Clostridium difficile Infection Strategies for Hospital ...

Lee F. Schroeder, Elizabeth Robilotti, Lance R. Peterson, Niaz Banaei and David W. Dowdy J. Clin. Microbiol. 2014, 52(2):489. DOI: 10.1128/JCM.02777-13. Published Ahead of Print 27 November 2013.

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Economic Evaluation of Laboratory Testing Strategies for Hospital-Associated Clostridium difficile Infection

Economic Evaluation of Laboratory Testing Strategies for Hospital-Associated Clostridium difficile Infection Department of Pathology, Stanford University School of Medicine, Stanford, California, USAa; Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, California, USAb; Department of Medicine, NorthShore University HealthSystem, University of Chicago, Evanston, Illinois, USAc; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USAd

Clostridium difficile infection (CDI) is the most common cause of infectious diarrhea in health care settings, and for patients presumed to have CDI, their isolation while awaiting laboratory results is costly. Newer rapid tests for CDI may reduce this burden, but the economic consequences of different testing algorithms remain unexplored. We used decision analysis from the hospital perspective to compare multiple CDI testing algorithms for adult inpatients with suspected CDI, assuming patient management according to laboratory results. CDI testing strategies included combinations of on-demand PCR (odPCR), batch PCR, lateral-flow diagnostics, plate-reader enzyme immunoassay, and direct tissue culture cytotoxicity. In the reference scenario, algorithms incorporating rapid testing were cost-effective relative to nonrapid algorithms. For every 10,000 symptomatic adults, relative to a strategy of treating nobody, lateral-flow glutamate dehydrogenase (GDH)/odPCR generated 831 true-positive results and cost $1,600 per additional true-positive case treated. Stand-alone odPCR was more effective and more expensive, identifying 174 additional true-positive cases at $6,900 per additional case treated. All other testing strategies were dominated by (i.e., more costly and less effective than) stand-alone odPCR or odPCR preceded by lateral-flow screening. A cost-benefit analysis (including estimated costs of missed cases) favored stand-alone odPCR in most settings but favored odPCR preceded by lateral-flow testing if a missed CDI case resulted in less than $5,000 of extended hospital stay costs and 93%, or if the symptomatic carrier proportion among the toxigenic culture-positive cases was >80%. These results can aid guideline developers and laboratory directors who are considering rapid testing algorithms for diagnosing CDI.

C

lostridium difficile infection (CDI) accounts for 15 to 25% of all antibiotic-associated diarrhea and is the most common cause of infectious diarrhea in health care settings (1, 2). In 2008, the incidence of CDI in hospitalized patients was estimated by the Association for Professionals in Infection Control and Epidemiology to be 13 per 1,000 patients (3), and in recent decades, the incidence of severe CDI has steadily risen in North America and Europe (4). On the individual patient level, health care-associated CDI significantly increases the costs and lengths of hospital stays (5, 6). The health care costs attributable to hospital-acquired CDI have been estimated at $9,000 to $13,000 per case (7, 8) and $500 million to $1.5 billion per year nationally (7–9). Usually, inpatients with suspected CDI are placed under contact precautions to prevent the transmission of disease, often for 1 to 2 days while awaiting the results of CDI testing. Such time-intensive tests include direct tissue culture cytotoxicity, plate reader enzyme immunoassay (EIA) for C. difficile toxin A/B and/or glutamate dehydrogenase (GDH), and batch PCR for genes in the pathogenicity locus (10). In recent years, however, two rapid diagnostic tests have been introduced: ondemand PCR (odPCR) (GeneXpert; Cepheid, Inc., Sunnyvale, CA) (11) and lateral-flow testing for both GDH and C. difficile toxin A/B (TechLab, Blacksburg, VA) (12). These tests provide results within 2 h, making presumptive contact precautions unnecessary if testing can be performed in real time. Because of the limited data available at the time of publication, the current guidelines for CDI management do not endorse odPCR or lateral-flow diagnostics (2). However, half of all laboratories participating in the 2012 College of American Pathologists (CAP) proficiency testing survey employed one or both rapid tests for CDI testing (CAP, Northfield, IL), and these methods have been embraced in the

February 2014 Volume 52 Number 2

literature (13). While several studies have estimated the economic impact of CDI, most of these have been retrospective or prospective cohort studies (9, 14–18), with the few cost models limited either to the laboratory (19–22) or without consideration of the choice of the testing algorithm (7, 8). To assess the economic implications of this change in laboratory practice and to inform future guideline development, we conducted a comparative economic evaluation of the testing strategies for CDI that include both traditional and rapid diagnostics. (This study was presented in part at the Academy of Clinical Laboratory Physicians and Scientists meeting, Atlanta, GA, 7 June 2013.) MATERIALS AND METHODS Model structure. We used decision analysis to compare eight algorithms of CDI testing in a hypothetical cohort of 10,000 adult inpatients suspected of having CDI. The algorithms included three strategies based on traditional technologies (batch PCR, EIA toxin A/B, and direct tissue culture cytotoxicity), three based on rapid diagnostics, and two nondiagnostic strategies for purposes of comparison (treat none and treat all). The

Received 9 October 2013 Returned for modification 7 November 2013 Accepted 22 November 2013 Published ahead of print 27 November 2013 Editor: R. Patel Address correspondence to Lee F. Schroeder, [email protected]. Supplemental material for this article may be found at http://dx.doi.org/10.1128 /JCM.02777-13. Copyright © 2014, American Society for Microbiology. All Rights Reserved. doi:10.1128/JCM.02777-13

Journal of Clinical Microbiology

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Lee F. Schroeder,a Elizabeth Robilotti,b Lance R. Peterson,c Niaz Banaei,a David W. Dowdyd

Schroeder et al.

TABLE 1 Model inputsa Inputb c

CDI prevalence

Isolation Isolation required for positive test (days) Room Private room for isolation (additional cost per day) ($) Preemptive isolation before result (days) for: Batch PCR Direct tissue culture cytotoxicity EIA toxin Materials ($) Isolation materials per isolation Isolation materials per day Labor ($) Wage Housekeeping (per h) Nursing (per h) Patient access manager (per h) Labor per isolation event (h) Patient access manager Housekeeping Labor per isolation day (h) Nursing

490 jcm.asm.org

Inputb

Base (low–high)

0.10 (0.09–0.11)

Missed cases Additional stay (days, without isolation) Hospital room cost (no isolation) ($/day) Transmission, no isolation (new cases/day) Transmission, isolation (new cases/day)

7 (5–9) 1,288 (883–1,804) 0.45 (0.19–0.62) 0.04 (0.02–0.08)

Treatment Cost per day ($) Duration of treatment (days)

23 (4–43) 12 (10–14)

0.99 (0.97–1.00) 0.83 (0.74–0.89) 0.87 (0.82–0.90) 0.48 (0.43–0.54) 0.65 (0.57–0.71) 0.91 (0.90–0.92) 0.90 (0.88–0.92) 0.94 (0.93–0.95) 0.97 (0.96–0.97) 0.99 (0.98–0.99) 0.66 (0.56–0.75) 0.92 (0.90–0.94) 0.75 (0.64–0.85) 0.97 (0.96–0.98) 43 (37–49) 3.8 (2.8–4.7) 20.0 (14.9–24.8) 1.3 (0.9–1.6) 5.6 (4.2–7.0) 5.6 (4.2–7.0) 5.0 (3.8–6.3) 15.0 (11.3–18.8) 5 (5–6) 15 (14–17) 10 (9–11) 12 (11–14) 12 (11–14) 38 (34–42) 47 (42–52) 55 (49–60)

8.5 (7.0–10.0) 25 (0–138)

1 (0.5–1.5) 2 (1.5–2.5) 0.5 (0.25–0.75) 53 (39–66) 29 (21–36)

18 (15–22) 47 (38–56) 47 (38–56) 0.5 (0.4–0.6) 0.5 (0.4–0.6) 0.5 (0.4–0.6)

a Diagnostic accuracy measures are relative to a gold standard toxigenic culture performed on samples from symptomatic patients. The instrument costs for odPCR are assumed to be $65,045 discounted over 5 years with the number of tests per year dependent on the algorithm used (odPCR, 10,000; lateral-flow GDH/odPCR, 1,780; lateral-flow GDH-Tox/odPCR, 1,060). The service costs for odPCR are assumed to be $6,340 annually. These odPCR capital costs were included in the odPCR reagent costs. b CDI, C. difficile infection; odPCR, on-demand PCR; GDH, glutamate dehydrogenase; EIA, enzyme immunoassay; Tox or toxin, Clostridium difficile toxin A and/or B; Sn, diagnostic sensitivity; Sp, diagnostic specificity. c CDI (C. difficile infection) prevalence refers to the positivity rate among samples submitted to the laboratory, i.e., among patients suspected of having CDI. d The concordance rate is between the lateral-flow GDH and GDH-Tox results for patients with and without CDI. The sensitivity of concordant results refers to the fraction of all concordant results that are GDH-positive and Tox-positive when considering toxigenic culture-positive samples. The specificity of the concordant results refers to the fraction of all concordant results that are GDH-negative and Tox-negative when considering toxigenic culture-negative samples.

three rapid algorithms were: stand-alone odPCR (1), lateral-flow GDH testing with positive results confirmed by odPCR (lateral-flow GDH/ odPCR) (2), and lateral-flow testing of both GDH and C. difficile toxin A/B with concordant positives treated, concordant negatives not treated, and discordant results confirmed by odPCR (lateral-flow GDH-Tox/ odPCR) (3) (see Fig. S1 in the supplemental material). For the purposes of our model, we assumed that patients were treated and isolated in accordance with testing results. We adopted the hospital/health care perspective to assess cost and effectiveness (e.g., laboratory testing, isolation protocol, treatment, prolonged hospitalization, and the transmission of disease) but excluded patient costs (e.g., lost work from longer hospitalization). For traditional algorithms, in which the test results were available after a delay of 4 h, we assumed that patients would be placed in contact isolation and initiated on CDI treatment while awaiting CDI test results. For the rapid testing algorithms, we assumed there was no presumptive isolation protocol or treatment. Estimation of model inputs. Where possible, the data were drawn from either the published literature and publicly available online sources or from direct observation at Stanford University Medical Center (Table 1). In the interest of maximizing the generalizability of the data to typical hospital scenarios, preference was given to data obtained from large cohort studies rather than to estimates from outbreak investigations. The laboratory testing costs included those of the reagents and labor. We assumed that the use of rapid diagnostics required purchasing capital equipment (i.e., algorithms involving odPCR), whereas equipment purchases for traditional diagnostics would be made even in the absence of CDI testing; thus, we included the former but excluded the latter to provide a high estimate of the cost of rapid diagnostics relative to traditional diagnostics. Cost estimates drawn from the literature (20, 21) were confirmed using internal estimates for labor, reagents, and turnaround times established in assay validation studies. The diagnostic accuracy measures for individual tests were taken from an internal meta-analysis of published studies. We defined CDI as a positive toxigenic C. difficile culture result for a sample from a symptomatic patient who was selected by a physician for CDI testing. For example, the diagnostic sensitivity of a test in our model was calculated as the fraction of these toxigenic culture-positive samples



Laboratory Diagnostic sensitivity for: odPCR Lateral-flow GDH Batch PCR EIA toxin Direct tissue culture cytotoxicity Diagnostic specificity for: odPCR Lateral-flow GDH Batch PCR EIA toxin Direct tissue culture cytotoxicity Lateral-flow GDH-Tox ratesd Concordance rate (CDI) Concordance rate (no CDI) Sn of concordant results (CDI) Sp of concordant results (no CDI) Labor Laboratory technician ($ per h) Laboratory labor (min per test) for: Batch PCR Direct tissue culture cytotoxicity EIA toxin Lateral-flow GDH Lateral-flow GDH-Tox odPCR Clinician contact per positive result (min) Equipment and consumable costs ($) for: Batch PCR Direct tissue culture cytotoxicity EIA toxin Lateral-flow GDH Lateral-flow GDH-Tox odPCR (includes capital costs and service) odPCR (in odPCR) odPCR (in lateral-flow GDH/odPCR) odPCR (in lateral-flow GDH-Tox/odPCR)

TABLE 1 (Continued) Base (low–high)

Economic Evaluation of C. difﬁcile Testing Strategies


rate to 70% (Annual Survey Database, American Hospital Association, 2009). The duration of an extended hospital stay for a missed case was estimated to be 7 days, ranging from 5 to 9 days, based on our hospital policy of retesting no more frequently than every 7 days due to the lack of efficacy of short-interval repeat testing (35, 36). We assumed that all missed cases and subsequently transmitted secondary cases would eventually undergo correct diagnosis and treatment at the same costs as for the initial true-positive cases in our model. Analytic methods. We included incremental costs only, that is, we considered only costs that would be affected by the choice of testing algorithm. Discounting of costs was not necessary, as all costs and effects were limited to a time span of weeks. The only exception was for capital equipment, for which we used a 3% annual discount rate over the useful life of each item. All costs were inflated to 2012 U.S. dollars using the consumer price index. Given the uncertainty of the costs attributable to missed cases of CDI, we conducted two separate analyses. In the first (cost-effectiveness analysis), we took as the primary outcome the cost per true-positive case treated. In this analysis, the costs of missed cases (i.e., those other than initial laboratory testing, patient isolation, and treatment) were not explicitly incorporated. In the second (cost-benefit analysis), we included explicit estimates of the monetary costs of missed cases, including transmission (and costs of diagnosing and treating transmitted cases), extended hospital stays, and eventual diagnosis and treatment as described above. Sensitivity and uncertainty analysis. We conducted one-way sensitivity analyses for both the cost-effectiveness and cost-benefit models across all model inputs, as described in Table 1; the most influential parameters were selected for two-way sensitivity analyses in the cost-benefit model, including the cost of extended hospital stays versus the transmission rates for missed cases. To account for possible discrepancies between the test results and clinical outcomes, we conducted an additional two-way sensitivity analysis between the diagnostic sensitivity of lateral-flow GDH and the symptomatic carrier proportion. The symptomatic carrier proportion was defined as the fraction of toxigenic culture-positive diarrheal samples that came from individuals who carried C. difficile but for whom diarrhea was due to other causes. Although the use of contact precautions in these patients may prevent the transmission of C. difficile, treatment for CDI will not prevent the adverse effects of disease. As such, we assumed that treatment of symptomatic carriers averted no length-of-stay costs but that contact isolation did reduce the transmission rate. We also conducted a probabilistic uncertainty analysis in which all parameters were simultaneously varied over probability distributions (see Table S2 in the supplemental material, n ⫽ 10,000 simulations). We used this analysis to derive uncertainty ranges, defined as the 5th to 95th percentiles of the simulation results. All simulations were performed using TreeAge Pro 2013 (TreeAge Software, Inc., Williamstown, MA) and R version 2.15.3 (R Foundation for Statistical Computing, Vienna, Austria).

RESULTS

Meta-analysis of CDI testing strategies. Five studies passed the inclusion criteria, and each typically involved multiple testing methodologies (11, 23–26). The total number of patient samples tested across all studies was 4,582, with no testing methodology assessed using ⬍1,150 patient samples. The accuracy estimates from these studies are shown in Table 1. The details of the metaanalysis are shown in Fig. S2 and S3 and Tables S3 and S4 in the supplemental material. Cost-effectiveness: cost per true case diagnosed and treated. According to the five included studies, we estimated that among 10,000 symptomatic individuals, 1,017 would have CDI as determined by toxigenic culture and 8,983 would not. As shown in Table 2, the number of missed CDI cases was minimized by standalone odPCR, whereas the number of false-positive diagnoses was minimized by lateral-flow GDH/odPCR. Relative to the treatnone strategy (no initial diagnostic testing or treatment con-

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that had a positive result using the test in question. Toxigenic culture typically involves aliquoting stool samples onto a medium that is selective for C. difficile with anaerobic incubation for several days. C. difficile isolates from the selective medium are then grown in broth, filtered, and the supernatant aliquoted onto fibroblast tissue culture, with and without neutralizing antitoxin/antibody for confirmation of the presence of toxigenic C. difficile. Thirty-eight studies were evaluated, with 5 meeting the inclusion criterion (23–27) of having consecutively collected diarrheal samples with uniform gold-standard toxigenic culture reference performed on all samples. We included only the studies that did not enrich stool samples before using the selective medium. The requirement of using consecutively collected samples ensured that the samples were representative of all samples submitted in clinical practice, and the requirement for a uniform gold standard ensured that the accuracy estimates across studies were comparable (28–32). The hospital costs for isolation and treatment were included in the model. The dollar estimates for isolation materials were derived directly from hospital records and included those for the initial isolation cart (less gowns and gloves) plus the daily gown and glove requirement. After a review of multiple state and federal databases, and due to the completeness of the data set, we selected publicly available data for the state of Arizona for estimates of isolation room costs. The median value of the differences in charges between private and semiprivate general medical/ surgical rooms published by the Arizona Department of Health was used as the base case, excluding hospitals that reported no differences in charge (Arizona Department of Health, Cost Reporting and Review [see http: //www.azdhs.gov/plan/crr/cr/hospitals.htm#CostComparison]). Since hospital charges often do not reflect actual costs, the median cost-tocharge ratio derived from the Healthcare Cost and Utilization Project (HCUP) data set for Arizona was then applied to this charge data (see http: //www.hcup-us.ahrq.gov/db/state/costtocharge.jsp). The upper range for the sensitivity analysis was calculated as the 90th percentile charge multiplied by the 90th percentile cost-to-charge ratio, and the lower range was set to zero to represent hospitals that offer only private rooms or have no cost differences between private and semiprivate rooms. Time estimates for nursing staff, patient access managers, and housekeeping staff per isolation episode and per day of isolation were derived from the staff interviews we conducted at the Stanford University Medical Center. The number of days of isolation per episode of diagnosed CDI (7 to 10 days) was based on our infection control protocol that isolation precautions remain in place until the completion of treatment, which is typically 7 days, and the patient being free of diarrhea for 48 h. The labor costs were derived from the median of the publicly available national estimates of salaries, including benefits, with a range of 10th to 90th percentiles (http://salary.com). Treatment costs were based on 2011 Stanford University Medical Center pharmacy data, weighting the costs of metronidazole and vancomycin according to relative utilization and averaging costs associated with direct purchase and compounding. Missed cases of CDI result in several costs, including those related to the transmission of disease, extended hospital stays, and eventual CDI diagnosis and treatment. The transmission of disease due to missed cases was estimated using a recently published model of CDI transmission (33). The parameters of that model were adjusted to fit our study scenario (see Table S1 in the supplemental material). We assumed that the transmission rates would be 10 times higher without isolation precautions than with isolation, as a similar value has been reported for methicillin-resistant Staphylococcus aureus transmission (34). The cost of extended hospital stays for missed cases was modeled as the additional number of hospital days for missed cases relative to those for correctly diagnosed CDI cases times the daily cost of a hospital room. Estimates for the daily room cost of nonisolation hospitalization were derived from the WHO-CHOICE (CHOosing Interventions that are Cost Effective; World Health Organization, 2007) unit cost model, adjusting the length of stay to 4.9 days (National Hospital Discharge Survey, Centers for Disease Control and Prevention [see http://www.cdc.gov/nchs/nhds.htm]) and the occupancy

Schroeder et al.

TABLE 2 Diagnostic accuracy of testing strategies among 10,000 hospitalized adults suspected of having CDIa TPc

FPd

FNe

TNf

Sng

Sph

PPVi

NPVj

Treat none EIA toxin Direct tissue culture cytotoxicity Lateral-flow GDH/ odPCR Lateral-flow GDH-Tox/odPCR Batch PCR Stand-alone odPCR Treat all

0 491 655

0 292 125

1,017 525 360

8,983 8,691 8,857

0.00 0.48 0.65

1.00 0.97 0.99

0.63 0.84

0.90 0.94 0.96

831

80

184

8,904

0.82

0.99

0.91

0.98

848

266

167

8,714

0.84

0.97

0.76

0.98

887 1,005 1,017

517 777 8,983

129 10 0

8,465 8,205 0

0.87 0.99 1.00

0.94 0.91 0.00

0.63 0.56 0.10

0.98 1.00

a Results are calculated as median value outputs of Monte Carlo simulations (see Table S2 in the supplemental material). CDI, Clostridium difficile infection. b EIA, enzyme immunoassay; GDH, glutamate dehydrogenase. c TP, true positive. d FP, false positive. e FN, false negative. f TN, true negative. g Sn, diagnostic sensitivity. h Sp, diagnostic specificity. i PPV, positive predictive value. j NPV, negative predictive value.

ducted), lateral-flow GDH/odPCR was estimated to cost $1,600 per true-positive case diagnosed and treated (Table 3). Standalone odPCR was both more effective and more expensive at $6,900 per additional true-positive case diagnosed and treated. Relative to stand-alone odPCR, treating all symptomatic patients (without laboratory testing) would cost ⬎$820,000 per additional true-positive case. All other strategies evaluated were less effective and more costly than either lateral-flow GDH/odPCR or standalone odPCR; however, the cost-effectiveness of lateral-flow GDH-Tox/odPCR and that of lateral-flow GDH/odPCR were nearly equivalent with overlapping uncertainty ranges, suggesting that the choice of whether or not to include lateral-flow Tox in the testing algorithm is best made on a site-specific basis. Given the dominance of two strategies, we performed a series of sensitivity analyses to evaluate the cost-effectiveness of stand-alone

TABLE 3 Cost-effectiveness measures of strategies for 10,000 hospitalized adults suspected of having CDIa

Testing strategyb

Cost (million $ [95% CI])c

Incr. cost (million $)d

TP treated (95% CI)e

Incr. TP treated

ICER ($ per additional TP)f

CER ($) vs treat noneg

Treat none Lateral-flow GDH/odPCR Lateral-flow GDH-Tox/odPCR EIA toxin Stand-alone odPCR Batch PCR Direct tissue culture cytotoxicity Treat all

0 1.3 (1.0–1.9) 1.6 (1.1–2.2) 2.4 (1.9–3.2) 2.6 (1.9–3.5) 3.6 (2.7–5.1) 4.4 (3.3–6.0) 11.9 (8.1–17.4)

NAh 1.3 Dominatedi Dominated 1.3 Dominated Dominated 9.3

0 831 (746–917) 848 (761–934) 491 (437–550) 1,005 (933–1,080) 887 (817–960) 655 (582–732) 1,017 (944–1,091)

NA 831 Dominated Dominated 174 Dominated Dominated 12

NA 1,600 Dominated Dominated 6,900 Dominated Dominated 820,000

NA 1,600 1,800 5,000 2,500 4,100 6,700 12,000

a Results calculated as median value outputs of Monte Carlo simulations (see Table S2 in the supplemental material). The effectiveness measure refers to the diagnosis and treatment of an additional true-positive case(s) of CDI. CDI, Clostridium difficile infection. b GDH, glutamate dehydrogenase. c Costs include those for initial laboratory testing, patient isolation, and treatment (excluding extra costs due to missed cases, which are considered in the cost-benefit analysis). d Incr., incremental. e TP, true positives. f ICER, incremental cost-effectiveness ratio. g CER, cost-effectiveness ratio. h NA, not applicable. i The term “dominated” means that there are other testing algorithms that will lead to equal numbers of or more true-positive cases treated and at a lower cost per case.

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Testing strategyb

odPCR relative to lateral-flow GDH/odPCR (see Fig. S4 in the supplemental material); the most influential parameters were the additional costs of a private (versus semiprivate) room for isolation and lateral-flow GDH diagnostic sensitivity (compared against toxigenic culture), assuming that the toxigenic culture results correlated with patient outcomes. Under no one-way variation did the cost per additional true-positive case diagnosed and treated by stand-alone odPCR fall below that of lateral-flow GDH/odPCR or rise above $11,000. Cost-benefit analysis: net cost of diagnosis and treatment. Correct initial diagnosis and treatment of CDI (and thus avoidance of missed cases) can be expected to result in cost savings, which were ignored in the cost-effectiveness analysis above. Using the assumptions described in Table S2 in the supplemental material, we evaluated the net cost of each diagnostic strategy as a function of the extra costs of a missed case (Fig. 1; see also Fig. S5 in the supplemental material for an alternative depiction). As determined by the cost-effectiveness analysis and as shown in Fig. 1, stand-alone odPCR was the preferred strategy in situations where the cost of a missed case was ⬎$6,900. We then used an analytical model to estimate these extra costs directly (i.e., transmission of disease, extended hospital stay, and eventual CDI diagnosis and treatment; see Table S2 in the supplemental material). This value was estimated to be $14,000 (median) per missed case, ranging from $8,000 (5th percentile) to $22,000 (95th percentile), all estimates for which stand-alone odPCR was the preferred strategy; however, uncertainty did not exclude equivalence with lateral-flow GDH/odPCR or lateral-flow GDH-Tox/odPCR in the lower ranges (Fig. 1). At our median estimate of $14,000, stand-alone odPCR was projected to result in a net savings compared to lateralflow GDH/odPCR or lateral-flow GDH-Tox/odPCR of $120 per patient tested, or $1.2 million for a cohort of 10,000 symptomatic adult inpatients. Stand-alone odPCR remained the preferred strategy using all one-way sensitivity analyses (see Fig. S6 in the supplemental material). A graphical depiction of the costs by type in our cost-benefit model is provided in Fig. S7 in the supplemental material. We next evaluated the preferred diagnostic strategy as a function of extended hospital stay and transmission of CDI

1000 800 600 400 200 0 0

4000

8000 12000 16000 20000 24000 Extra costs of an additional missed case ($)

Stand-alone odPCR Lateral-flow GDH/odPCR Lateral-flow GDH-Tox/odPCR

28000

EIA toxin Batch PCR Direct tissue culture cytotoxicity Treat none

FIG 1 Net cost per patient tested by the extra cost of an additional missed case. The net cost was calculated as the cost of initial testing, treatment, and isolation, plus the extra cost of additional missed cases (consisting of transmission of disease, extended hospital stay, and eventual CDI diagnosis and treatment) for each missed case occurring in the given testing strategy. The colored lines represent different testing strategies (rapid algorithms are dashed), with shaded areas representing 5th to 95th percentiles from the corresponding Monte Carlo simulations. Strategies with the lowest cost per patient tested (i.e., lower along the graph at any given vertical “slice”) are preferred. The incremental cost-effectiveness of each nondominated strategy (relative to the next best strategy) from Table 3 can be seen on the x axis, where the minimal cost lines intersect ($1,600 and $6,900). Using our model, the most likely value for the extra cost of a missed case is represented by the black line (median, $13,848), with the uncertainty range shaded gray (5th and 95th percentiles). GDH, glutamate dehydrogenase; EIA, enzyme immunoassay; Tox, C. difficile toxin A/B; odPCR, on-demand PCR.

caused by a missed diagnosis (Fig. 2). If a missed CDI case caused ⬎5 secondary cases (baseline estimate, 3), stand-alone odPCR was preferred, regardless of the length of the extended stay of a missed case. Likewise, if the costs of an extended stay (or other morbidities not included in our model) for a missed case were ⬎$8,000 (baseline estimate, $9,000), stand-alone odPCR was preferred, regardless of the transmission rates. However, testing with lateral-flow diagnostics prior to odPCR was preferred if a missed CDI case resulted in ⬍$5,000 of extended hospital stay costs and ⬍2 transmissions. In settings where lateral-flow GDH/odPCR (or lateral-flow GDH-TOX/ odPCR) was preferred, the cost savings were typically modest (⬍$80 per patient tested) relative to those with stand-alone odPCR. In contrast, in settings where stand-alone odPCR was preferred to lateral-flow GDH/odPCR, the cost savings were more substantial (up to $210 per patient tested). Two parameters that could be varied to result in a strong preference for lateral-flow GDH/odPCR relative to stand-alone odPCR were lateral-flow GDH diagnostic sensitivity and the symptomatic carrier proportion (defined as the fraction of individuals with diarrhea and positive toxigenic culture who have diarrhea due to causes other than CDI; see Fig. S8 in the supplemental material). Specifically, our baseline assumption was that all individuals with symptomatic diarrhea and a positive toxigenic culture would benefit from CDI treatment. Under this assump-


tion, lateral-flow GDH required a diagnostic sensitivity of 93% for lateral-flow GDH/odPCR to achieve equivalence with stand-alone odPCR; however, as the symptomatic carrier proportion rose from zero to 0.80, this equivalence was achieved at the reference level for lateral-flow GDH diagnostic sensitivity (83%). In other words, if ⬎80% of individuals with diarrhea and a positive toxigenic culture had diarrhea due to causes other than CDI, lateralflow GDH/odPCR was preferred to stand-alone odPCR. DISCUSSION

This economic evaluation explores the economic implications of different laboratory testing algorithms related to hospital-associated CDI, with the finding that rapid testing is likely to be cost-saving and more effective relative to the traditional technologies that are recommended in current guidelines (2). Under most reasonable scenarios, stand-alone odPCR as a one-step test is the strategy that minimizes false-negative results and costs to the health care system. However, where costs of a missed CDI diagnosis are minimal, where lateralflow GDH/odPCR or lateral-flow GDH-Tox/odPCR can be performed with high diagnostic sensitivity, or where the symptomatic carrier proportion is high, testing with lateral-flow GDH or lateralflow GDH-Tox before odPCR is a justifiable option. The diagnostic accuracy measures for lateral-flow GDH and lateral-flow GDH-Tox were lower in our model than those of many published estimates (12, 19–21, 37–40). Several studies con-

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Net cost per-patient tested ($)

1200


150

100

50

Lateral-flow GDH/odPCR preferred (savings per-patient tested, $)

2000

4000

6000

8000

10000

12000

Stand-alone odPCR preferred (savings per-patient tested, $)

14000

200 70

60

50

40

30

20

0

10

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

Secondary cases transmitted per missed case

FIG 2 Two-way sensitivity analysis: missed-case extended stay ($) versus transmission. The color in each area of this diagram depicts the economically preferred CDI testing strategy according to the cost of an extended hospital stay for a missed case on the y axis and the number of secondary transmissions per missed case on the x axis; the intensity of color reflects the savings per patient tested over the next best option. Here, the savings for lateral-flow GDH/odPCR are calculated relative to stand-alone odPCR, as there is significant uncertainty overlap between the two lateral-flow strategies. Three strategies are preferred at different magnitudes of the extended hospital stay (or other morbidities not specifically modeled) and transmission resulting from a missed case: stand-alone odPCR (red), lateral-flow GDH/odPCR (blue), and treat-none (green). Where lateral-flow GDH/odPCR testing is preferred, the next best option (stand-alone odPCR) is up to $80 more costly per patient tested, but where stand-alone odPCR is preferred, the next best option is up to $210 more costly per patient tested. CDI, C. difficile infection; GDH, glutamate dehydrogenase; Tox, C. difficile toxin A/B; odPCR, on-demand PCR.

cluded these lateral-flow assays have diagnostic sensitivities and specificities near 100% when used in algorithms with nucleic acid amplification. Most of these studies, however, did not perform toxigenic gold-standard testing on all samples or did not use consecutively collected samples, and were thus excluded. An exception is the study by Swindells et al. (26), which was included in our accuracy calculation but had minimal impact due to a small sample size (n ⫽ 136). In support of our estimates of diagnostic accuracy, another small study (n ⫽ 114) that performed toxigenic cultures on all samples found the diagnostic sensitivity of the lateral-flow GDH-Tox/odPCR algorithm to be 61% (11). The lateral-flow GDH-Tox data from this study were not included in the model, as the results were only published for the entire lateralflow GDH-Tox/odPCR algorithm and not lateral-flow GDH-Tox separately. Nonetheless, in settings where the diagnostic sensitivity of lateral-flow GDH is felt to be ⬎90 to 95%, testing with this assay prior to odPCR is likely to be the most cost-effective strategy. Further evaluation of the accuracy of lateral-flow GDH and lateral-flow GDH-Tox with a toxigenic gold standard on consecutive samples will greatly aid in the decision-making process of whether to include these assays prior to odPCR. False-positive cases play an important role given the relatively low specificity of odPCR (estimated at 91%), which had a positive predictive value of only 56% in our model. The estimated costs for false positives are less uncertain than those associated with false negatives and consist primarily of isolation costs (see Fig. S7 in the supplemental material). Growing concerns about financial penalties and mandated reporting for

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certain health care-associated infections, including CDI, might reduce the preference for lower-specificity tests, such as standalone odPCR (41); however, this may be counterbalanced by a strong desire to use a sensitive strategy to prevent secondary cases, which would also be reported. Though our model was based on diagnostic accuracies calculated against an acceptable definition of CDI (i.e., positive toxigenic culture in symptomatic patients performed on samples sent for CDI testing), CDI defined in this way may not correlate well with clinical outcomes. Most published studies of CDI assays to date, however, have only considered diagnostic accuracy, while very few have measured patient outcomes (42, 43). Thus, we have used diagnostic accuracy as a surrogate for the ability of test results to translate into patient outcomes; to the extent that this translation does not occur in actual practice, our results may be biased in favor of tests with higher diagnostic sensitivities. The symptomatic carrier proportion included in our model is meant to partially address the concern that despite being the accepted gold standard (2, 44), toxigenic culture may identify carriers of C. difficile in patients with diarrhea that is due to other causes, and as such may not correlate well with clinical outcomes. To our knowledge, values for symptomatic carrier proportions have not been published, though C. difficile carriage among asymptomatic hospitalized patients has recently been estimated at 10% to 12% (45–47). The symptomatic carrier proportion will depend on test ordering practices. For instance, it should be lower if tested patients have been selected with the appropriate risk factors and possibly by severity of disease, thus increasing the overall



Extra cost of a missed case due to extended day ($)

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ACKNOWLEDGMENTS Niaz Banaei received honoraria for participating in Cepheid’s Medical Board of Directors for the tuberculosis TB/RIF product. Lance R. Peterson has done consulting work for Abbott, Cepheid, Merck, Pfizer, and Roche, has received honoraria from Abbott, Merck, Pfizer, and Roche, and has received research grants unrelated to this work from Cepheid, Care-


Fusion, Great Basin, Geneweave, Roche, AHRQ, and NIH. The other authors have no potential conflicts of interest or financial disclosures. Funding for the analysis software and Lee Schroeder’s contribution was provided by the Department of Pathology, Stanford University School of Medicine. Elizabeth Robilotti’s contributions were supported by NRSA grant no. T32: Epidemiology of Emerging Infections and Bioterrorism Training grant. David Dowdy’s contributions were partially supported by the B. Frank and Kathleen Polk Assistant Professorship in Epidemiology at the Johns Hopkins Bloomberg School of Public Health. We thank Cristina Lanzas, University of Tennessee, for her generous assistance in adapting her ecological model of CDI transmission to our economic evaluation.

REFERENCES 1. Johnson S, Gerding DN. 1998. Clostridium difficile-associated diarrhea. Clin. Infect. Dis. 26:1027–10341027-1036. http://dx.doi.org/10.1086 /520276. 2. Cohen SH, Gerding DN, Johnson S, Kelly CP, Loo VG, McDonald LC, Pepin J, Wilcox MH, Society for Healthcare Epidemiology of America, Infectious Diseases Society of America. 2010. Clinical practice guidelines for Clostridium difficile infection in adults: 2010 update by the Society for Healthcare Epidemiology of America (SHEA) and the Infectious Diseases Society of America (IDSA). Infect. Control Hosp. Epidemiol. 31:431– 455. http://dx.doi.org/10.1086/651706. 3. Jarvis WR, Schlosser J, Jarvis AA, Chinn RY. 2009. National point prevalence of Clostridium difficile in US health care facility inpatients, 2008. Am. J. Infect. Control 37:263–270. http://dx.doi.org/10.1016/j.ajic .2009.01.001. 4. Bassetti M, Villa G, Pecori D, Arzese A, Wilcox M. 2012. Epidemiology, diagnosis and treatment of Clostridium difficile infection. Expert Rev. Anti Infect. Ther. 10:1405–1423. http://dx.doi.org/10.1586/eri.12.135. 5. Ghantoji SS, Sail K, Lairson DR, DuPont HL, Garey KW. 2010. Economic healthcare costs of Clostridium difficile infection: a systematic review. J. Hosp. Infect. 74:309 –318. http://dx.doi.org/10.1016/j.jhin.2009.10.016. 6. Dubberke ER, Wertheimer AI. 2009. Review of current literature on the economic burden of Clostridium difficile infection. Infect. Control Hosp. Epidemiol. 30:57– 66. http://dx.doi.org/10.1086/592981. 7. McGlone SM, Bailey RR, Zimmer SM, Popovich MJ, Tian Y, Ufberg P, Muder RR, Lee BY. 2012. The economic burden of Clostridium difficile. Clin. Microbiol. Infect. 18:282–289. http://dx.doi.org/10.1111/j .1469-0691.2011.03571.x. 8. Zimlichman E, Henderson D, Tamir O, Franz C, Song P, Yamin CK, Keohane C, Denham CR, Bates DW. 2013. Health care-associated infections: a meta-analysis of costs and financial impact on the US health care system. JAMA Intern. Med. 173:2039 –2046. http://dx.doi .org/10.1001/jamainternmed.2013.9763. 9. Kyne L, Hamel MB, Polavaram R, Kelly CP. 2002. Health care costs and mortality associated with nosocomial diarrhea due to Clostridium difficile. Clin. Infect. Dis. 34:346 –353. http://dx.doi.org/10.1086/338260. 10. Peterson LR, Robicsek A. 2009. Does my patient have Clostridium difficile infection? Ann. Intern. Med. 151:176 –179. http://dx.doi.org/10.7326 /0003-4819-151-3-200908040-00005. 11. Tenover FC, Novak-Weekley S, Woods CW, Peterson LR, Davis T, Schreckenberger P, Fang FC, Dascal A, Gerding DN, Nomura JH, Goering RV, Akerlund T, Weissfeld AS, Baron EJ, Wong E, Marlowe EM, Whitmore J, Persing DH. 2010. Impact of strain type on detection of toxigenic Clostridium difficile: comparison of molecular diagnostic and enzyme immunoassay approaches. J. Clin. Microbiol. 48:3719 –3724. http://dx.doi.org/10.1128/JCM.00427-10. 12. Reyes RC, John MA, Ayotte DL, Covacich A, Milburn S, Hussain Z. 2007. Performance of TechLab C. DIFF QUIK CHEK and TechLab. C. DIFFICILE TOX A/B II for the detection of Clostridium difficile in stool samples. Diagn. Microbiol. Infect. Dis. 59:33–37. http://dx.doi.org/10 .1128/JCM.42.10.4863-4865.2004. 13. Burnham CA, Carroll KC. 2013. Diagnosis of Clostridium difficile infection: an ongoing conundrum for clinicians and for clinical laboratories. Clin. Microbiol. Rev. 26:604 – 630. http://dx.doi.org/10.1128/CMR.00016-13. 14. Dubberke ER, Reske KA, Olsen MA, McDonald LC, Fraser VJ. 2008. Short- and long-term attributable costs of Clostridium difficile-associated

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proportion of tests in the laboratory that have a positive result and decreasing the symptomatic carrier proportion in those positive results (31). If a substantial fraction of symptomatic individuals with a positive gold-standard test for CDI receive no benefit from treatment, algorithms that minimize the probability of treatment (e.g., treat-none and lateral-flow screening) become more attractive, though these same patients would continue to transmit toxigenic C. difficile if not isolated (45, 46, 48). Importantly, some testing strategies may be more sensitive for C. difficile that causes diarrhea than for C. difficile as a mere colonizer (42, 43). To the extent that this is true (and that, e.g., lateral-flow Tox or EIA toxin is more sensitive for true CDI than for carriage of toxigenic C. difficile), our model may relatively overestimate the cost of a missed case for such algorithms and thus underestimate their utility. Under such assumptions, lateral-flow Tox would be a sensitive test for patients with positive toxigenic culture results who are more likely to experience a poor outcome due to CDI, while odPCR would be a sensitive test for the population of potential toxigenic C. difficile excretors as a whole (4, 42). Though not specifically modeled in our study, in such a scenario, a plausible strategy would be to isolate patients based on odPCR and treat based on lateral-flow Tox. Future modeling studies are needed to explore the utility of such an algorithm. A limitation of this study is that we did not consider the impact that testing strategies would have on reducing the presence of CDI in the hospital over time. Also, our study focused on non-outbreak CDI management, as this setting is more common and a typical concern of infection prevention and control policies. Though not explicitly modeled here, it is likely that testing strategies that minimize missed diagnoses and opportunities for CDI transmission would also be preferred during outbreak settings. Furthermore, in our study, we did not consider the intensive care unit (ICU), pediatric populations, or long-term acute care populations, for which diagnostic accuracy and costs may differ. Finally, our model assumed that the treating physicians followed the test results. To the extent that treating physicians ignore test results, both the costs and effectiveness of the diagnostic strategies may converge, making the laboratory costs of the tests a more important differentiating concern. This hospital-wide econometric evaluation of CDI testing suggests that rapid testing for CDI is more effective and less costly than traditional technologies recommended by current guidelines (2). Specifically, in most settings, evaluation with stand-alone odPCR is likely to be preferred both economically and from the perspective of avoiding false-negative diagnoses. However, if the cost of a missed case is low, if lateral-flow diagnostics can be performed with high diagnostic sensitivity, or if the symptomatic carrier proportion is high, multistep algorithms involving lateral-flow testing before odPCR are justifiable alternatives. New guidelines for CDI testing in the health care setting should account for these economic and clinical realities and consider rapid diagnostic testing to be an appropriate strategy for diagnosing hospital-associated CDI.

Schroeder et al.

15. 16.

18.

19.

20.

21.

22.

23.

24.

25.

26. 27.

28. 29. 30.

31.

32.

496

jcm.asm.org

33. 34.

35. 36.

37.

38.

39.

40.

41.

42.

43.

44.

45.

46.

47.

48.

ports. Ann. Emerg. Med. 33:85–91. http://dx.doi.org/10.1016/S0196 -0644(99)70422-1. Lanzas C, Dubberke ER, Lu Z, Reske KA, Gröhn YT. 2011. Epidemiological model for Clostridium difficile transmission in healthcare settings. Infect. Control Hosp. Epidemiol. 32:553–561. http://dx.doi.org/10.1086/660013. Jernigan JA, Titus MG, Gröschel DH, Getchell-White S, Farr BM. 1996. Effectiveness of contact isolation during a hospital outbreak of methicillin-resistant Staphylococcus aureus. Am. J. Epidemiol. 143:496 –504. http: //dx.doi.org/10.1093/oxfordjournals.aje.a008770. Luo RF, Banaei N. 2010. Is repeat PCR needed for diagnosis of Clostridium difficile infection? J. Clin. Microbiol. 48:3738 –3741. http://dx.doi.org /10.1128/JCM.00722-10. Aichinger E, Schleck CD, Harmsen WS, Nyre LM, Patel R. 2008. Nonutility of repeat laboratory testing for detection of Clostridium difficile by use of PCR or enzyme immunoassay. J. Clin. Microbiol. 46:3795–3797. http://dx.doi.org/10.1128/JCM.00684-08. Bruins MJ, Verbeek E, Wallinga JA, Bruijnesteijn van Coppenraet LES, Kuijper EJ, Bloembergen P. 2012. Evaluation of three enzyme immunoassays and a loop-mediated isothermal amplification test for the laboratory diagnosis of Clostridium difficile infection. Eur. J. Clin. Microbiol. Infect. Dis. 31:3035–3039. http://dx.doi.org/10.1007/s10096-012-1658-y. Culbreath K, Ager E, Nemeyer RJ, Kerr A, Gilligan PH. 2012. Evolution of testing algorithms at a university hospital for detection of Clostridium difficile infections. J. Clin. Microbiol. 50:3073–3076. http://dx.doi.org/10 .1128/JCM.00992-12. Kawada M, Annaka M, Kato H, Shibasaki S, Hikosaka K, Mizuno H, Masuda Y, Inamatsu T. 2011. Evaluation of a simultaneous detection kit for the glutamate dehydrogenase antigen and toxin A/B in feces for diagnosis of Clostridium difficile infection. J. Infect. Chemother. 17:807– 811. http://dx.doi.org/10.1007/s10156-011-0267-5. Orellana-Miguel MA, Alcolea-Medina A, Barrado-Blanco L, RodriguezOtero J, Chaves-Sánchez F. 2013. Algorithm proposal based on the C. Diff Quik Chek Complete ICT device for detecting Clostridium difficile infection. Enferm. Infecc. Microbiol. Clin. 31:97–99. http://dx.doi.org/10 .1016/j.eimc.2012.01.003. Goldenberg SD, Price NM, Tucker D, Wade P, French GL. 2011. Mandatory reporting and improvements in diagnosing Clostridium difficile infection: an incompatible dichotomy? J. Infect. 62:363–370. http://dx .doi.org/10.1016/j.jinf.2011.03.007. Planche TD, Davies KA, Coen PG, Finney JM, Monahan IM, Morris KA, O’Connor L, Oakley SJ, Pope CF, Wren MW, Shetty NP, Crook DW, Wilcox MH. 2013. Differences in outcome according to Clostridium difficile testing method: a prospective multicentre diagnostic validation study of C difficile infection. Lancet Infect. Dis. 13:17–20. http://dx.doi.org /10.1016/S1473-3099(12)70301-8. Baker I, Leeming JP, Reynolds R, Ibrahim I, Darley E. 2013. Clinical relevance of a positive molecular test in the diagnosis of Clostridium difficile infection. J. Hosp. Infect. 84:311–315. http://dx.doi.org/10.1016/j.jhin .2013.05.006. Delmée M, Van Broeck J, Simon A, Janssens M, Avesani V. 2005. Laboratory diagnosis of Clostridium difficile-associated diarrhoea: a plea for culture. J. Med. Microbiol. 54:187–191. http://dx.doi.org/10.1099 /jmm.0.45844-0. Curry SR, Muto CA, Schlackman JL, Pasculle AW, Shutt KA, Marsh JW, Harrison LH. 2013. Use of multilocus variable number of tandem repeats analysis genotyping to determine the role of asymptomatic carriers in Clostridium difficile transmission. Clin. Infect. Dis. 57:1094 –1102. http: //dx.doi.org/10.1093/cid/cit475. Guerrero DM, Becker JC, Eckstein EC, Kundrapu S, Deshpande A, Sethi AK, Donskey CJ. 2013. Asymptomatic carriage of toxigenic Clostridium difficile by hospitalized patients. J. Hosp. Infect. 85:155–158. http: //dx.doi.org/10.1016/j.jhin.2013.07.002. Leekha S, Aronhalt KC, Sloan LM, Patel R, Orenstein R. 2013. Asymptomatic Clostridium difficile colonization in a tertiary care hospital: admission prevalence and risk factors. Am. J. Infect. Control 41:390 –393. http: //dx.doi.org/10.1016/j.ajic.2012.09.023. Walker AS, Eyre DW, Wyllie DH, Dingle KE, Harding RM, O’Connor L, Griffiths D, Vaughan A, Finney J, Wilcox MH, Crook DW, Peto TEA, Infections in Oxforshire Research Database. 2012. Characterisation of Clostridium difficile hospital ward-based transmission using extensive epidemiological data and molecular typing. PLoS Med. 9:e1001172. http: //dx.doi.org/10.1371/journal.pmed.1001172.



17.

disease in nonsurgical inpatients. Clin. Infect. Dis. 46:497–504. http://dx .doi.org/10.1086/526530. Kofsky P, Rosen L, Reed J, Tolmie M, Ufberg D. 1991. Clostridium difficile–a common and costly colitis. Dis. Colon Rectum 34:244 –248. http://dx.doi.org/10.1007/BF02090164. McFarland LV, Surawicz CM, Rubin M, Fekety R, Elmer GW, Greenberg RN. 1999. Recurrent Clostridium difficile disease: epidemiology and clinical characteristics. Infect. Control Hosp. Epidemiol. 20:43–50. http: //dx.doi.org/10.1086/501553. O’Brien JA, Lahue BJ, Caro JJ, Davidson DM. 2007. The emerging infectious challenge of Clostridium difficile-associated disease in Massachusetts hospitals: clinical and economic consequences. Infect. Control Hosp. Epidemiol. 28:1219 –1227. http://dx.doi.org/10.1086/522676. Teasley DG, Gerding DN, Olson MM, Peterson LR, Gebhard RL, Schwartz MJ, Lee JT, Jr. 1983. Prospective randomised trial of metronidazole versus vancomycin for Clostridium-difficile-associated diarrhoea and colitis. Lancet ii:1043–1046. Larson AM, Fung AM, Fang FC. 2010. Evaluation of tcdB real-time PCR in a three-step diagnostic algorithm for detection of toxigenic Clostridium difficile. J. Clin. Microbiol. 48:124 –130. http://dx.doi.org/10.1128/JCM .00734-09. Quinn CD, Sefers SE, Babiker W, He Y, Alcabasa R, Stratton CW, Carroll KC, Tang YW. 2010. C. Diff Quik Chek complete enzyme immunoassay provides a reliable first-line method for detection of Clostridium difficile in stool specimens. J. Clin. Microbiol. 48:603– 605. http://dx.doi .org/10.1128/JCM.01614-09. Sharp SE, Ruden LO, Pohl JC, Hatcher PA, Jayne LM, Ivie WM. 2010. Evaluation of the C. Diff Quik Chek complete assay, a new glutamate dehydrogenase and A/B toxin combination lateral flow assay for use in rapid, simple diagnosis of Clostridium difficile disease. J. Clin. Microbiol. 48:2082–2086. http://dx.doi.org/10.1128/JCM.00129-10. Vasoo S, Stevens J, Portillo L, Barza R, Schejbal D, Wu MM, Chancey C, Singh K. Cost-effectiveness of a modified two-step algorithm using a combined glutamate dehydrogenase/toxin enzyme immunoassay and real-time PCR for the diagnosis of Clostridium difficile infection. J. Microbiol. Immunol. Infect., in press. http://dx.doi.org/10.1016/j.jmii.2012.07.008. Peterson LR, Mehta MS, Patel PA, Hacek DM, Harazin M, Nagwekar PP, Thomson RB, Jr, Robicsek A. 2011. Laboratory testing for Clostridium difficile infection: light at the end of the tunnel. Am. J. Clin. Pathol. 136:372–380. http://dx.doi.org/10.1309/AJCPTP5XKRSNXVIL. Barbut F, Braun M, Burghoffer B, Lalande V, Eckert C. 2009. Rapid detection of toxigenic strains of Clostridium difficile in diarrheal stools by real-time PCR. J. Clin. Microbiol. 47:1276 –1277. http://dx.doi.org/10 .1128/JCM.00309-09. Barbut F, Monot M, Rousseau A, Cavelot S, Simon T, Burghoffer B, Lalande V, Tankovic J, Petit JC, Dupuy B, Eckert C. 2011. Rapid diagnosis of Clostridium difficile infection by multiplex real-time PCR. Eur. J. Clin. Microbiol. Infect. Dis. 30:1279 –1285. http://dx.doi.org/10 .1007/s10096-011-1224-z. Swindells J, Brenwald N, Reading N, Oppenheim B. 2010. Evaluation of diagnostic tests for Clostridium difficile infection. J. Clin. Microbiol. 48: 606 – 608. http://dx.doi.org/10.1128/JCM.01579-09. Tenover FC, Baron EJ, Peterson LR, Persing DH. 2011. Laboratory diagnosis of Clostridium difficile infection: can molecular amplification methods move us out of uncertainty? J. Mol. Diagn. 13:573–582. http://dx .doi.org/10.1016/j.jmoldx.2011.06.001. Rutjes AW, Reitsma JB, Vandenbroucke JP, Glas AS, Bossuyt PM. 2005. Case-control and two-gate designs in diagnostic accuracy studies. Clin. Chem. 51:1335–1341. http://dx.doi.org/10.1373/clinchem.2005.048595. Rutjes AW, Reitsma JB, Di Nisio M, Smidt N, van Rijn JC, Bossuyt PM. 2006. Evidence of bias and variation in diagnostic accuracy studies. CMAJ 174:469 – 476. http://dx.doi.org/10.1503/cmaj.050090. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Leeflang MM, Sterne JA, Bossuyt PM. 2011. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann. Intern. Med. 155: 529 –536. http://dx.doi.org/10.7326/0003-4819-155-8-201110180-00009. Dubberke ER, Han Z, Bobo L, Hink T, Lawrence B, Copper S, HoppeBauer J, Burnham C-AD, Dunne WM, Jr. 2011. Impact of clinical symptoms on interpretation of diagnostic assays for Clostridium difficile infections. J. Clin. Microbiol. 49:2887–2893. http://dx.doi.org/10.1128 /JCM.00891-11. Mower WR. 1999. Evaluating bias and variability in diagnostic test re-