.

The evidence base for the review had several limitations. Over the years, RT-PCR has gradually replaced viral culture as the preferred reference standard for influenza diagnosis. Although we preferentially included results from RT-PCR when available, both are currently accepted reference standards, and choosing only RT-PCR would have biased our review to include only recent studies. Considerable heterogeneity was found in the pooled estimates, as expected. Despite our attempts to explain it through the regression model, substantial heterogeneity remained unexplained. Many factors, possibly contributing to this residual heterogeneity, could not be assessed because they were not reported in most studies. For example, duration of clinical symptoms before testing is likely to have an important effect on test performance (12). This information was mentioned in only 13% of the included studies. Many studies failed to stratify by specimen type. Also, some subgroups, such as children and adults, were by necessity broad and could encompass different age ranges. Finally, other variables, such as flu vaccination coverage of the population under study, inclusion or exclusion of persons with comorbid conditions, type of swab used, who collected the specimen, transport medium used, and time elapsed before specimen processing, were reported so infrequently that their effect was difficult to assess.

Studies also had methodological limitations. In particular, less than one half of the studies reported blinded assessment of the RIDTs. Although RIDTs give a dichotomous yes/no answer, faint lines seen during reading may be an important source of false-positive results (113). Unblinded assessment could lead to an overoptimistic estimate of the test performance, even though we did not find any difference in reported accuracy between studies that reported blinding versus no blinding.

Although we searched several sources and updated our searches, we may have missed some eligible studies. Further, we extracted data on studies only in English and French. We could not formally assess publication bias because there is no valid method to do so when dealing with diagnostic studies.

The most important advantage of RIDTs is their rapid turnaround time, providing clinicians with an answer within minutes. Although they undoubtedly have higher accuracy, RT-PCR and viral culture take hours or even days to give results, even discounting transportation time to the nearest laboratory. Thus, RIDTs fill a void at the point of care that no other test is likely to fill in the near future: as a first-line test to be confirmed (especially if negative) by more time-consuming, definitive testing. As long as clinicians understand the limitations of RIDTs, namely that a negative result is unreliable and should be confirmed by using culture or RT-PCR, RIDTs could enable clinicians to institute prompt infection-control measures, begin antiviral treatment in high-risk populations, and make informed decisions about further diagnostic investigations. Although additional studies that evaluate test accuracy of RIDTs are not likely to add new knowledge, studies that evaluate clinical effect of RIDTs on patient management are needed to confirm whether and when RIDTs may decrease use of ancillary tests and empirical antibiotic treatment and increase appropriate use of antiviral treatment (88, 109, 149154). Finally, cost-effectiveness studies are essential to see whether potential benefits offset the added costs of routine use of RIDTs.

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Church DL, Davies HD, Mitton C, Semeniuk H, Logue M, Maxwell C. et al.  Clinical and economic evaluation of rapid influenza a virus testing in nursing homes in calgary, Canada. Clin Infect Dis. 2002; 34.790-5
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Esposito S, Marchisio P, Morelli P, Crovari P, Principi N.  Effect of a rapid influenza diagnosis. Arch Dis Child. 2003; 88.525-6
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Sharma V, Dowd MD, Slaughter AJ, Simon SD.  Effect of rapid diagnosis of influenza virus type a on the emergency department management of febrile infants and toddlers. Arch Pediatr Adolesc Med. 2002; 156.41-3
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Figures

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Appendix Figure. Summary of evidence search and selection.

ILI = influenza-like illness; RIDT = rapid influenza diagnostic test.

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Figure 1. Quality Assessment of Diagnostic Accuracy Studies assessments of the quality of included studies.

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Figure 2. Hierarchical summary receiver-operating characteristic curve plot of rapid influenza diagnostic test studies.

Individual studies (n = 159) are shown as open circles whose size is proportionate to the size of the study. Summary point is shown as a closed circle, representing sensitivity estimates pooled by using bivariate random-effects regression model. The hierarchical summary receiver-operating characteristic curve is shown as a dashed line and is truncated outside the area for which data exist.

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Tables

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Appendix Table. Study Characteristics

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Table 1. Characteristics of the 159 Included Studies

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Table 2. Accuracy Estimates From Subgroup Analyses

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Table 3. Studies That Provided Data on Effect of Duration of Symptoms on Test Accuracy

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RIDT Performance: Health-Economic Implications
Posted on May 18, 2012
Jeffrey, Petrozzino, Principal, Mark J. Atkinson
Compara Biomedical
Conflict of Interest: None Declared

The study done by Chartrand et. at. (Ann Intern Med. 2012;156:500- 511) is a well-done and comprehensive metaanalysis of 26 rapid influenza diagnostic tests' (RIDT) performance in adults and children with influenza -like illness (ILI). Fundamentally, the pooled RIDT performance reported by this study (sensitivity 62.3%, 95% CI 57.9% to 66.6%; specificity 98.2%, CI 97.5% to 98.7%) is the same as that previously reported (sensitivity 72%, CI 62-81; specificity 96%, CI 93-97%) for one widely- used commercial RIDT prior to the H1N1 pandemic (1).

It is important to consider these findings in the context of the commonly used alternative of unaided clinical diagnosis for ILI, where the pooled sensitivity is essentially the same as that of the RIDT (65%, CI 55 -74%), but specificity is about 30% lower (67%, CI 57-76%). This consideration has important health-economic implications. For example, the 8-fold higher false positive rate with unaided clinical ILI diagnosis will predictably lead to more non-indicated antiviral therapy (and associated complications and costs), possible promotion of antiviral drug resistance, complications of undiagnosed or untreated bacterial illness, or undetected illnesses other than influenza. In addition to these clinical implications, the estimated average cost to prevent one false positive result by using RIDTs in place of unaided ILI diagnosis would be -$30.02 to -$11.02 (i.e., $11.02 to $30.02 savings), $8.29 to $41.54, and $123.22 to $199.22, respectively during the peri-influenza, influenza, and epidemic influenza seasons (1).

References

1. Petrozzino JJ, Smith C, Atkinson MJ. Rapid diagnostic testing for seasonal influenza: an evidence-based review and comparison with unaided clinical diagnosis. J Emerg Med. 2010;39:476-490.e1. [PMID: 20227846]

Conflict of Interest:

Jeffrey Petrozzino and Mark J. Atkinson were employees or consultants of The Aequitas Group, Inc. between September 2007 and September 2008.

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