ABSTRACT
Bacteremia can progress to septic shock and death without appropriate medical intervention. Increasing evidence supports the role of molecular diagnostic panels in reducing the clinical impact of these infections through rapid identification of the infecting organism and associated antimicrobial resistance genes. We report the results of a multicenter clinical study assessing the performance of the GenMark Dx ePlex investigational-use-only blood culture identification Gram-negative panel (BCID-GN), a rapid diagnostic assay for detection of bloodstream pathogens in positive blood culture (PBC) bottles. Prospective, retrospective, and contrived samples were tested. Results from the BCID-GN were compared to standard-of-care bacterial identification methods. Antimicrobial resistance genes (ARGs) were identified using PCR and sequence analysis. The final BCID-GN analysis included 2,444 PBC samples, of which 926 were clinical samples with negative Gram stain results. Of these, 109 samples had false-negative and/or -positive results, resulting in an overall sample accuracy of 88.2% (817/926). After discordant resolution, overall sample accuracy increased to 92.9% (860/926). Pre- and postdiscordant resolution sample accuracy excludes 37 Gram-negative organisms representing 20 uncommon genera, 10 Gram-positive organisms, and 1 Candida species present in 5% of samples that are not targeted by the BCID-GN. The overall weighted positive percent agreement (PPA), which averages the individual PPAs from the 27 targets (Gram-negative and ARG), was 94.9%. The limit of detection ranged from 104 to 107 CFU/ml, except for one strain of Fusobacterium necrophorum at 108 CFU/ml.
KEYWORDS: ePlex, GenMark, Gram-negative, bacteremia, rapid diagnostics, AST, antimicrobial testing
INTRODUCTION
Extrapolating from data obtained in high-income countries, Fleischmann et al. (1) estimated that the annual global burden of sepsis is about 35.1 million cases, with approximately 5.3 million deaths. The mortality rate in patients with severe sepsis and septic shock increases by 7.4% for each hour that appropriate antibiotic therapy is delayed (2). Circumstances like these necessitate immediate initiation of empirical broad-spectrum antibiotics for all patients with presumed infection (3) until the organism is definitively identified and its susceptibility is determined (4). The administration of broad-spectrum antibiotics can lead to complications, including toxicity, increased antibiotic resistance, and Clostridioides difficile toxin-related diseases. Thus, it is essential to determine the nature of the infecting organism(s) and corresponding antibiotic susceptibilities as soon as possible to allow the selection of the appropriate and targeted therapy.
Currently, there are several commercially available, FDA-cleared blood culture identification (BCID) systems that use molecular methods for rapid identification of pathogens and antibiotic resistance genes (ARGs) in positive blood culture (PBC) bottles. One such system offers a single panel to detect 43 Gram-positive (GP) and Gram-negative (GN) bacteria and yeasts as well as 10 antibiotic resistance markers (5–7). Two other platforms offer separate panels for the identification of GP and GN bacteria, but neither has a fungal panel (8, 9). While other commercial assays provide multitarget pathogen panels, few of the existing assays incorporate “Pan” targets into their panel design. Lack of a Pan target adds to reliance on accurate interpretation of the primary Gram stain result for selection of the correct test panel or could result in failure to recognize the presence of an off-panel organism or an unsuspected mixed infection. The BCID-GN is a comprehensive panel comprising 21 specific Gram-negative genus or species targets, including anaerobic bacteria and less common aerobic targets. In addition, BCID-GN detects 6 genetic markers of antibiotic resistance as well as Pan targets for select Gram-positive bacteria and Candida species to aid in detection of polymicrobial cultures.
The purpose of this study was to establish the analytical and clinical performance characteristics of the GenMark Dx ePlex investigational-use-only (IUO) blood culture identification Gram-negative panel (BCID-GN) to support the regulatory premarket submission. We report the results of a study in which six large clinical microbiology laboratories assessed the performance of BCID-GN on 2,444 prospective, retrospective, and contrived samples.
MATERIALS AND METHODS
Study population.
The study population included patients of all ages and genders, with 10% of samples from patients <18 years of age (see Table S1 in the supplemental material). PBCs were collected for standard patient care and diagnosis in 10 geographically diverse regions within the United States, including the following cities: Albuquerque, NM; Baltimore, MD; Charleston, SC; Danville, PA; Detroit, MI (two sites); Harvey, IL; Indianapolis, IN; Milwaukee, WI; and San Diego, CA. Samples were obtained from clinical and reference laboratories representing a variety of clinical settings, including outpatient clinics, hospitals, emergency departments, extended care facilities, and any other facilities where patients sought medical care.
Overall study design and conduct.
Clinical samples were cultured and tested as ordered per the standard-of-care procedures (SOC) at each site. The study was performed using two protocols, one for collection and a second for collection and testing; both were approved by a central Institutional Review Board (IRB) (Quorum, Seattle, WA) and/or the site-specific IRB.
Sample collection and storage.
Thirteen different blood culture bottle types (i.e., broth) from three manufacturers (Becton, Dickinson, bioMérieux, Inc., and Thermo Fisher Scientific) were used. Clinical sample inclusion criteria were defined as residual inoculated blood culture bottles, which were flagged as positive in an automated continuous monitoring blood culture system with a readable Gram stain result exhibiting Gram-negative organisms. Samples were tested when prospective (fresh or frozen), within the time frame specified by the manufacturer (10), or retrospective (frozen blood culture broth samples from trial sites). Contrived samples were prepared in BD Bactec blood culture bottles (Plus Aerobic/F, Plus Anaerobic/F, Lytic/10 Anaerobic/F, or Peds Plus/F) as previously described (11–14). For the prospective collection, exclusion criteria were defined as any fresh broth from positive blood culture bottle(s) from a patient that was already tested. (In the retrospective arm, two bottles from the same patient may have been tested if different types flagged positive [i.e., one anaerobic and one aerobic]). Bottles containing charcoal, and samples with insufficient volume to complete required testing were also excluded.
The GenMark Dx ePlex BCID-GN panel testing.
Samples were tested at one of six clinical sites with the BCID-GN (10). Frozen samples were labeled such that testing personnel were blinded to the sample origin (i.e., prospective, retrospective, and contrived) and the expected organism results (10). Each day of testing, negative and positive controls were tested using the BCID-GN. Controls were prepared by GenMark Dx, and testing rotated among different instrument bays with each day of testing to ensure all bays were targeted for equal testing during the study.
The BCID-GN device is a sample-to-answer cartridge that uses electrowetting and eSensor technology for the extraction, amplification, and competitive DNA hybridization (15, 16) for the identification of 21 bacterial targets, six ARGs associated with extended-spectrum beta-lactamases (ESBL) and carbapenemases, and two Pan targets direct from PBC bottles (Table 1). The BCID-GN ARG assays detect the following genetic determinants of resistance: CTX-M, KPC, IMP, VIM, NDM, and OXA (OXA-23 and OXA-48). Results for ARG targets are only reported when an associated organism known to carry the gene is detected by the BCID-GN (Table 2).
TABLE 1.
Target type | Target |
---|---|
Bacterial targets | Acinetobacter baumannii |
Bacteroides fragilis | |
Citrobacter spp. | |
Cronobacter sakazakii | |
Enterobacter cloacae complex | |
Enterobacter (non-cloacae complex) | |
Escherichia coli | |
Fusobacterium necrophorum | |
Fusobacterium nucleatum | |
Haemophilus influenzae | |
Klebsiella oxytoca | |
Klebsiella pneumoniae groupa | |
Morganella morganii | |
Neisseria meningitidis | |
Proteus spp. | |
Proteus mirabilis | |
Pseudomonas aeruginosa | |
Salmonella spp. | |
Serratia spp. | |
Serratia marcescens | |
Stenotrophomonas maltophilia | |
Antimicrobial resistance markers | CTX-M (blaCTX-M) |
IMP (blaIMP) | |
KPC (blaKPC) | |
NDM (blaNDM) | |
OXA (blaOXA)b | |
VIM (blaVIM) | |
Pan targets | Pan-Gram-positivec |
Pan-Candidad |
K. pneumoniae, K. quasipneumoniae, and K. variicola.
23 and 48 like.
Coverage of the Pan Gram-positive target includes the following: Bacillus cereus group (including B. cereus and B. thuringiensis), Bacillus subtilis group (including B. amyloliquefaciens, B. atrophaeus, B. licheniformis, and B. subtilis), Enterococcus (including E. avium, E. casseliflavus, E. cecorum, E. dispar, E. durans, E. faecalis, E. faecium, E. gallinarum, E. hirae, E. italicus, E. malodoratus, E. pseudoavium, E. raffinosus, E. saccharolyticus, and E. sanguinicola), Staphylococcus (including S. arlettae, S. aureus, S. auricularis, S. capitis, S. caprae, S. carnosus, S. chromogenes, S. cohnii, S. epidermidis, S. gallinarum, S. haemolyticus, S. hominis, S. hyicus, S. intermedius, S. lentus, S. lugdunensis, S. muscae, S. pasteurii, S. pettenkoferi, S. pseudintermedius, S. saccharolyticus, S. saprophyticus, S. schleiferi, S. sciuri, S. simulans, S. vitulinus, S. warneri, and S. xylosus), and/or Streptococcus (including S. agalactiae, S. anginosus, S. bovis, S. constellatus, S. cricetid, S. dysgalactiae, S. equi, S. equinus, S. gallolyticus, S. gordonii, S. infantarius, S. infantis, S. intermedius, S. mitis, S. oralis, S. parasanguinis, S. peroris, S. pneumoniae, S. pyogenes, S. salivarius, S. sanguinis, and S. thoraltensis).
Coverage of the Pan Candida target includes C. albicans, C. glabrata, C. krusei, and C. parapsilosis.
TABLE 2.
Organism(s) | Resistance markera |
|||||
---|---|---|---|---|---|---|
CTX-M | IMP | KPC | NDM | OXA | VIM | |
Acinetobacter baumannii, Citrobacter spp., Enterobacter cloacae complex, Enterobacter (non-cloacae complex), Escherichia coli, Klebsiella oxytoca, Klebsiella pneumoniae group, Morganella morganii, Proteus spp., Proteus mirabilis, Pseudomonas aeruginosa, Salmonella, Serratia spp., Serratia marcescens | X | X | X | X | X | X |
Bacteroides fragilis, Fusobacterium necrophorum, Fusobacterium nucleatum, Haemophilus influenzae, Neisseria meningitidis | ||||||
Cronobacter sakazakii | X | |||||
Stenotrophomonas maltophilia | X |
An “X” denotes that the genetic target is detected for that organism list.
Briefly, after inverting the blood culture bottle several times to mix, 50 μl was aspirated and loaded into the loading port of the BCID-GN cartridge, and the cap was depressed to close the port. Each cartridge was barcoded according to the manufacturer’s instructions, the barcode was scanned by the ePlex instrument, and the cartridge was inserted into an available bay. Upon completion, the ePlex instrument ejected the cartridge for disposal and a BCID-GN report was generated (10).
Reference and comparator methods.
The comparator method(s) for organism identification were the site’s SOC, including traditional culture, FDA-cleared matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI-TOF MS) (i.e., bioMérieux Vitek MS and Bruker Biotyper), and automated phenotypic identification and antibiotic susceptibility platforms (e.g., Becton, Dickinson [BD] Phoenix, bioMérieux Vitek 2, and Siemens MicroScan). The phenotypic methods listed above varied at different sites and served as the site’s reference identification standard. Due to issues with organism misidentification, samples with a member of the Acinetobacter calcoaceticus-baumannii complex (15) or Candida parapsilosis (16) identified by SOC were confirmed using analytically validated PCR amplification assays followed by bidirectional sequencing (PCR/sequencing) or 16S sequencing by Laboratory Corporation of America Holdings (LabCorp [Burlington, NC]) according to FDA clinical trial instructions outlined in the 510k summary (10). The comparator methods for ARGs were analytically validated by real-time PCR amplification assay(s) followed by bidirectional sequencing, developed and performed by LabCorp.
Discordant resolution method.
Results from collected samples that were discordant between the BCID-GN and the comparator method(s) (i.e., false negative, false positive) were tested with analytically validated PCR amplification assay(s) followed by bidirectional sequencing by LabCorp to determine the presence or absence of the organism.
Data and statistical methods.
For each target on the BCID-GN, investigational results were compared to the comparator method results. Organisms or ARGs detected by both investigational and comparator methods were classified as true positive, whereas those detected by neither were classified as true negative. Organisms or ARGs detected by comparator methods but not the BCID-GN were classified as false negative, whereas organisms or ARGs detected by the BCID-GN but not by comparator methods were classified as false positive.
Positive percent agreement (PPA) and negative percent agreement (NPA) with comparator method results were determined for each target on the BCID-GN, according to standard laboratory calculations (17). The two-sided 95% confidence intervals (CI) were calculated for PPA and NPA. For ARGs, performance was calculated including only samples with associated organisms detected by comparator methods.
RESULTS
Total samples.
A combination of 354 prospectively enrolled, 1,326 retrospectively selected (from frozen, banked PBC samples), and 780 contrived (isolates spiked into whole blood and blood culture bottles) samples were tested with the BCID-GN. Of the 354 prospective samples, 171 were collected and tested (fresh never frozen), while 183 were collected and frozen for later testing. One of these samples was withdrawn due to organism identification by unacceptable methods. Four additional samples lacking final, valid BCID-GN results were withdrawn and excluded from analysis (one prospective, three retrospective). Of the 2,460 total samples initially tested with the BCID-GN, 126 yielded invalid results for an initial validity rate of 94.9%. After repeat testing, 12 samples were excluded as nonevaluable, since they continued to produce invalid results; the final validity rate was 99.5%. Therefore, the final analysis included 926 clinical samples with Gram-negative Gram stain results (349 prospective, 577 retrospective) and 777 contrived samples evaluable for the organism-specific targets present on the BCID-GN. An additional 741 clinical samples without Gram-negative Gram stain results were evaluated for the Pan targets only.
Demographic/sample information.
Subject demographic information was linked to the evaluable prospectively and retrospectively collected samples with Gram-negative stain results (n = 349 and 577, respectively) and is provided in Table S1 in the supplemental material. Mean and median ages of the two groups were 59.2 and 62.0, respectively, for prospectively collected samples and 59.6 and 64.0 for retrospectively collected samples. Female gender was higher in the prospective samples (51.9%) but lower in the retrospective samples (46.8%).
Approximately 84% of clinical samples were tested fresh or frozen within 12 h of bottle positivity in ultralow temperature freezers for several months before testing with the investigational assay. Testing of the remaining clinical samples occurred at the following intervals: 7% within 12 to 24 h; 4% within 24 to 48 h; 4% within 48 to 96 h; <1% within 96 to 168 h; and <1% within 168 to 504 h (10 retrospective samples positive for a Candida species). All testing of contrived samples occurred within 2 h of the bottle flag; samples identified after the 2-h period were excluded. No difference in performance was observed due to storage conditions (10). The instructions for use (IFU) allow for room temperature storage up to 7 days, refrigerated storage up to 1 month, and storage at −80°C to −20°C for up to 18 months after bottle positivity (10).
Three commercially available blood culture systems were included in the study during a time when no relevant background DNA or recalls were reported from the manufacturers. Across all sites, BD Bactec blood culture bottles represented the majority (84% across all categories, 6% of which were pediatric bottles), with the remaining 16% split among BacT/Alert (9%) and TREK (7%). Of the total pediatric bottles, 39% were clinical samples and the remaining were contrived samples. The distribution of bottle types is shown in Table S2.
Distribution of standard laboratory procedures.
In some cases, comparator methods differed between the fresh and frozen samples tested due to changes in the site’s standard methods utilized at the time of original testing. The majority (79.6%) of prospective fresh samples grew organisms identified by FDA-cleared MALDI-TOF mass spectrometry (Vitek MS or Bruker). Prospective frozen samples contained organisms identified primarily by Vitek 2 (63.7%) or MicroScan (25.3%). Retrospective Gram-negative samples contained organisms identified by Phoenix (52.5%) or Vitek 2 (31.5%). Although some nonviable amplification of microbial DNA has been previously reported (18), during the time of the ePlex study, no background amplification signals were identified.
Assay performance.
There were 926 prospective and retrospective PBC samples with Gram stains that displayed Gram-negative organisms, including 842 samples with only Gram-negative bacilli. There were 67 samples containing mixtures of Gram-negative and Gram-positive bacteria, two containing mixtures of Gram-negative bacteria and yeast, four with mixed Gram-negative and Gram-positive bacteria and yeast, and 11 with Gram-variable organisms.
A total of 72 samples had one or more false-negative results, but the BCID-GN correctly identified the organisms and ARGs identified by comparator methods in 92.2% (854/926) of these samples. There were 109 samples with false-negative and/or -positive results, resulting in an overall sample accuracy where the BCID-GN detected all organisms/ARGs found by comparator methods and did not detect additional organisms/ARGs (i.e., false positive) of 88.2% (817/926). After discordant resolution of the false-negative and -positive results (i.e., correction for organisms/ARGs confirmed by other test methods to agree with the BCID-GN results), the overall sample accuracy increased to 92.9% (860/926). The above-described sample results are depicted in Fig. 1. These results exclude 37 Gram-negative organisms representing 20 uncommon genera, 10 Gram-positive organisms, and one Candida species, detected in 5% of samples that are not targeted by the BCID-GN.
The PPA and NPA for the BCID-GN targets versus comparator methods are provided in Table 3 for the 13 Gram-negative targets in the order Enterobacterales, Table 4 for the eight non-Enterobacterales Gram-negative targets, Table 5 for the two Pan targets, and Table 6 for the six ARG targets. The overall weighted PPA includes all sample types and represents an average of the individual PPAs; the overall PPA was 93.6% across all 29 targets and 94.9% excluding the two Pan targets. For all tables, results are presented in the following categories: clinical samples (both prospectively and retrospectively collected samples), contrived samples, and all samples combined. Footnotes are provided, by target, to indicate the number of false-negative or false-positive results that were resolved by discordant resolution using PCR/sequencing. The results presented were calculated before discordant resolution and represent a comparison to comparator methods (e.g., local laboratory results). Results in parentheses indicate 95% confidence intervals.
TABLE 3.
Target | Clinical samples |
Contrived samples |
Combined samples |
|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PPA |
NPA |
PPA |
NPA |
PPA |
NPA |
|||||||
TP/TP+FN | % (95% CI) | TN/TN+FP | % (95% CI) | TP/TP+FN | % (95% CI) | TN/TN+FP | % (95% CI) | TP/TP+FN | % (95% CI) | TN/TN+FP | % (95% CI) | |
Citrobacter spp.a | 25/26 | 96.2 (81.1–99.3) | 896/900 | 99.6 (98.9–99.8) | 43/43 | 100 (91.8–100) | 734/734 | 100 (99.5–100) | 68/69 | 98.6 (92.2–99.7) | 1,630/1,634 | 99.8 (99.4–99.9) |
C. sakazakii | 1/1 | 100 (20.7–100) | 925/925 | 100 (99.6–100) | 45/45 | 100 (92.1–100) | 732/732 | 100 (99.5–100) | 46/46 | 100 (92.3–100) | 1,657/1,657 | 100 (99.8–100) |
Enterobacter spp.b | 20/22 | 90.9 (72.2–97.5) | 903/904 | 99.9 (99.4–100) | 36/36 | 100 (90.4–100) | 741/741 | 100 (99.5–100) | 56/58 | 96.6 (88.3–99.0) | 1,644/1,645 | 99.9 (99.7–100) |
E. cloacae complexc | 66/69 | 95.7 (88.0–98.5) | 852/857 | 99.4 (98.6–99.8) | 35/37 | 94.6 (82.3–98.5) | 739/740 | 99.9 (99.2–100) | 101/106 | 95.3 (89.4–98.0) | 1,591/1,597 | 99.6 (99.2–99.8) |
E. coli d | 263/273 | 96.3 (93.4–98.0) | 650/653 | 99.5 (98.7–99.8) | 52/52 | 100 (93.1–100) | 725/725 | 100 (99.5–100) | 315/325 | 96.9 (94.4–98.3) | 1,375/1,378 | 99.8 (99.4–99.9) |
K. oxytoca e | 40/47 | 85.1 (72.3–92.6) | 876/879 | 99.7 (99.0–99.9) | 20/20 | 100 (83.9–100) | 757/757 | 100 (99.5–100) | 60/67 | 89.6 (80.0–94.8) | 1,633/1,636 | 99.8 (99.5–99.9) |
K. pneumoniae groupf | 164/169 | 97.0 (93.3–98.7) | 753/757 | 99.5 (98.6–99.8) | 72/72 | 100 (94.9–100) | 705/705 | 100 (99.5–100) | 236/241 | 97.9 (95.2–99.1) | 1,458/1,462 | 99.7 (99.3–99.9) |
M. morganii g | 13/13 | 100 (77.2–100) | 912/913 | 99.9 (99.4–100) | 49/49 | 100 (92.7–100) | 728/728 | 100 (99.5–100) | 62/62 | 100 (94.2–100) | 1,640/1,641 | 99.9 (99.7–100) |
Proteus spp. | 76/78 | 97.4 (91.1–99.3) | 848/848 | 100 (99.5–100) | 9/9 | 100 (70.1–100) | 768/768 | 100 (99.5–100) | 85/87 | 97.7 (92.0–99.4) | 1,616/1,616 | 100 (99.8–100) |
P. mirabilis | 72/74 | 97.3 (90.7–99.3) | 852/852 | 100 (99.6–100) | 9/9 | 100 (70.1–100) | 768/768 | 100 (99.5–100) | 81/83 | 97.6 (91.6–99.3) | 1,620/1,620 | 100 (99.8–100) |
Salmonella h | 20/21 | 95.2 (77.3–99.2) | 905/905 | 100 (99.6–100) | 34/35 | 97.1 (85.5–99.5) | 742/742 | 100 (99.5–100) | 54/56 | 96.4 (87.9–99.0) | 1,647/1,647 | 100 (99.8–100) |
Serratia | 44/44 | 100 (92.0–100) | 881/882 | 99.9 (99.4–100) | 36/36 | 100 (90.4–100) | 741/741 | 100 (99.5–100) | 80/80 | 100 (95.4–100) | 1,622/1,623 | 99.9 (99.7–100) |
S. marcescens i | 43/43 | 100 (91.8–100) | 882/883 | 99.9 (99.4–100) | 19/19 | 100 (83.2–100) | 758/758 | 100 (99.5–100) | 62/62 | 100 (94.2–100) | 1,640/1,641 | 99.9 (99.7–100) |
Citrobacter braakii (2) and Citrobacter freundii (2) were detected in 4/4 false-positive clinical samples using PCR/sequencing.
A species of the Enterobacter non-cloacae complex was not detected in 2 false-negative clinical samples, but PCR/sequencing detected E. cloacae, which was not identified by standard laboratory procedures. Enterobacter non-cloacae complex was not detected in the false-positive sample using PCR/sequencing.
A species of the Enterobacter cloacae complex was not detected in 1 false-negative clinical sample, but PCR/sequencing and MALDI-TOF detected E. coli, which was not identified by standard laboratory procedures. E. cloacae was detected in 2/5 false-positive clinical samples using PCR/sequencing.
E. coli was not detected in 1 false-negative clinical sample using PCR/sequencing; a different organism was not detected. E. coli was detected in 3/3 false-positive clinical samples using PCR/sequencing.
K. oxytoca was not detected in 2 false-negative clinical samples using PCR/sequencing, but 16S sequencing detected Raoultella ornithinolytica and R. planticola, which were not identified by standard laboratory procedures. K. oxytoca was not detected in an additional 2 false-negative clinical samples using PCR/sequencing; a different organism was not detected. Klebsiella oxytoca was detected in 3/3 false-positive clinical samples using PCR/sequencing.
K. pneumoniae was not detected in 1 false-negative clinical sample, but PCR/sequencing and MALDI-TOF detected K. oxytoca, which was not identified by standard laboratory procedures. K. pneumoniae was not detected in an additional 2 false-negative clinical samples using PCR/sequencing; a different organism was not detected. K. pneumoniae was detected in 4/4 false-positive clinical samples using PCR/sequencing.
M. morganii was detected in 1/1 false-positive clinical sample using PCR/sequencing.
Salmonella was not detected in 1 false-negative clinical sample, but PCR/sequencing detected E. coli, which was not identified by standard laboratory procedures.
S. marcescens was detected in the one false-positive clinical sample using PCR/sequencing.
TABLE 4.
Target | Clinical samples |
Contrived samples |
Combined samples |
|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PPA |
NPA |
PPA |
NPA |
PPA |
NPA |
|||||||
TP/TP+FN | % (95% CI) | TN/TN+FP | % (95% CI) | TP/TP+FN | % (95% CI) | TN/TN+FP | % (95% CI) | TP/TP+FN | % (95% CI) | TN/TN+FP | % (95% CI) | |
A. baumannii a | 19/19 | 100 (83.2–100) | 905/906 | 99.9 (99.4–100) | 55/55 | 100 (93.5–100) | 722/722 | 100 (99.5–100) | 74/74 | 100 (95.1–100) | 1,627/1,628 | 99.9 (99.7–100) |
B. fragilis b | 25/28 | 89.3 (72.8–96.3) | 896/898 | 99.8 (99.2–99.9) | 40/40 | 100 (91.2–100) | 737/737 | 100 (99.5–100) | 65/68 | 95.6 (87.8–98.5) | 1,633/1,635 | 99.9 (99.6–100) |
F. necrophorum | 1/1 | 100 (20.7–100) | 925/925 | 100 (99.6–100) | 47/48 | 97.9 (89.1–99.6) | 729/729 | 100 (99.5–100) | 48/49 | 98.0 (89.3–99.6) | 1,654/1,654 | 100 (99.8–100) |
F. nucleatum c | 5/5 | 100 (56.6–100) | 920/921 | 99.9 (99.4–100) | 47/47 | 100 (92.4–100) | 730/730 | 100 (99.5–100) | 52/52 | 100 (93.1–100) | 1,650/1,651 | 99.9 (99.7–100) |
H. influenzae | 14/14 | 100 (78.5–100) | 912/912 | 100 (99.6–100) | 41/41 | 100 (91.4–100) | 736/736 | 100 (99.5–100) | 55/55 | 100 (93.5–100) | 1,648/1,648 | 100 (99.8–100) |
N. meningitidis d | 0/0 | 925/926 | 99.9 (99.4–100) | 44/44 | 100 (92.0–100) | 733/733 | 100 (99.5–100) | 44/44 | 100 (92.0–100) | 1,658/1,659 | 99.9 (99.7–100) | |
P. aeruginosa e | 83/88 | 94.3 (87.4–97.5) | 834/838 | 99.5 (98.8–99.8) | 32/32 | 100 (89.3–100) | 745/745 | 100 (99.5–100) | 115/120 | 95.8 (90.6–98.2) | 1,579/1,583 | 99.7 (99.4–99.9) |
S. maltophilia f | 11/14 | 78.6 (52.4–92.4) | 911/912 | 99.9 (99.4–100) | 36/36 | 100 (90.4–100) | 741/741 | 100 (99.5–100) | 47/50 | 94.0 (83.8–97.9) | 1,652/1,653 | 99.9 (99.7–100) |
A. baumannii was detected in the 1/1 false-positive clinical sample using PCR/sequencing.
B. fragilis was not detected in 2 false-negative clinical samples, but PCR/sequencing detected B. caccae and B. thetaiotaomicron, which were not identified by standard laboratory procedures. B. fragilis was not detected in the additional 1 false-negative clinical sample using PCR/sequencing, but PCR/sequencing detected F. nucleatum (F. nucleatum was also detected by the ePlex BCID-GN panel). B. fragilis was detected in 2/2 false-positive clinical samples using PCR/sequencing.
F. nucleatum was detected in 1/1 false-positive clinical sample using PCR/sequencing.
N. meningitidis was not detected in the one false-positive clinical sample using PCR/sequencing.
P. aeruginosa was detected in 2/4 false-positive clinical samples using PCR/sequencing.
S. maltophilia was detected in the one false-positive clinical sample using PCR/sequencing.
TABLE 5.
Target | Clinical samples |
Contrived samples |
Combined samples |
|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PPA |
NPA |
PPA |
NPA |
PPA |
NPA |
|||||||
TP/TP+FN | % (95% CI) | TN/TN+FP | % (95% CI) | TP/TP+FN | % (95% CI) | TN/TN+FP | % (95% CI) | TP/TP+FN | % (95% CI) | TN/TN+FP | % (95% CI) | |
Pan Candida spp.a | 104/110 | 94.5 (88.6–97.5) | 1554/1557 | 99.8 (99.4–99.9) | 0/0 | 777/777 | 100 (99.5–100) | 104/110 | 94.5 (88.6–97.5) | 2,331/2,334 | 99.9 (99.6–100) | |
Pan Gram-positiveb | 628/649 | 96.8 (95.1–97.9) | 996/1018 | 97.8 (96.7–98.6) | 0/0 | 776/777 | 99.9 (99.3–100) | 628/649 | 96.8 (95.1–97.9) | 1,772/1,795 | 98.7 (98.1–99.1) |
Candida albicans (2 samples) and Candida glabrata (1 sample) were detected in 3/3 false-positive clinical samples using PCR/sequencing.
Bacillus (the Gram-positive organism identified by standard laboratory procedures) was not detected in 2 false-negative clinical samples using PCR/sequencing, but 16S sequencing detected Paenibacillus lautus and Paenibacillus urinalis, which were not identified by standard laboratory procedures. The Gram-positive organism identified by standard laboratory procedures (i.e., Bacillus and S. aureus) was not detected using PCR/sequencing in an additional 2 false-negative clinical samples; a different organism was not detected. Bacillus (1 sample), Enterococcus (3 samples), Staphylococcus (4 samples), and Streptococcus (8 samples) were detected in 16/22 false-positive clinical samples using PCR/sequencing.
TABLE 6.
Target | Clinical samples |
Contrived samples |
Combined samples |
|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PPA |
NPA |
PPA |
NPA |
PPA |
NPA |
|||||||
TP/TP+FN | % (95% CI) | TN/TN+FP | % (95% CI) | TP/TP+FN | % (95% CI) | TN/TN+FP | % (95% CI) | TP/TP+FN | % (95% CI) | TN/TN+FP | % (95% CI) | |
CTX-Ma | 74/85 | 87.1 (78.3–92.6) | 754/754 | 100 (99.5–100) | 75/75 | 100 (95.1–100) | 437/437 | 100 (99.1–100) | 149/160 | 93.1 (88.1–96.1) | 1,191/1,191 | 100 (99.7–100) |
IMP | 0/0 | 829/829 | 100 (99.5–100) | 40/40 | 100 (91.2–100) | 436/436 | 100 (99.1–100) | 40/40 | 100 (91.2–100) | 1,265/1,265 | 100 (99.7–100) | |
KPC | 7/8 | 87.5 (52.9–97.8) | 821/822 | 99.9 (99.3–100) | 44/44 | 100 (92.0–100) | 477/477 | 100 (99.2–100) | 51/52 | 98.1 (89.9–99.7) | 1,298/1,299 | 99.9 (99.6–100) |
NDM | 0/0 | 829/829 | 100 (99.5–100) | 54/54 | 100 (93.4–100) | 422/422 | 100 (99.1–100) | 54/54 | 100 (93.4–100) | 1,251/1,251 | 100 (99.7–100) | |
OXAb | 10/13 | 76.9 (49.7–91.8) | 814/816 | 99.8 (99.1–99.9) | 37/37 | 100 (90.6–100) | 439/439 | 100 (99.1–100) | 47/50 | 94.0 (83.8–97.9) | 1,253/1,255 | 99.8 (99.4–100) |
VIM | 0/0 | 829/829 | 100 (99.5–100) | 42/42 | 100 (91.6–100) | 434/434 | 100 (99.1–100) | 42/42 | 100 (91.6–100) | 1,263/1,263 | 100 (99.7–100) |
For 8/11 remaining false-negative CTX-M target detections, discordant analysis was not evaluable due to sample contamination.
In 1/3 false-negative clinical samples, the OXA signal was above the threshold for detection; however, an associated organism was not detected by the BCID-GN, and the OXA target was reported as NA. One additional false-negative sample was tested with an FDA-cleared multiplex assay, and OXA was not detected. The isolate from the remaining false-negative sample tested negative for OXA-23 and OXA-48 by qPCR.
The BCID-GN was tested to establish limits of detection (LOD) at bacterial suspensions with densities between 1 × 104 and 1 × 107 CFU/ml. The density range was selected to represent the typical densities of bacteria encountered at the time of bottles being flagged positive by blood culture instruments and to create standard microbial densities for analysis. One exception was F. necrophorum, which was tested at a density of 1 × 107 and 1 × 108 CFU/ml due to its slower metabolism; it was known that 1 × 108 CFU/ml was required.
(i) Enterobacterales.
The overall PPA for the common Enterobacterales ranged from a high of 100% for Cronobacter sakazakii, Morganella morganii, Serratia marcescens, and Serratia spp. to a low of 89.6% for Klebsiella oxytoca. Overall NPA for the common Enterobacterales ranged from 99.6% to 100%. The combined sample size for each target in Table 3 is 1,703. For positive results, the sample size ranged from a low of 46 for C. sakazakii to a high of 325 for Escherichia coli. Across the Enterobacterales targets, there were 22 false-positive results from retrospective (frozen) and prospective (fresh) clinical samples. Based on the discordant resolution with PCR/sequencing, 18 of these were confirmed to be correctly detected by the BCID-GN. Enterobacterales targets with discordant results are discussed below.
(a) Citrobacter species. Overall PPA and NPA were 98.6% and 99.8%, respectively, with one false-negative and four false-positive results. Citrobacter braakii (n = 2) and Citrobacter freundii (n = 2) were detected using PCR/sequencing in the four false-positive samples.
(b) Enterobacter species. Overall PPA and NPA for Enterobacter (non-cloacae complex) were 96.6% and 99.9%, respectively, with two false-negative and one false-positive result. In two false-negative samples, PCR/sequencing detected E. cloacae instead of a non-cloacae species. For E. cloacae complex, overall PPA and NPA were 95.3% and 99.6%, respectively, with five false negative and six false positives, of which two false-positive samples had E. cloacae detected by PCR/sequencing (the same two samples that had false-negative results for the Enterobacter target).
(c) Escherichia coli. Overall PPA and NPA of E. coli were 96.9% and 99.8%, with 10 false-negative and three false-positive results. The presence of E. coli was confirmed in all three false-positive samples by PCR or sequencing.
(d) Klebsiella oxytoca and K. pneumoniae group. The K. oxytoca target’s overall PPA was 89.6% and NPA was 99.8%, with seven false-negative and three false-positive results. In two false-negative samples, a Raoultella species was detected by 16S sequencing instead of K. oxytoca, and in two other false-negative samples, K. oxytoca was not detected by PCR/sequencing. K. oxytoca was detected by PCR/sequencing in all three false-positive samples. The K. pneumoniae group target had an overall PPA of 97.9% and NPA of 99.7%, with five false-negative and four false-positive results. One of the false-negative results was determined to be K. oxytoca by both PCR/sequencing and MALDI-TOF MS (one of the samples that had false-positive K. oxytoca results, discussed above), with K. pneumoniae not detected in two of the remaining false-negative samples. All four false-positive samples had the presence of K. pneumoniae confirmed by PCR, MALDI-TOF, or sequencing.
(e) Morganella morganii. The M. morganii target had an overall PPA of 100% and NPA of 99.9%, with one false-positive result that was confirmed to be M. morganii by PCR/sequencing.
(f) Proteus species. The Proteus species and Proteus mirabilis targets had similar results, with an overall PPA of 97.6% to 97.7% and NPA of 100%. There were two false-negative results, both the result of one sample; both had P. mirabilis detected by PCR/sequencing.
(g) Salmonella species. The Salmonella species target had an overall PPA of 96.4% and NPA of 100%, with two false-negative results, one of which was determined to be E. coli by PCR/sequencing.
(h) Serratia species. The Serratia spp. and S. marcescens targets both had an overall PPA of 100% and NPA of 99.9%, with one false-positive result that was confirmed to be S. marcescens by PCR/sequencing.
(ii) Non-Enterobacterales.
The overall PPA for the non-Enterobacterales ranged from a high of 100% for A. baumannii, Fusobacterium nucleatum, Haemophilus influenzae, and Neisseria meningitidis to a low of 94.0% for Stenotrophomonas maltophilia. Overall NPA for the non-Enterobacterales ranged from 99.7% to 100%. The combined sample size for each target is in Table 4. Non-Enterobacterales is 1,703 for all targets except for A. baumannii complex, for which one sample could not be verified as A. baumannii or another species in the complex (n = 1,702). For positive results, the sample size ranged from a low of 44 for N. meningitidis to a high of 120 for Pseudomonas aeruginosa. Across the non-Enterobacterales targets, there were 10 false-positive results in clinical samples; seven of these were confirmed to be correctly detected by the BCID-GN based on discordant resolution with PCR/sequencing. Non-Enterobacterales targets with discordant results are discussed below.
(a) Acinetobacter baumannii. The A. baumannii target had an overall PPA of 100% and NPA of 99.9%, with one false-positive result that was confirmed as A. baumannii by PCR/sequencing.
(b) Bacteroides fragilis. The B. fragilis target had an overall PPA of 95.6% and NPA of 99.9%, with three false-negative and two false-positive results. In two of the false-negative samples, PCR/sequencing detected Bacteroides caccae and Bacteroides thetaiotaomicron. Both false-positive samples had B. fragilis detected by PCR/sequencing.
(c) Fusobacterium.Fusobacterium necrophorum had an overall PPA of 98.0% and NPA of 100%, with one false-negative result. F. nucleatum had an overall PPA of 100% and NPA of 99.9%, with one false-positive sample that had F. nucleatum detected by PCR/sequencing.
(d) Neisseria meningitidis. The N. meningitidis target had 100% overall PPA and 99.9% NPA, with one false-positive result that was not detected by PCR/sequencing.
(e) Pseudomonas aeruginosa. The P. aeruginosa target had an overall PPA of 95.8% and NPA of 99.7%, with five false-negative results and four false-positive results. The five false-negative samples and two of the four false-positive samples had P. aeruginosa detected by PCR/sequencing.
(f) Stenotrophomonas maltophilia. The S. maltophilia target had an overall PPA of 94.0% and NPA of 99.9%, with three false-negative results and one false-positive result. Two false-negative and one false-positive sample had S. maltophilia detected by PCR/sequencing.
(iii) Pan targets.
The combined performance data for Pan targets is represented in Table 5 for 2,444 samples. The Pan Candida target’s PPA and NPA were 94.5% and 99.9%, respectively (n = 110 for positive samples), while the Pan Gram-positive target had 96.8% PPA and 98.7% NPA (n = 649 for positive samples). In terms of false-negative discrepancies, Bacillus spp. identified by standard laboratory procedures were not detected in two clinical samples; instead, 16S sequencing detected Paenibacillus lautus and Paenibacillus urinalis. There were three false-positive Pan Candida results, all of which were confirmed to be correctly detected by the BCID-GN after discordant resolution (Candida albicans [n = 2], Candida glabrata [n = 1]). There were 22 false-positive Pan Gram-positive results in clinical samples; 16 were confirmed to be correctly detected by the BCID-GN based on discordant resolution with PCR/sequencing (Bacillus spp. [n = 1], Enterococcus spp. [n = 3], Staphylococcus spp. [n = 4], and Streptococcus spp. [n = 8]).
(iv) ARGs.
Table 6 represents performance for ARG targets associated with the microbes listed in Table 2. An associated organism for CTX-M or KPC was present in 1,351 samples, and for the remaining ARGs, an associated organism was present in 1,305 samples. IMP, NDM, and VIM targets had 100% PPA and NPA but were only detected in 40, 54, and 42 contrived samples, respectively. Including clinical and contrived samples, KPC PPA was 98.1% and NPA was 99.9% (8 clinical and 44 contrived positive samples). The CTX-M target represents the lowest combined PPA at 93.1%, with 100% NPA. For 8/11 remaining false-negative CTX-M target detections, discordant analysis was not evaluable due to sample contamination. With those samples excluded, the CTX-M PPA is 97.4%. Additionally, three false-negative CTX-M results occurred due to the associated genetic target not being detected by the BCID-GN (CTX-M signal was above the threshold for detection but was reported as not applicable [NA] due to the lack of organism detection). OXA PPA was 94.0% and NPA was 99.8% (13 clinical and 37 contrived samples). An investigation into false-negative results similar to that undertaken for CTX-M determined that one OXA-positive sample was contaminated. One sample also did not have the associated organism detected by the BCID-GN, but the OXA signal was above the threshold for detection. For ARG comparison with phenotype results, refer to the 510k summary (10).
(v) Microbial diversity.
Table S3 summarizes data from Tables 1 to 4, substratified by organism and organism group. This view visualizes the breadth and depth of the microbial diversity tested in the clinical study. Seventy-nine species or microbial groups had targets detected in the samples tested. Forty-three Gram-positive microbes were detected by the Pan Gram-positive target and four yeast species were detected by the Pan Candida target.
Table S4 summarizes results from the rare microbes identified by standard of care methods that are off-panel for the BCID-GN. For prospective and retrospective samples, these include 10 Gram-positive organisms representing seven genera, 37 Gram-negative organisms representing 19 genera, and one yeast, Candida lusitaniae. The most common genera not included on the BCID-GN were Acinetobacter (n = 6), Providencia (n = 6), and Achromobacter (n = 3), with species from the genera Aeromonas, Bacteroides, Clostridium, Moraxella, Micrococcus, Leclercia, and Sphingomonas each identified in two samples.
(vi) Polymicrobial samples.
Table 7 summarizes clinical samples with multiple organisms detected by the BCID-GN. The BCID-GN identified 103 samples with multiple organisms. Of these, 84 (9.0%) samples contained two organisms, and 19 (2.0%) samples had three organisms. Within the 103 total codetections, 69 contained the same organisms identified by comparator methods; the remaining 34 codetections (polymicrobial samples) included 39 organisms that were missed by the comparator method (i.e., false positive). Discordant resolution confirmed the presence of 32 organisms, whereas four were unconfirmed and three with Pan Gram-positive false-positive results were not tested.
TABLE 7.
Codetection type | No. of codetections (% of samples) | No. of discrepant codetections | No. of discrepant organism ARG(s)a |
---|---|---|---|
Total | 103 (11.1%) | 34 | 39 |
Double detections | 84 (9.0%) | 22 | 22 |
Triple detections | 19 (2.0%) | 12 | 17 |
A discrepant organism/ARG is one that was detected by the BCID-GN panel but not by the comparator method(s) (i.e., false positive); 36/39 false-positive organisms were investigated using PCR/sequencing (3 Pan GP organisms were not tested). Of the 36 false-positive organisms tested, 32 were detected, 3 were not detected, and 1 was indeterminate.
Refer to the 510k summary (Tables 60 and 61) for more detail (10).
Table 8 represents the 103 distinct multiple organisms and antibiotic gene combinations detected by the BCID-GN. The 32 discrepant false-positive organisms that were confirmed present in the sample by discordant resolution are denoted with an asterisk (*), and the remaining seven discrepant false-positive organisms are denoted with a caret (^). The 38 additional distinct codetections identified by comparator methods, which include false-negative organism(s), are provided in Table 9.
TABLE 8.
Distinct codetection combinations detected by the BCID-GN panelb |
||||
---|---|---|---|---|
1 | 2 | 3 | ARG | No. of samples (no. discrepanta) |
A. baumannii* | K. pneumoniae GP* | Pan GP* | CTX-M, OXA | 1 (1) |
A. baumannii | Pan GP | 4 (0) | ||
A. baumannii | Pan GP^ | OXA | 4 (1) | |
B. fragilis* | E. cloacae complex | Pan GP | 1 (1) | |
B. fragilis* | E. coli | 2 (1) | ||
B. fragilis | Pan GP | 1 (0) | ||
Citrobacter | E. cloacae complex^ | 1 (1) | ||
Citrobacter* | E. cloacae complex* | K. oxytoca | 2 (2) | |
Citrobacter | E. coli | 1 (0) | ||
Citrobacter* | K. oxytoca | 1 (1) | ||
Citrobacter* | K. oxytoca | K. pneumoniae GP | 1 (1) | |
Citrobacter | K. oxytoca* | K. pneumoniae GP | 1 (1) | |
Citrobacter | K. pneumoniae GP | 1 (0) | ||
Citrobacter | K. pneumoniae GP | Pan GP | CTX-M | 1 (0) |
Citrobacter | M. morganii* | Pan GP | 1 (1) | |
Citrobacter | P. mirabilis | Pan GP* | 1 (1) | |
Citrobacter | Pan GP* | 3 (2) | ||
E. cloacae complex | E. coli | K. pneumoniae GP | 1 (0) | |
E. cloacae complex | K. pneumoniae GP | 1 (0) | ||
E. cloacae complex | P. aeruginosa* | Pan GP | 1 (1) | |
E. cloacae complex | Pan Candida* | 1 (1) | ||
E. cloacae complex | Pan Candida | Pan GP | 1 (0) | |
E. cloacae complex | Pan GP^ | 4 (1) | ||
E. coli | K. oxytoca* | 3 (1) | ||
E. coli | K. oxytoca | Pan GP | 1 (0) | |
E. coli | K. pneumoniae GP | 2 (0) | ||
E. coli* | K. pneumoniae GP | CTX-M | 1 (1) | |
E. coli | M. morganii | 1 (0) | ||
E. coli | P. mirabilis | 3 (0) | ||
E. coli | P. mirabilis | Pan GP | 1 (0) | |
E. coli | Pan GP* | 10 (3) | ||
E. coli | Pan GP | CTX-M | 1 (0) | |
Enterobacter ^ | K. pneumoniae GP | 1 (1) | ||
Enterobacter | Pan Candida | 1 (0) | ||
Enterobacter | Pan GP | 1 (0) | ||
H. influenzae | N. meningitidis^ | P. aeruginosa^ | 1 (1) | |
K. oxytoca | K. pneumoniae GP* | 2 (1) | ||
K. oxytoca | Pan GP* | 4 (2) | ||
K. oxytoca | S. marcescens* | 2 (1) | ||
K. pneumoniae GP | P. mirabilis | 1 (0) | ||
K. pneumoniae GP | Pan GP* | 6 (1) | ||
K. pneumoniae GP | Pan GP^ | CTX-M, KPC | 1 (1) | |
K. pneumoniae GP* | Pan GP | S. marcescens | 1 (1) | |
K. pneumoniae GP | S. maltophilia | 1 (0) | ||
M. morganii | P. aeruginosa* | Pan GP | 1 (1) | |
M. morganii | P. mirabilis | 2 (0) | ||
M. morganii | Pan GP | Proteus | 1 (0) | |
P. aeruginosa | P. mirabilis | Pan GP | 1 (0) | |
P. aeruginosa | Pan GP | 2 (0) | ||
P. mirabilis | Pan GP* | 8 (2) | ||
P. mirabilis | Pan GP | CTX-M | 1 (0) | |
Pan Candida | Pan GP | 2 (0) | ||
Pan GP | S. maltophilia | 1 (0) | ||
Pan GP | S. marcescens | 3 (0) |
A discrepant organism/ARG is one that was detected by the BCID-GN but not by the comparator method(s) (i.e., false positive); 36/39 false-positive organisms were investigated using PCR/sequencing (3 Pan GP organisms were not tested). Of the 36 false-positive organisms tested, 32 were detected, 3 were not detected, and 1 was indeterminate.
*, Discordant false-positive organism confirmed present by PCR/sequencing; ^, discordant false-positive organism not confirmed present by PCR/sequencing.
TABLE 9.
Distinct organism/antibiotic resistance gene combinations detected by comparator methodsb |
No. of samples (no. discreptant) | No. of discrepant organism(s)/ARG(s)a | ||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | ARG | ||
Aeromonas caviae* | E. coli | Enterococcus casseliflavus | K. oxytoca | 1 (1) | E. casseliflavus (1) | |
Aeromonas veronii* | E. cloacae | 1 (1) | E. cloacae (1) | |||
C. albicans | E. faecium | Staphylococcus hominis | 1 (1) | C. albicans (1) | ||
C. albicans | P. aeruginosa | 1 (1) | C. albicans (1) | |||
C. braakii | E. cloacae | K. oxytoca | 1 (1) |
C. braakii (1), K. oxytoca (1) |
||
C. freundii | Enterococcus | 1 (1) | Enterococcus (1) | |||
C. glabrata | E. aerogenes | Staphylococcus | 1 (1) | Staphylococcus (1) | ||
C. glabrata | P. mirabilis | 1 (1) | C. glabrata (1) | |||
C. krusei | S. epidermidis | 1 (1) | S. epidermidis (1) | |||
C. youngae | K. oxytoca | 1 (1) | K. oxytoca (1) | |||
E. aerogenes | K. oxytoca | Leclercia adecarboxylata* | 2 (2) | E. aerogenes (2) | ||
E. aerogenes | P. aeruginosa | 1 (1) | P. aeruginosa (1) | |||
E. cloacae | E. coli | 1 (1) | E. coli (1) | |||
E. cloacae | E. faecalis | 1 (1) | E. faecalis (1) | |||
E. cloacae | M. morganii | 1 (1) | E. cloacae (1) | |||
E. cloacae | S. maltophilia | 1 (1) | S. maltophilia (1) | |||
E. coli | E. faecalis | K. pneumoniae | 1 (1) | E. coli (1) | ||
E. coli | E. faecium | CTX-M | 1 (1) | E. faecium (1) | ||
E. coli | K. pneumoniae | 1 (1) | E. coli (1) | |||
E. coli | P. aeruginosa | 1 (1) | P. aeruginosa (1) | |||
E. coli | P. mirabilis | 1 (1) | E. coli (1) | |||
E. coli | P. mirabilis | P. vulgaris | Streptococcus viridans group | 1 (1) | S. viridans group (1) | |
E. coli | P. mirabilis | Providencia stuartii* | S. anginosus GP | CTX-M | 1 (1) | E. coli (1) |
E. coli | S. anginosus GP | 1 (1) | S. anginosus gp (1) | |||
E. faecalis | K. pneumoniae | 2 (2) |
E. faecalis (1), K. pneumoniae (1) |
|||
E. faecalis | M. morganii | P. mirabilis | 1 (1) | E. faecalis (1) | ||
E. faecalis | P. aeruginosa | S. aureus | 1 (1) | P. aeruginosa (1) | ||
E. faecalis | S. maltophilia | 1 (1) | E. faecalis (1) | |||
K. pneumoniae | P. aeruginosa | 1 (1) | P. aeruginosa (1) | |||
K. pneumoniae | S. aureus | 1 (1) | S. aureus (1) | |||
K. pneumoniae | Staphylococcus | 1 (1) | Staphylococcus (1) | |||
K. pneumoniae | Staphylococcus haemolyticus | Nonfermenting GN bacilli* | 1 (1) | K. pneumoniae (1) | ||
P. aeruginosa | S. maltophilia | 2 (2) | S. maltophilia (2) | |||
P. mirabilis | Providencia stuartii* | 1 (1) | P. mirabilis (1) | |||
S. maltophilia | Streptococcus | 1 (1) | Streptococcus (1) |
A discrepant organism/ARG is defined as one that was detected by the comparator method(s), which should have been detected by the BCID-GN but was not (excludes organisms not targeted by the BCID-GN).
*, Off-panel organism not targeted by the BCID-GN.
Table 9 illustrates the distribution of false-negative results found in mixtures for the BCID-GN. Seven codetections include a BCID-GN off-panel organism, denoted with an asterisk, which is not expected to be identified by the BCID-GN. Other false-negative results include a variety of microbes in mixtures, some rapid growing and some slow growing. No apparent pattern of microbial false-negative results is evident. E. coli, P. aeruginosa, and Enterococcus faecalis were the most frequently missed pathogens, followed by C. albicans.
DISCUSSION
The BCID-GN provides results describing pathogens identified in PBC bottles in approximately 90 min on the ePlex platform, a scalable (3 to 24 bays), random, and continuous-access instrument with automated quality control monitoring. With this system, hands-on time from PBC bottles is <2 min (19). The panel was challenged with a well-populated sample set representing the most identified Gram-negative bloodstream pathogens (20–25) and ARG targets, with an overall accuracy of 92.9% after discordant resolution. Although molecular discordant resolution has limitations, it is a practical option for a comparative accuracy study for molecular multiplex methods, as conventional culture methods cannot detect nucleic acids and, therefore, cannot verify the presence or absence of genetic sequences. This approach has been used in similar previously published studies (11–14).
The panel is diverse and reflects common bloodstream pathogens as well as rare microbes with high pathogenicity. For example, as Salmonella strains become more resistant (26), a rapid Salmonella species result can be useful in streamlining antimicrobial treatment. Another example is the non-Enterobacterales; although less common than the Enterobacterales targets, they are pathogens that pose significant public health risks (24, 25, 27). Both H. influenzae and N. meningitidis are commonly reportable to state public health laboratories (28). For N. meningitidis, public health and infection prevention investigations can include contacts with the source patient; therefore, rapid results support these efforts and could speed targeted antimicrobial therapy. Antimicrobial chemoprophylaxis of close contacts of a patient with meningococcal disease is important to prevent secondary cases (29), regardless of whether a meningococcal outbreak is suspected.
BCID-GN utility is also noted for the identification of bacteremia caused by two Fusobacterium spp., which are associated with a high mortality rate for patients with renal insufficiency, heart failure or malignancies, and Lemierre’s syndrome (30). Rapid identification of Fusobacterium species and B. fragilis supports the ability to broaden coverage for anaerobic bacteria, which can be absent in some empirical antibiotic regimens (30, 31). Finally, identification of the nonfermenters P. aeruginosa, S. maltophilia, and A. baumannii will support the escalation of therapy while broadening coverage to include the multidrug resistance (MDR) of these bacteria (32–35). The BCID-GN can provide information to hospital epidemiologists, infection preventionists, and bed management teams by providing faster identification of MDR microbes, upon which to base isolation practices for microbes with the potential for health care-associated outbreaks (36–38).
As with any molecular syndromic test panel, it is impossible to include all microbes that could be identified by routine culture methods in a panel. In this case, 5% of organisms were identified by culture only. The breadth of the culture-positive/BCID-GN-negative samples includes 39 rare species, five of which could be opportunistic pathogens from skin contamination of blood cultures in the proper clinical setting. Only Providencia stuartii (n = 6) exceeded five samples, followed by Acinetobacter radioresistans (n = 3) and then Micrococcus luteus and Leclercia adecarboxylata (n = 2 each). The remaining 35 species were represented by a frequency of one among 926 samples tested (0.01%); nine Gram-positive and 33 Gram-negative microbes needed to be identified by traditional methods. In comparison, some alternative commercial panels have fewer targets, thereby missing clinically significant organisms, such as anaerobes (Bacteroides spp. and Fusobacterium spp.) (8) or S. maltophilia, whereby awareness of its predictable and intrinsic antibiotic resistance adds benefit to therapeutic decision-making. The added diversity of pathogens detected by BCID-GN adds value compared to some other methods (8), which do not provide the extensive diversity, sensitivity, or improved ability to discern mixtures (39, 40); however, newer versions of panels are now available with a broader microbe menu (6). One option for identification of a much broader diversity of microbes is the use of MALDI-TOF MS methods, direct from blood cultures (41–43). MALDI-TOF MS does not yet provide any resistance profiles for Gram-positive or Gram-negative organisms and does not work well for mixed infections.
The BCID-GN is the only currently available, FDA-cleared molecular panel that incorporates a Pan Gram-positive and Pan Candida targets into a Gram-negative panel. The inclusion of these targets could limit the conditions under which Gram-positive or fungal antimicrobial coverage is inadvertently discontinued if the Gram stain is not sensitive enough to visualize these microbes or when fast-growing Gram-negative microbes predominate at the time of the stain. These targets are an important addition to antimicrobial decision strategy, because coinfections are not uncommon and the added protection of the Pan Gram-positive call prevents improper deescalation (40, 44). The use of the Pan Gram-positive target limits the number of times one would have to test both panels, as one would do in some other methods (45), with potential cost-benefits. The Accelerate Pheno reports a panbacterial target, but the result is not Gram stain specific; it simply alerts the user to the presence of bacteria (46).
The Pan Gram-positive target is designed to primarily detect organisms that may be missed by Gram stain or are potentially Gram variable. The Pan Gram-positive results include organisms commonly considered contaminants found on the skin that may also be opportunistic pathogens and associated with illnesses such as endocarditis. While the lack of inclusion of other skin contaminants (C. acnes, Lactobacillus spp., Micrococcus spp.) in the Pan Gram-positive result might be useful in certain populations (47), in other populations, laboratorians might need to scrutinize patient history more closely (48). Some of the organisms detected by the pan-GP target could be BCC (13, 49, 50) (refer to Table S3), and some could be true infections. Traditional chart reviews to assess blood culture contamination events still apply to molecular methods.
Performance of the full ePlex BCID Gram-positive panel for immunocompromised patients, transplant patients, cancer patients, and others likely exhibiting a significant infection (48) may be necessary. For example, implications for Bacillus spp. that are associated with severe infections in transplant populations (51), Staphylococcus epidermidis, Micrococcus spp., and Cutibacterium acnes (51, 52), which may be implicated in endocarditis and other infections, and Corynebacterium spp., which can cause MDR infections due to Corynebacterium jeikeium and Corynebacterium striatum (51). The Pan Candida target on the BCID-GN only detects C. albicans, C. parapsilosis, C. glabrata, and C. krusei, while other, less prevalent Candida species, including C. auris, are not included. When yeast species are observed on Gram stain or when a Pan Candida target is identified in the absence of a Gram stain exhibiting yeast, the BCID-FP (fungal pathogen) panel could be used for further testing (53).
One of the most important advantages of the BCID-GN is its ability to differentiate among microbes with similar Gram reactions, thereby increasing its ability to identify coinfections. The BCID-GN identified 103 samples with multiple (two or three) organisms (11.1%). Importantly, 34 codetections included 39 organisms that were missed by the comparator method, with 32 organisms confirmed present by discordant resolution. Of 103 multiple distinct organisms and antibiotic gene combinations detected by the BCID-GN, a Pan Gram-positive result was detected in 69 coinfections; for 15 of these (22%), the SOC method did not detect the Gram-positive organism. Of the 15, 12 were confirmed, and three were not tested. The Gram stain result only identified the presence of Gram-positive microbes in one of 15 samples, missing 14/15 Gram-positive results. A positive result for BCID-GN where the culture did not grow an organism could be the result of a patient being treated with antibiotics that could render an organism nonculturable but present and viable in the bloodstream, or it could represent the short-lived presence of nucleic acid sequence in the bloodstream, which could be detected by molecular methods.
The BCID-GN method is a PCR that was tested to establish limits of detection (LOD) at bacterial suspensions with densities between 1 × 104 and 1 × 107 CFU/ml, with the exception of F. necrophorum, which was tested at a density of 1 × 108 CFU/ml. The density range was selected to represent the typical densities of bacteria encountered at the time of bottles being flagged positive by blood culture instruments.
The LODs for microbes on the BCID-GN support detection of mixed cultures, as evidenced by the larger number of mixed infections identified in this trial compared to others (54). The low LOD becomes important with mixed infections, since some slower-growing microbes may be present in lower numbers than the faster-growing species. Because background DNA (i.e., the presence of DNA from nonviable organisms) has been found in various brands of blood culture media and has been detected by several molecular methods (55, 56), it is considered best practice to correlate results from rapid molecular detection with subsequent subculture growth and the prescribed antimicrobials present in the patient’s bloodstream at the time of blood culture collection. With any rapid molecular detection method, including BCID-GN, vigilance and investigation of rare discrepancies caused by dead microbes or excess DNA in blood culture broth should occur (18).
In this study, some coinfections were identified solely by SOC methods; they represent a mixture of microbes similar to those that were missed by SOC. A plausible assumption is that sampling variability and sampling error contributed, as defined by the statistical Poisson distribution characterizing microbes present in solution at low density, a known limitation to any method that attempts to identify microbes in low density (57). There are no apparent trends to the microbes undetected by the BCID-GN; we refer the reader to the 510k summary for further information (10). Therefore, the results likely indicate the presence of organisms in lower concentrations at the time of testing. E. coli, P. aeruginosa, and E. faecalis were the most commonly missed pathogens, followed by C. albicans, which is relatively slow growing.
The BCID-GN contains targets for the class A carbapenemase, KPC, the class B metallo-beta-lactamases IMP, VIM, and NDM, and the class D beta-lactamase OXA (23 and 48), frequently produced by A. baumannii; all confer resistance to carbapenems (58). The BCID-GN targets the CTX-M group of ESBLs, which are the most common ESBLs globally and mediate resistance to penicillins, cephalosporins, and monobactams (59). Accuracy for antimicrobial resistance gene targets (n = 1,351) was high, ranging from 93.1% to 100%, and had matching performance that is similar to or better than those of phenotypic and molecular comparator methods for antimicrobial susceptibility testing (60). The opportunity for antimicrobial stewardship programs to escalate therapy has been clearly demonstrated for the identification of resistance genes in Gram-negative bacteria causing bloodstream infections (61). Likewise, in local pockets where MDR clones are common, NPAs for ceftriaxone susceptibility in E. coli and K. pneumoniae in the absence of either CTX-M or a carbapenemase gene were 98% and 93 to 94%, respectively (40), suggesting that, depending on local epidemiology, formulary, and stewardship practices, the absence of a resistance gene can guide antimicrobial deescalation. Negative results for these select antimicrobial resistance gene assays do not indicate susceptibility, as there are multiple mechanisms of resistance (62).
One limitation of the BCID-GN is the exclusion of the mcr-1 and mcr-2 genes, which are indicative of colistin resistance (63). In the United States, colistin resistance is relatively rare and colistin is not a first-line antibiotic; therefore, reflex testing can occur. However, colistin resistance is a global challenge, and its panel exclusion may represent a challenge in some global settings (64).
The overall study limitations include potential bias introduced by testing more than one bottle from the same patient in the retrospective arm and the need to use contrived samples, as they may not reflect the contemporary bacterial strains circulating in the local geography.
While it is common to use contrived samples in clinical trials to supplement the breadth of the microbes and ARG targets that occur during the clinical trial period, it is prudent to verify multiplex methods with as many fresh samples as possible and to comply with state and federal regulations for new method verification. Overall, the targeted study population and sample matrices mimic reality in many laboratories; thus, results should be generalizable to other settings. The proportion of prospective or retrospective clinical results was acceptable to the U.S. FDA. The testing was performed in geographically distinct regions of the United States, with populations ranging from community-based hospitals to quaternary care health care organizations, performed by medical technologists or equivalents. As with any molecular blood culture identification method, clinical presentation and the use of additional laboratory testing of PBCs is still required (e.g., subculturing to identify organisms that are not detected by the panels for susceptibility testing, differentiation of mixed growth, and association of antimicrobial resistance marker genes to a specific organism) for the final diagnosis of bacterial bloodstream infection.
The ePlex BCID-GN offers advantages as a significant aid in the diagnosis of specific agents of bacteremia. The Pan target calls are present to alert laboratory scientists to a potential error in Gram stain reading and microbes that may be present at low density, and all laboratories can benefit from the ability to detect anaerobic pathogens and a wider range of ARG genes. The throughput range of the ePlex instrument is practical for both community hospitals and large core laboratories, with particular advantages to integrated delivery laboratories that can match throughput needs to the size of the individual laboratories while maintaining the same protocols and computer interfaces. With the addition of the BCID-GN to the previously FDA-cleared ePlex BCID panels, including the blood culture identification Gram-positive panel (BCID-GP) (13) and fungal pathogen panel (BCID-FP) (14), the ePlex BCID panels become the most comprehensive rapid molecular diagnostic offering for the identification of pathogens and resistance mechanisms causing bloodstream infections, with the ability to detect a total of 41 bacterial targets, 15 fungal targets, and 10 ARG markers from a PBC bottle.
ACKNOWLEDGMENTS
We thank the medical laboratory scientists from all participating clinical laboratories: Geisinger Medical Center and the Weis Center for Research (specifically Amanda M. Styer and Alexa Jefferis); TriCore Reference Laboratories; Johns Hopkins University (specifically Teresa Wakefield, Shawna Lewis, Marissa Totten, Karl Ramos, Sarah Becket, and Austin Etape), Medical College of Wisconsin; Indiana University; and Detroit Medical Center (specifically Maureen Taylor and Robert Mitchell).
Footnotes
Supplemental material is available online only.
Contributor Information
Donna M. Wolk, Email: dmwolk@geisinger.edu.
Carey-Ann D. Burnham, Washington University School of Medicine
REFERENCES
- 1.Fleischmann C, Scherag A, Adhikari NK, Hartog CS, Tsaganos T, Schlattmann P, Angus DC, Reinhart K, International Forum of Acute Care Team . 2016. Assessment of global incidence and mortality of hospital-treated sepsis. Current estimates and limitations. Am J Respir Crit Care Med 193:259–272. 10.1164/rccm.201504-0781OC. [DOI] [PubMed] [Google Scholar]
- 2.Kumar A, Roberts D, Wood KE, Light B, Parrillo JE, Sharma S, Suppes R, Feinstein D, Zanotti S, Taiberg L, Gurka D, Kumar A, Cheang M. 2006. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med 34:1589–1596. 10.1097/01.CCM.0000217961.75225.E9. [DOI] [PubMed] [Google Scholar]
- 3.Kumar A. 2016. Systematic bias in meta-analyses of time to antimicrobial in sepsis studies. Crit Care Med 44:e234–e235. 10.1097/CCM.0000000000001512. [DOI] [PubMed] [Google Scholar]
- 4.Rhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R, Kumar A, Sevransky JE, Sprung CL, Nunnally ME, Rochwerg B, Rubenfeld GD, Angus DC, Annane D, Beale RJ, Bellinghan GJ, Bernard GR, Chiche JD, Coopersmith C, De Backer DP, French CJ, Fujishima S, Gerlach H, Hidalgo JL, Hollenberg SM, Jones AE, Karnad DR, Kleinpell RM, Koh Y, Lisboa TC, Machado FR, Marini JJ, Marshall JC, Mazuski JE, McIntyre LA, McLean AS, Mehta S, Moreno RP, Myburgh J, Navalesi P, Nishida O, Osborn TM, Perner A, Plunkett CM, Ranieri M, Schorr CA, Seckel MA, Seymour CW, Shieh L, Shukri KA, et al. 2017. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med 43:304–377. 10.1007/s00134-017-4683-6. [DOI] [PubMed] [Google Scholar]
- 5.Southern TR, VanSchooneveld TC, Bannister DL, Brown TL, Crismon AS, Buss SN, Iwen PC, Fey PD. 2015. Implementation and performance of the BioFire FilmArray blood culture identification panel with antimicrobial treatment recommendations for bloodstream infections at a midwestern academic tertiary hospital. Diagn Microbiol Infect Dis 81:96–101. 10.1016/j.diagmicrobio.2014.11.004. [DOI] [PubMed] [Google Scholar]
- 6.Cortazzo V, D’Inzeo T, Giordano L, Menchinelli G, Liotti FM, Fiori B, De Maio F, Luzzaro F, Sanguinetti M, Posteraro B, Spanu T. 2021. Comparing BioFire FilmArray BCID2 and BCID panels for direct detection of bacterial pathogens and antimicrobial resistance genes from positive blood cultures. J Clin Microbiol 59:e03163-20. 10.1128/JCM.03163-20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Berinson B, Both A, Berneking L, Christner M, Lutgehetmann M, Aepfelbacher M, Rohde H. 21May2021. Usefulness of Biofire FilmArray BCID2 for blood culture processing in clinical practice. J Clin Microbiol 10.1128/JCM.00543-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Walker T, Dumadag S, Lee CJ, Lee SH, Bender JM, Cupo Abbott J, She RC. 2016. Clinical impact of laboratory implementation of Verigene BC-GN microarray-based assay for detection of Gram-negative bacteria in positive blood cultures. J Clin Microbiol 54:1789–1796. 10.1128/JCM.00376-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Granato PA, Unz MM, Widen RH, Silbert S, Young S, Heflin KL, Conover MS, Buchan BW, Ledeboer NA. 2018. Clinical evaluation of the iCubate iC-GPC assay for detection of Gram-positive bacteria and resistance markers from positive blood cultures. J Clin Microbiol 56:e00485-18. 10.1128/JCM.00485-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.GenMark. 2019. ePlex blood culture identification Gram-negative (BCID-GN) panel. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMN/pmn.cfm?ID=K182619.
- 11.Maubon D, Dard C, Garnaud C, Cornet M. 2018. Profile of GenMark's ePlex blood culture identification fungal pathogen panel. Expert Rev Mol Diagn 18:119–132. 10.1080/14737159.2018.1420476. [DOI] [PubMed] [Google Scholar]
- 12.Babady NE, England MR, Jurcic Smith KL, He T, Wijetunge DS, Tang YW, Chamberland RR, Menegus M, Swierkosz EM, Jerris RC, Greene W. 2018. Multicenter evaluation of the ePlex respiratory pathogen panel for the detection of viral and bacterial respiratory tract pathogens in nasopharyngeal swabs. J Clin Microbiol 56:e01658-17. 10.1128/JCM.01658-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Carroll KC, Reid JL, Thornberg A, Whitfield NN, Trainor D, Lewis S, Wakefield T, Davis TE, Church KG, Samuel L, Mills R, Jim P, Young S, Nolte FS. 2020. Clinical performance of the novel GenMark Dx ePlex blood culture ID Gram-positive panel. J Clin Microbiol 58:e01730-19. 10.1128/JCM.01730-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Zhang SX, Carroll KC, Lewis S, Totten M, Mead P, Samuel L, Steed LL, Nolte FS, Thornberg A, Reid JL, Whitfield NN, Babady NE. 2020. Multicenter evaluation of a PCR-based digital microfluidics and electrochemical detection system for the rapid identification of 15 fungal pathogens directly from positive blood cultures. J Clin Microbiol 58:e02096-16. 10.1128/JCM.02096-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Chang HC, Wei YF, Dijkshoorn L, Vaneechoutte M, Tang CT, Chang TC. 2005. Species-level identification of isolates of the Acinetobacter calcoaceticus-Acinetobacter baumannii complex by sequence analysis of the 16S-23S rRNA gene spacer region. J Clin Microbiol 43:1632–1639. 10.1128/JCM.43.4.1632-1639.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Feng X, Wu Z, Ling B, Pan S, Liao W, Pan W, Yao Z. 2014. Identification and differentiation of Candida parapsilosis complex species by use of exon-primed intron-crossing PCR. J Clin Microbiol 52:1758–1761. 10.1128/JCM.00105-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.U.S. FDA. 2007. Guidance for industry and FDA staff: statistical guidance on reporting results from studies evaluating diagnostic tests. U.S. Food and Drug Administration, Silver Spring, MD. [Google Scholar]
- 18.Butler-Wu SD. 2020. Guidelines regarding genotypic false detections from blood culture bottles. ASM Press, Washington, DC. [Google Scholar]
- 19.Huang TD, Melnik E, Bogaerts P, Evrard S, Glupczynski Y. 2019. Evaluation of the ePlex blood culture identification panels for detection of pathogens in bloodstream infections. J Clin Microbiol 57:e01597-18. 10.1128/JCM.01597-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Vinker-Shuster M, Stepensky P, Temper V, Shayovitz V, Masarwa R, Averbuch D. 2019. Gram-negative bacteremia in children with hematologic malignancies and following hematopoietic stem cell transplantation: epidemiology, resistance, and outcome. J Pediatr Hematol Oncol 41:e493–e498. 10.1097/MPH.0000000000001556. [DOI] [PubMed] [Google Scholar]
- 21.Kadri SS, Adjemian J, Lai YL, Spaulding AB, Ricotta E, Prevots DR, Palmore TN, Rhee C, Klompas M, Dekker JP, PowersJH, III, Suffredini AF, Hooper DC, Fridkin S, Danner RL, National Institutes of Health Antimicrobial Resistance Outcomes Research Institute . 2018. Difficult-to-treat resistance in Gram-negative bacteremia at 173 US hospitals: retrospective cohort analysis of prevalence, predictors, and outcome of resistance to all first-line agents. Clin Infect Dis 67:1803–1814. 10.1093/cid/ciy378. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Sogaard KK, Farkas DK, Sogaard M, Schonheyder HC, Thomsen RW, Sorensen HT. 2017. Gram-negative bacteremia as a clinical marker of occult malignancy. J Infect 74:153–162. 10.1016/j.jinf.2016.09.011. [DOI] [PubMed] [Google Scholar]
- 23.Goto M, O'Shea AMJ, Livorsi DJ, McDanel JS, Jones MM, Richardson KK, Beck BF, Alexander B, Evans ME, Roselle GA, Kralovic SM, Perencevich EN. 2016. The effect of a nationwide infection control program expansion on hospital-onset Gram-negative rod bacteremia in 130 Veterans Health Administration medical centers: an interrupted time-series analysis. Clin Infect Dis 63:642–650. 10.1093/cid/ciw423. [DOI] [PubMed] [Google Scholar]
- 24.Sligl WI, Dragan T, Smith SW. 2015. Nosocomial Gram-negative bacteremia in intensive care: epidemiology, antimicrobial susceptibilities, and outcomes. Int J Infect Dis 37:129–134. 10.1016/j.ijid.2015.06.024. [DOI] [PubMed] [Google Scholar]
- 25.Chiu CW, Li MC, Ko WC, Li CW, Chen PL, Chang CM, Lee NY, Lee CC. 2015. Clinical impact of Gram-negative nonfermenters on adults with community-onset bacteremia in the emergency department. J Microbiol Immunol Infect 48:92–100. 10.1016/j.jmii.2013.08.004. [DOI] [PubMed] [Google Scholar]
- 26.Angelo KM, Reynolds J, Karp BE, Hoekstra RM, Scheel CM, Friedman C. 2016. Antimicrobial resistance among nontyphoidal salmonella isolated from blood in the United States, 2003–2013. J Infect Dis 214:1565–1570. 10.1093/infdis/jiw415. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Moghnieh R, Estaitieh N, Mugharbil A, Jisr T, Abdallah DI, Ziade F, Sinno L, Ibrahim A. 2015. Third generation cephalosporin resistant Enterobacteriaceae and multidrug resistant gram-negative bacteria causing bacteremia in febrile neutropenia adult cancer patients in Lebanon, broad spectrum antibiotics use as a major risk factor, and correlation with poor prognosis. Front Cell Infect Microbiol 5:11. 10.3389/fcimb.2015.00011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Martin NG, Sadarangani M, Pollard AJ, Goldacre MJ. 2014. Hospital admission rates for meningitis and septicaemia caused by Haemophilus influenzae, Neisseria meningitidis, and Streptococcus pneumoniae in children in England over five decades: a population-based observational study. Lancet Infect Dis 14:397–405. 10.1016/S1473-3099(14)70027-1. [DOI] [PubMed] [Google Scholar]
- 29.Tolomeo O, Buffett C, Richardson E. 1998. Management of a cluster of cases of invasive group C Neisseria meningitidis infections in the Hamilton-Wentworth region–Ontario. Can Commun Dis Rep 24:122–126. [PubMed] [Google Scholar]
- 30.Almohaya AM, Almutairy TS, Alqahtani A, Binkhamis K, Almajid FM. 2020. Fusobacterium bloodstream infections: a literature review and hospital-based case series. Anaerobe 62:102165. 10.1016/j.anaerobe.2020.102165. [DOI] [PubMed] [Google Scholar]
- 31.Gajdacs M, Urban E. 2020. Relevance of anaerobic bacteremia in adult patients: a never-ending story? Eur J Microbiol Immunol 10.1556/1886.2020.00009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Hawkey PM, Warren RE, Livermore DM, McNulty CAM, Enoch DA, Otter JA, Wilson APR. 2018. Treatment of infections caused by multidrug-resistant Gram-negative bacteria: report of the British Society for Antimicrobial Chemotherapy/Healthcare Infection Society/British Infection Association Joint Working Party. J Antimicrob Chemother 73:iii2–iii78. 10.1093/jac/dky027. [DOI] [PubMed] [Google Scholar]
- 33.Carrara E, Pfeffer I, Zusman O, Leibovici L, Paul M. 2018. Determinants of inappropriate empirical antibiotic treatment: systematic review and meta-analysis. Int J Antimicrob Agents 51:548–553. 10.1016/j.ijantimicag.2017.12.013. [DOI] [PubMed] [Google Scholar]
- 34.Khoshnood S, Heidary M, Mirnejad R, Bahramian A, Sedighi M, Mirzaei H. 2017. Drug-resistant gram-negative uropathogens: a review. Biomed Pharmacother 94:982–994. 10.1016/j.biopha.2017.08.006. [DOI] [PubMed] [Google Scholar]
- 35.LynchJP, III, Zhanel GG, Clark NM. 2017. Emergence of antimicrobial resistance among Pseudomonas aeruginosa: implications for therapy. Semin Respir Crit Care Med 38:326–345. 10.1055/s-0037-1602583. [DOI] [PubMed] [Google Scholar]
- 36.Sah R, Siwakoti S, Baral R, Rajbhandari RS, Khanal B. 2018. Stenotrophomonas maltophilia causing blood stream infection in neonates and infants: a cause for concern. Trop Doct 48:227–229. 10.1177/0049475517743360. [DOI] [PubMed] [Google Scholar]
- 37.Folgori L, Tersigni C, Hsia Y, Kortsalioudaki C, Heath P, Sharland M, Bielicki J. 2018. The relationship between Gram-negative colonization and bloodstream infections in neonates: a systematic review and meta-analysis. Clin Microbiol Infect 24:251–257. 10.1016/j.cmi.2017.08.008. [DOI] [PubMed] [Google Scholar]
- 38.Wong MR, Del Rosso P, Heine L, Volpe V, Lee L, Kornblum J, Lin Y, Layton M, Weiss D. 2010. An outbreak of Klebsiella pneumoniae and Enterobacter aerogenes bacteremia after interventional pain management procedures, New York City, 2008. Reg Anesth Pain Med 35:496–499. 10.1097/AAP.0b013e3181fa1163. [DOI] [PubMed] [Google Scholar]
- 39.Pogue JM, Heil EL, Lephart P, Johnson JK, Mynatt RP, Salimnia H, Claeys KC. 2018. An antibiotic stewardship program blueprint for optimizing Verigene BC-GN within an institution: a tale of two cities. Antimicrob Agents Chemother 62:e02538-17. 10.1128/AAC.02538-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Claeys KC, Heil EL, Pogue JM, Lephart PR, Johnson JK. 2018. The Verigene dilemma: gram-negative polymicrobial bloodstream infections and clinical decision making. Diagn Microbiol Infect Dis 91:144–146. 10.1016/j.diagmicrobio.2018.01.012. [DOI] [PubMed] [Google Scholar]
- 41.Payne M, Champagne S, Lowe C, Leung V, Hinch M, Romney MG. 2018. Evaluation of the FilmArray blood culture identification panel compared to direct MALDI-TOF MS identification for rapid identification of pathogens. J Med Microbiol 67:1253–1256. 10.1099/jmm.0.000802. [DOI] [PubMed] [Google Scholar]
- 42.Schieffer KM, Tan KE, Stamper PD, Somogyi A, Andrea SB, Wakefield T, Romagnoli M, Chapin KC, Wolk DM, Carroll KC. 2014. Multicenter evaluation of the Sepsityper extraction kit and MALDI-TOF MS for direct identification of positive blood culture isolates using the BD BACTEC FX and VersaTREK diagnostic blood culture systems. J Appl Microbiol 116:934–941. 10.1111/jam.12434. [DOI] [PubMed] [Google Scholar]
- 43.Arroyo MA, Denys GA. 2017. Parallel evaluation of the MALDI Sepsityper and Verigene BC-GN assays for rapid identification of Gram-negative bacilli from positive blood cultures. J Clin Microbiol 55:2708–2718. 10.1128/JCM.00692-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Cateau E, Cognee AS, Tran TC, Vallade E, Garcia M, Belaz S, Kauffmann-Lacroix C, Rodier MH. 2012. Impact of yeast-bacteria coinfection on the detection of Candida sp. in an automated blood culture system. Diagn Microbiol Infect Dis 72:328–331. 10.1016/j.diagmicrobio.2011.12.012. [DOI] [PubMed] [Google Scholar]
- 45.Siu GK, Chen JH, Ng TK, Lee RA, Fung KS, To SW, Wong BK, Cheung S, Wong IW, Tam MM, Lee SS, Yam WC. 2015. Performance evaluation of the Verigene Gram-positive and Gram-negative blood culture test for direct identification of bacteria and their resistance determinants from positive blood cultures in Hong Kong. PLoS One 10:e0139728. 10.1371/journal.pone.0139728. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Henig O, Cooper CC, Kaye KS, Lephart P, Salimnia H, Taylor M, Hussain N, Hussain Z, Deeds K, Hayat U, Patel J, Pogue JM. 2019. The hypothetical impact of Accelerate Pheno system on time to effective therapy and time to definitive therapy in an institution with an established antimicrobial stewardship programme currently utilizing rapid genotypic organism/resistance marker identification. J Antimicrob Chemother 74:i32–i39. 10.1093/jac/dky533. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Forrest GN, Mehta S, Weekes E, Lincalis DP, Johnson JK, Venezia RA. 2006. Impact of rapid in situ hybridization testing on coagulase-negative staphylococci positive blood cultures. J Antimicrob Chemother 58:154–158. 10.1093/jac/dkl146. [DOI] [PubMed] [Google Scholar]
- 48.Ambrosioni J, Hernandez-Meneses M, Tellez A, Pericas J, Falces C, Tolosana JM, Vidal B, Almela M, Quintana E, Llopis J, Moreno A, Miro JM, Hospital Clinic Infective Endocarditis Institute . 2017. The changing epidemiology of infective endocarditis in the twenty-first century. Curr Infect Dis Rep 19:21. 10.1007/s11908-017-0574-9. [DOI] [PubMed] [Google Scholar]
- 49.Centers for Disease Control and Prevention. 2021. NHSN organism list (all organisms, common commensals, MBI organisms, and UTI bacteria)–January 2021. Centers for Disease Control and Prevention, Atlanta, GA. https://www.cdc.gov/nhsn/xls/master-organism-com-commensals-lists.xlsx. [Google Scholar]
- 50.Centers for Disease Control and Prevention. 2021. Bloodstream infection (BSI) events: central line-associated bloodstream infection (CLABSI) and non-central line-associated bloodstream infection. Centers for Disease Control and Prevention, Atlanta, GA. https://www.cdc.gov/nhsn/psc/bsi/index.html. Accessed 9 June 2021. [Google Scholar]
- 51.Hall GCKC. 2016. Selected topics in aerobic microbiology, p 467–491. In Hayden RW, Carroll KC, Tang YW (ed), Diagnostic microbiology of the immunocompromised host, 2nd ed. American Society for Microbiology, Washington, DC. [Google Scholar]
- 52.Church D. 2016. Selected topics in anaerobic microbiology, p 493–535. In Hayden RW, Carroll KC, Tang YW (ed), Diagnostic microbiology of the immunocompromised host, 2nd ed. American Society for Microbiology, Washington, DC. [Google Scholar]
- 53.GenMark Diagnostics Inc. 2019. ePlex blood culture identification fungal pathogen (BCID-FP) panel product insert, PI1076–B. GenMark Diagnostics Inc, Carlsbad, CA. [Google Scholar]
- 54.Altun O, Almuhayawi M, Ullberg M, Ozenci V. 2013. Clinical evaluation of the FilmArray blood culture identification panel in identification of bacteria and yeasts from positive blood culture bottles. J Clin Microbiol 51:4130–4136. 10.1128/JCM.01835-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Anonymous. 2014. Class 2 device recall FilmArray BCID panel. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfRES/res.cfm?id=127466. Accessed 22 January 2021.
- 56.Anonymous. 2019. Class 2 device recall FilmArray BCID panel. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfRES/res.cfm?id=171432. Accessed 22 January 2021.
- 57.El-Shaarawi AH, Esterby SR, Dutka BJ. 1981. Bacterial density in water determined by poisson or negative binomial distributions. Appl Environ Microbiol 41:107–116. 10.1128/aem.41.1.107-116.1981. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Evans BA, Amyes SG. 2014. OXA beta-lactamases. Clin Microbiol Rev 27:241–263. 10.1128/CMR.00117-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Doi Y, Iovleva A, Bonomo RA. 2017. The ecology of extended-spectrum beta-lactamases (ESBLs) in the developed world. J Travel Med 24:S44–S51. 10.1093/jtm/taw102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Diekema DJ, Lee K, Raney P, Herwaldt LA, Doern GV, Tenover FC. 2004. Accuracy and appropriateness of antimicrobial susceptibility test reporting for bacteria isolated from blood cultures. J Clin Microbiol 42:2258–2260. 10.1128/JCM.42.5.2258-2260.2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Bookstaver PB, Nimmich EB, SmithTJ, III, Justo JA, Kohn J, Hammer KL, Troficanto C, Albrecht HA, Al-Hasan MN. 2017. Cumulative effect of an antimicrobial stewardship and rapid diagnostic testing bundle on early streamlining of antimicrobial therapy in Gram-negative bloodstream infections. Antimicrob Agents Chemother 61:e00189-17. 10.1128/AAC.00189-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Cruz-Lopez F, Villarreal-Trevino L, Camacho-Ortiz A, Morfin-Otero R, Flores-Trevino S, Garza-Gonzalez E. 2020. Acquired genetic elements that contribute to antimicrobial resistance in frequent Gram-negative causative agents of healthcare-associated infections. Am J Med Sci 360:631–640. 10.1016/j.amjms.2020.06.028. [DOI] [PubMed] [Google Scholar]
- 63.Poirel L, Jayol A, Nordmann P. 2017. Polymyxins: antibacterial activity, susceptibility testing, and resistance mechanisms encoded by plasmids or chromosomes. Clin Microbiol Rev 30:557–596. 10.1128/CMR.00064-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Wang R, van Dorp L, Shaw LP, Bradley P, Wang Q, Wang X, Jin L, Zhang Q, Liu Y, Rieux A, Dorai-Schneiders T, Weinert LA, Iqbal Z, Didelot X, Wang H, Balloux F. 2018. The global distribution and spread of the mobilized colistin resistance gene mcr-1. Nat Commun 9:1179. 10.1038/s41467-018-03205-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
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