Infectious Diseases: Selected Tests - Medical Clinical Policy Bulletins (2023)


Table Of Contents

Applicable CPT / HCPCS / ICD-10 Codes


Scope of Policy

This Clinical Policy Bulletin (CPB) addresses metagenomic next-generation sequencing (mNGS),multiplex immunoassay tests, molecular syndromic panels, and other tests for infectious diseases that do not fit in other CPBs.

  1. Experimental and Investigational

    1. Metagenomic next-generation tests

      Aetna considers the following metagenomic next-generation sequencing (mNGS) tests (not an all-inclusive list) as experimental and investigationalbecause of insufficient evidence in the peer-reviewed literature:

      1. IDbyDNA AlloID, Respiratory Pathogen ID/AMR Panel (RPIP), and Urinary Pathogen ID/AMR Panel (UPIP);
      2. Karius Test (mNGS of microbial cell-free-DNA) (see also,CPB 0650 - Polymerase Chain Reaction Testing: Selected Indications);
      3. Metagenomic next generation sequencing for central nervous system (CNS) infections (e.g., Johns Hopkins Metagenomic Next Generation Sequencing Assay for Infectious Disease Diagnostics);
      4. MicroGenDX qPCR + NGS and MicroGenDX shotgun metagenomics tests.
    2. Muliplex immunoassay tests

      Aetna considers the MeMed BV test as experimental and investigational for the diagnosis or management of infectious disease becausethe effectiveness of this approach has not been established.

  2. Related Policies

    Laboratory-related policies:


    • CPB 0140 - Genetic Testing
    • CPB 0215 - Lyme Disease and other Tick-Borne Diseases
    • CPB 0227 - BRCA Testing, Prophylactic Mastectomy, and Prophylactic Oophorectomy
    • CPB 0249 - Inflammatory Bowel Disease: Serologic Markers and Pharmacogenomic and Metabolic Assessment of Thiopurine Therapy
    • CPB 0319 - RET Proto-Oncogene Testing
    • CPB 0352 - Tumor Markers
    • CPB 0433 - Chlamydia Trachomatis - Screening and Diagnosis
    • CPB 0443 - Cervical Cancer Screening and Diagnosis
    • CPB 0499 - Nonstandard Laboratory Test Panels
    • CPB 0516 - Colorectal Cancer Screening
    • CPB 0561 - Celiac Disease Laboratory Testing
    • CPB 0643 - Diagnosis of Vaginitis
    • CPB 0650 - Polymerase Chain Reaction Testing: Selected Indications
    • CPB 0715 - Pharmacogenetic and Pharmacodynamic Testing
    • CPB 0787 - Comparative Genomic Hybridization (CGH)

CPT Codes / HCPCS Codes / ICD-10 Codes

CodeCode Description

Information in the [brackets] below has been added for clarification purposes.&nbspCodes requiring a 7th character are represented by"+" :

CPT codes not covered for indications listed in the CPB:

Respiratory Pathogen ID/AMR Panel (RPIP) and Urinary Pathogen ID/AMR Panel (UPIP) –no specific code
0112UInfectious agent detection and identification, targeted sequence analysis (16S and 18S rRNA genes) with drug-resistance gene
0152UInfectious disease (bacteria, fungi, parasites, and DNA viruses), microbial cell-free DNA, plasma, untargeted next-generation sequencing, report for significant positive pathogens
0323UInfectious agent detection by nucleic acid (DNA and RNA), central nervous system pathogen, metagenomic next-generation sequencing, cerebrospinal fluid (CSF), identification of pathogenic bacteria, viruses, parasites, or fungi
0351UInfectious disease (bacterial or viral), biochemical assays, tumor necrosis factor- related apoptosis-inducing ligand (TRAIL), interferon gamma-induced protein-10 (IP- 10), and C-reactive protein, serum, algorithm reported as likelihood of bacterial infection


Infectious diseases are caused by microscopic organisms (bacteria, viruses, fungi, and parasites) that penetrate the body’s protective barriers (e.g., skin, mucous membranes) and can result in symptoms ranging from mild to severe, including death.Timely and accurate diagnosis is an essential step in the management of infectious diseases. Although symptoms can indicate a disease, a laboratory test may be necessary to identify the specific microorganism causing the infection so that appropriate treatment can be prescribed.Metagenomic sequencing has been proposed as a laboratory method to diagnose infectious disease.

There are many different laboratory methods available on the market that are used to identify microorganisms.Microbiological approaches, such as culture and gram staining, are traditional methods for diagnosing infectious diseases. However, these approachesare considered labor-intensive and time-consuming. Furthermore, some microorganisms are difficult to culture or identify. Thus, newer approaches have been developed which use DNA sequencing technology to identify microbial agents.

Laboratory testing methods, such as nucleic acid amplification tests (NAATs), are used to extract/purify, amplify (copy) and detect genetic material [deoxyribonucleic acid (DNA) or ribonucleic acid (RNA)] in microorganisms, making the pathogen much easier to identify.The polymerase chain reaction (PCR) is an example of this type of test (Paul et al, 2020; Vazquez-Pertejo, 2020). SeeCPB 0650 - Polymerase Chain Reaction Testing: Selected Indications. Most culture-independent methods, such as PCR tests, require a priori knowledge of microorganisms that are suspected to be present within a clinical sample under investigation in order to detect them (Boers et al, 2019). PCR-based tests have been developed further into multiplex assays which allow for simultaneous detection of several biological agents. However, even multiplex (or panel-based) PCRs "can only identify predefined targets, so one must have suspect organisms or targets in mind in order to detect them" (Wang and Jean, 2021).

First-generation methods for determining the nucleotide sequence of DNA, such as Sanger sequencing, is a low-throughput method used to determine a portion of the nucleotide sequence of an individual's genome. This technique uses PCR amplification of genetic regions of interest followed by sequencing of PCR products (NIH/NCI, 2022).Sanger sequencing can be difficult to interpret when performed on complex or polymicrobial samples (Wang and Jean, 2021).

Next-generation sequencing (NGS)-based tests have emerged and present the "possibility of an agnostic diagnostic method capable of comprehensive detection of multiple pathogens simultaneously and directly from a patient sample" (Wang and Jean, 2021).Next-generation sequencing is a high-throughput, massively parallel, culture-free sequencing method that tests for an array of potential pathogens within a microbial sample simultaneously in a single sequencing run in order to determine a cause of disease(Boers et al, 2019).An advantage of NGS compared with PCR is that prior knowledge of the target organism(s), and thus target-specific primers, is not required.Major applications of NGS in clinical microbiology laboratories include: targeted NGS (tNGS), whole genome sequencing, and metagenomic NGS (mNGS) (Want and Jean, 2021).

Metagenomic Next-Generation Sequencing Tests

Metagenomic next-generation sequencing tests (mNGS),sometimes called "shotgun" sequencing, is an unbiased hypothesis-free diagnostic approach to the detection of pathogens. mNGS allows for thousands to billions of DNA fragments to be simultaneously and independently sequenced from a clinical sample which may containmixed populations of microorganisms, and assigning these to their reference genomes to understand which microbes are present and in what proportions (Gu et al, 2019; Lee, 2019). "Clinical tests have been developed to detect the nucleic acids of microbes from various specimen types such as blood, joint fluid, and cerebrospinal fluid (CSF) to aid the diagnosis of various infections. A significant limitation of mNGS is that most of the nucleic acids in clinical samples are from the host, so the host genome dominates sequence reads. This can result in decreased analytical sensitivity for detection of pathogens present at relatively low burden" (Wang and Jean, 2019). Despite the potential of mNGS, major reservations "include the interpretation of findings (distinguishing contamination and colonization from true pathogens), selection and validation of databases used for analyses, and prediction (or lack thereof) of antimicrobial susceptibilities. A common perception is that mNGS is so incredibly sensitive that it will reveal a diagnosis when all other testing is negative. While mNGS may be analytically more sensitive than standard culturing methods in some cases, the necessary removal of vast amounts of human nucleic acid during sequencing preparation and (by computational methods) during the post-analytic process, can decrease the sensitivity in comparison to targeted PCR approaches for many organisms". Furthermore, "contamination of samples during specimen collection is a large concern given the increased analytical sensitivity of mNGS in comparison to standard culture methods, and there needs to be a validated quality-control process in place for steps from assessing reagent purity to measuring adequate genome coverage controls" (Lee, 2019).

IDbyDNA Tests

IDbyDNA, Inc. offers "syndromic Precision Metagenomics testing applications" to help identify various infectious disease indications. IDbyDNA utilizes the Explify software platform which integrates artificial intelligence (AI), knowledge from global experts, and proprietary reagents to assist laboratory professionals on obtaining rapid actionable infectious disease insights.The Explify software platform aims to provide ultra-rapid DNA search technology, AI-powered data interpretation, curated collections of millions of DNA sequences, comprehensive genotype-phenotype databases for antimicrobial resistance (AMR) prediction, and user-friendly software interfaces.

The Respiratory Pathogen ID/AMR Panel (RPIP) uses precision RNA and DNA sequencing, RPIP enrichment probes and automated Explify RPIP data analysis to deliver sensitive detection and quantification of over 280 respiratory pathogens causing pneumonia, and AMR information for 60 antibiotics and antivirals. RPIP can potentially identify over 180 bacteria including 13 mycobacteria and other slow growing pathogens, 50+ fungi, and 40+ viruses including full genome characterization of SARS-CoV-2 and influenza A/B viruses, and antibiotic and antiviral resistance information based on more than 2,000 genomic markers.

The Urinary Pathogen ID/AMR Panel (UPIP) uses mNGS to detect and quantify over 170 common, less commonchallenging-to-grow, and frequently missed uropathogens which can lead to recurrent or difficult to manage urinary tract infections. The comprehensive panel identifies more than 120 bacteria, 35 viruses, 14 fungi, 4 parasites, and antibiotic resistance information for 46 antibiotics based on more than 3,500 resistance markers.

(Video) Bacterial Meningitis (CNS Infection) – Infectious Diseases | Lecturio

The AlloID, by CareDX and powered by IDbyDNA's Explify software platform, aims to provideplasma-based precision metagenomics detection and quantification of viruses, bacteria, fungi, and parasites that are of particular concern for causing infections in patients with transplants. AlloID will also deliver genotyping information for transplant viruses, antiviral resistance profiling, and detection of multidrug-resistant bacteria.

Gaston et al (2022) state thatNGS approaches hold the possibility of consolidating some or all diagnostic approaches for pathogen identification and characterization into a single assay. The authorsevaluated the performance of the Respiratory Pathogen ID/AMR (RPIP) kit (Illumina, Inc.) with automated Explify bioinformatic analysis (IDbyDNA, Inc.), a targeted NGS workflow enriching specific pathogen sequences and antimicrobial resistance (AMR) markers, and a complementary untargeted metagenomic workflow with in-house bioinformatic analysis. Compared to a composite clinical standard consisting of provider-ordered microbiology testing, chart review, and orthogonal testing, both workflows demonstrated similar performances. The overall agreement for the RPIP targeted workflow was 65.6% (95% confidence interval, 59.2 to 71.5%), with a positive percent agreement (PPA) of 45.9% (36.8 to 55.2%) and a negative percent agreement (NPA) of 85.7% (78.1 to 91.5%). The overall accuracy for the metagenomic workflow was 67.1% (60.9 to 72.9%), with a PPA of 56.6% (47.3 to 65.5%) and an NPA of 77.2% (68.9 to 84.1%). The approaches revealed pathogens undetected by provider-ordered testing (Ureaplasma parvum, Tropheryma whipplei, severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2], rhinovirus, and cytomegalovirus [CMV]), although not all pathogens detected by provider-ordered testing were identified by the NGS workflows. The RPIP targeted workflow required more time and reagents for library preparation but streamlined bioinformatic analysis, whereas the metagenomic assay was less demanding technically but required complex bioinformatic analysis. The results from both workflows were interpreted utilizing standardized criteria, which is necessary to avoid reporting nonpathogenic organisms. The RPIP targeted workflow identified AMR markers associated with phenotypic resistance in some bacteria but incorrectly identified blaOXA genes in Pseudomonas aeruginosa as being associated with carbapenem resistance. The authors concluded that these workflows could serve as adjunctive testing with, but not as a replacement for, standard microbiology techniques. The authors note multiple limitations in this study. First, the patients in the study represented a heavily pretreated population, with many treated with antimicrobial agents in the 14 days prior to the acquisition of the BAL fluid specimens. It is possible that false-positive bacterial and fungal results represented organisms that could not be recovered in culture due to antimicrobial use. Second, BAL fluid was not collected using protected techniques, potentially allowing contamination of samples by oropharyngeal flora. Third, the same provider-ordered tests were not applied to all samples. Although this is representative of provider ordering practices, all pathogens may not have been identified if missed by NGS workflows and the lack of standardized testing. Finally, reporting of BAL fluid cultures is standardized in the Johns Hopkins clinical microbiology laboratory, but variability by technologists in the extent of workup for small quantities of bacterial isolates may have occurred.

Karius Test

The Karius Test for infectious disease uses next-generation sequencing (NGS) to detect microbial cell free DNA (cfDNA) in plasma from bacteria, DNA viruses, fungi and protozoa. Microbial cfDNA may be found in plasma when viable microorganisms are not detected in blood by other methods. The reported microorganism(s) may or may not bethe cause of patient infection. Results should be interpreted within the context of clinical data, including medical history, physical findings, epidemiological factors, and other laboratory data (Karius, 2020). "The Karius Test can detect more than 1,000 bacteria, fungi, parasites, and select DNA viruses. Detected microorganisms are reported quantitatively as DNA molecules per microliter of plasma (MPM), and are compared to reference MPM ranges established in healthy, asymptomatic individuals" (Wang and Jean, 2019).

Camargo et al (2019) state that cell-free DNA (cfDNA) sequencing technology in diagnostic evaluation of infections in immunocompromised hosts is limited. The authors conducted an exploratory study using next-generation sequencing (NGS) for detection of microbial cfDNA in a cohort of 10 immunocompromised patients with febrile neutropenia, pneumonia or intra-abdominal infection. Pathogen identification by cfDNA NGS demonstrated positive agreement with conventional diagnostic laboratory methods in 7 (70%) cases, including patients with proven/probable invasive aspergillosis,Pneumocystis jiroveciipneumonia,Stenotrophomonas maltophiliabacteremia, Cytomegalovirus and Adenovirus viremia. NGS results were discordant in 3 (30%) cases including two patients with culture negative sepsis who had undergone hematopoietic stem cell transplant in whom cfDNA testing identified the etiological agent of sepsis; and one kidney transplant recipient with invasive aspergillosis who had received > 6 months of antifungal therapy prior to NGS testing. The authors concluded that these observations support the clinical utility of measurement of microbial cfDNA sequencing from peripheral blood for rapid noninvasive diagnosis of infections in immunocompromised hosts; however, larger studies are needed.

Hogan et al (2021) state that mNGS ofplasma cell-free DNA has emerged as an attractive diagnostic modality allowing broad-range pathogen detection, noninvasive sampling, and earlier diagnosis. However, little is known about its real-world clinical impact as used in routine practice. The authorsperformed a retrospective cohort study of all patients for whom plasma mNGS (Karius test) was performed for all indications at 5 United States institutions over 1.5 years. Comprehensive records review was performed, and standardized assessment of clinical impact of the mNGS based on the treating team's interpretation of Karius results and patient management was established.A total of 82 Karius tests were evaluated from 39 (47.6%) adults and 43 (52.4%) children and a total of 53 (64.6%) immunocompromised patients. The authors found that Karius positivity rate was 50 of 82 (61.0%), with 25 (50.0%) showing 2 or more organisms (range, 2-8). The Karius test results led to positive impact in 6 (7.3%), negative impact in 3 (3.7%), and no impact in 71 (86.6%), and was indeterminate in 2 (2.4%). Cases with positive Karius result and clinical impact involved bacteria and/or fungi but not DNA viruses or parasites. In 10 patients who underwent 16 additional repeated tests, only 1 was associated with clinical impact. The authors concluded that thereal-world impact of the Karius test as currently used in routine clinical practice is limited. Further studies are needed to identify high-yield patient populations, define the complementary role of mNGS to conventional microbiological methods, and discern how best to integrate mNGS into current testing algorithms.

Shishido et al (2022) state that metagenomic next-generation sequencing of microbial cell-free DNA (mcfDNA)allows for non-invasive pathogen detection from plasma. However, there is little data describing the optimal role for this assay in real-world clinical decision making. The authors performeda single-center retrospective cohort study of 80 adult patients for whom a mcfDNA (Karius©) test was sent between May 2019 and February 2021.The most common reason for sending the assay was unknown microbiologic diagnosis (78%), followed by avoiding invasive procedures (14%). Categorical variables were reported using frequency and percentages. Mean ± standard deviation of age was reported and days of hospitalization reported using median and quartiles. Comparative analysis of categorical variables was conducted by the Fisher’s exact test or Chi-square test as appropriate, and days of hospitalization was compared using Mann–Whitney U test. Statistical tests were performed using SPSS (IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp) with p-values ≤ 0.05 as the significance threshold. The test had a positive impact in 34 (43%), a negative impact in 2 (3%), and uncertain or no impact in 44 (55%). A positive impact was observed in solid organ transplant recipients (SOTR, 71.4%, p = 0.003), sepsis (71.4%, p = 0.003), and those receiving antimicrobial agents for less than 7 days prior to mcfDNA testing (i.e., 61.8%, p = 0.004). Positive impact was driven primarily by de-escalation of antimicrobial therapy. The authors concluded that clinicalimpact of mcfDNA testing was highest in SOTR, patients with sepsis and patients who had been on antimicrobial therapy for less than 7 days. Positive impact was driven by de-escalation of antimicrobial therapy which may highlight a potential role for mcfDNA in the realm of stewardship. The authors note that asa descriptive retrospective study, the data were uncontrolled with a heterogenous patient population and lacked standard comparison to conventional testing. They wereunable to incorporate patient outcomes (i.e., mortality) into the final analysis. Additionally, the retrospective nature of the study does not allow for control of the timing of testing and patient and disease characteristics, therefore there was considerable variability among factors and many underrepresented patient populations and diseases.Additionally, the small sample size and retrospective nature allow for only hypothesis-generating conclusions to be made.

Metagenomic Next-Generation Sequencing for CNS Infections

Zhang et al (2020) state thatdiagnostic value of metagenomic next-generation sequencing (mNGS), an emerging powerful platform, remains to be studied in CNS infections. The authorsconducted a single-center prospective cohort study to compare mNGS with conventional methods including culture, smear and etc. The study included 248 suspected CNS infectious patients. The authors found thatmNGS reported a 90% (9/10) sensitivity in culture-positive patients without empirical treatment and 66.67% (6/9) in empirically-treated patients. Detected an extra of 48 bacteria and fungi in culture-negative patients, mNGS provided a higher detection rate compared to culture in patients with (34.45% vs. 7.56%, McNemar test, p < 0.0083) or without empirical therapy (50.00% vs. 25.00%, McNemar test, p > 0.0083). Compared to conventional methods, positive percent agreement and negative percent agreement was 75.00% and 69.11% separately. The authors found that mNGS detection rate was significantly higher in patients with cerebrospinal fluid (CSF) WBC > 300 * 106/L, CSF protein greater than 500 mg/L or glucose ratio less than or equal to 0.3. mNGS sequencing read is correlated with CSF WBC, glucose ratio levels and clinical disease progression. The authors concluded thatmNGS showed a satisfying diagnostic performance in CNS infections and had an overall superior detection rate to culture. mNGS may hold diagnostic advantages especially in empirically treated patients. CSF laboratory results were statistically relevant to mNGS detection rate, and mNGS could dynamically monitor disease progression. The authors noted limitations such as their study had a relatively small sample size of viral, fungal and parasitic CNS infections and therefore could not yet come to conclusions about the diagnostic value of mNGS in these groups. Second, they used preliminary data for analysis but still lack of health control simultaneously. Also, a bactec microbial detection system for CSF culture and novel optimized laboratory and statistical methods for mNGS could be applied to raise positivity. What’s more, RNA library preparations were conducted in a limited number of patients, which might neglect some neuroinvasive RNA viruses. Further, as the mNGS results may be easily influenced by many factors, the standards in their single center cross-section study should be thoroughly modified and tested before applying to other centers.

Xing et al (2020) conducted aprospective multicenter to assess theperformance of metagenomic next-generation sequencing (mNGS) in the diagnosis of infectious encephalitis and meningitis.Cerebrospinal fluid samples from patients with viral encephalitis and/or meningitis, tuberculous meningitis, bacterial meningitis, fungal meningitis, and non-central nervous system (CNS) infections were subjected to mNGS. The study included a total of 213 patients with infectious and non-infectious CNS diseases. The authors found that the mNGS-positive detection rate of definite CNS infections was 57.0%. At a species-specific read number (SSRN) greater than or equal to 2, mNGS performance in the diagnosis of definite viral encephalitis and/or meningitis was optimal (area under the curve [AUC] = 0.659, 95% confidence interval [CI] = 0.566-0.751); the positivity rate was 42.6%. At a genus-specific read number greater than or equal to 1, mNGS performance in the diagnosis of tuberculous meningitis (definite or probable) was optimal (AUC=0.619, 95% CI=0.516-0.721); the positivity rate was 27.3%. At SSRNs greater than or equal to 5 or 10, the diagnostic performance was optimal for definite bacterial meningitis (AUC=0.846, 95% CI = 0.711-0.981); the sensitivity was 73.3%. The sensitivities of mNGS (at SSRN greater than or equal to 2) in the diagnosis of cryptococcal meningitis and cerebral aspergillosis were 76.92 and 80%, respectively. The authors concluded thatmNGS of cerebrospinal fluid effectively identifies pathogens causing infectious CNS diseases. mNGS should be used in conjunction with conventional microbiological testing.However, the authors noted several limitations in this study. Firstly, RNA-Seq data were not tested in parallel with DNA sequencing, which might provide valuable complementary information. Furthermore, because DNA extraction efficiency is critical in terms of mNGS results, a comparison of the extraction efficiencies of the various kits must be performed in future studies. Finally, the sample size was relatively small, especially after stratification of patients according to the types of infections. The authors state that the new technology exhibits great potential; however, careful attention is needed with respect to DNA and RNA co-extraction methods, extraction efficiency, differentiation of colonization from infection, and method standardization.

Zhu et al (2022) state that it is not well-understood whether mNGS has comparable sensitivity to target-dependent nucleic acid test for pathogen identification. The authors evaluated31 patients with chickenpox and neurological symptoms for screening of possible varicella-zoster virus (VZV) central nervous system (CNS) infection in a single-center hospital in China. Microbiological diagnosing of VZV cerebrospinal fluid (CSF) infection was performed on stored CSF samples using mNGS, quantitative and qualitative VZV-specific PCR assays, and VZV IgM antibodies test. The authors found that about 80.6% of the patients had normal CSF white blood cell counts (≤ 5 × 106/L). VZV IgM antibodies presented in 16.1% of the CSF samples, and nucleic acids were detectable in 16.1 and 9.7% using two different VZV-specific real-time PCR protocols. Intriguingly, maximal identification of VZV elements was achieved by CSF mNGS (p = 0.001 and p = 007; compared with qualitative PCR and VZV IgM antibody test, respectively), with sequence reads of VZV being reported in 51.6% (16/31) of the CSF samples. All VZV PCR positive samples were positive when analyzed by mNGS. Of note, human betaherpesvirus 6A with clinical significance was unexpectedly detected in one CSF sample. The authors concluded that their study suggests that CSF mNGS may have higher sensitivity for VZV detection than CSF VZV PCR and antibody tests, and has the advantage of identifying unexpected pathogens. However, although the sensitivity of mNGS can be further increased by technical innovation, the specificity will continue to be a great concern. Clinical judgement of the treating physicians is very important for interpretating the results.

(Video) Infective Endocarditis

MicroGenDX Tests

MicroGenDX offers qPCR+NGS testing on a wide selection of specimen types for identifyingmycobacteria, fungi, anaerobes, and microbes that can result in infectious disease.MicroGenDX combines multiplex quantitative (qPCR) with targeted 16S/ITS NGS and matches samplesagainst a curated database of over 50,000 microbial species. MicroGenDX’s qPCR+NGS results identify all potentially pathogenic microbial taxa, along with their clinically relevant distribution in the sample, in 3.5 days.MicroGenDX is also able to provide shotgun metagenomics.

McDonald and colleagues (2017) conducted aprospective, randomized, open-label, controlled, head-to-head comparative phase II study of standard urine culture and sensitivity (C&S) versus DNA NGS testing (MicroGenDX test) for the diagnosis and treatment efficacy in patients with symptoms of acute cystitis based on short-term outcomes. A total of 44 patients and 22 control subjects completed the study, over 14 days. Patients were randomized to receive treatment based on culture results (Arm A, n=22) or treatment based on DNA NGS test results (Arm B, n=22).In total, 13 of 44 patients (30%) had positive urine culture results whereas 44 of 44 patients had positive DNA NGS results.Of the 22 control subjects, 5 had positive urine culture results and 21 of 22 had positive DNA NGS results. The five subjects with positive C&S and DNA NGS findings all had similar organisms. However, in three of these subjects, DNA NGS results reported two or more organisms in addition to the common one. On a head-to-head comparison, symptom scores were significantly better for those patients whose treatment was based on DNA NGS versus traditional C&S. Patients treated in Arm A, Subset 2, (culture-negative, DNA NGS-positive) improved with respect to symptom scores when they started treatment on day 8. The authors concluded that DNA NGS may help when diagnosing and treating symptoms of acute cystitis, especially when urine culture results are negative. However, the authors note that a significant limitation of the study is the small sample size, with low statistical power of the results. A larger study needs to be done with a bigger sample size to achieve more robust conclusions of this promising study.

Tarabichi and colleagues (2018) conducted aprospective, single-blinded studyto assess the use of NGS (MicroGenDX) for detecting organisms in synovial fluid. Eighty-sixanonymized samples of synovial fluid were obtained from patients undergoing aspiration of the hip or knee as part of the investigation of a periprosthetic infection. A panel of synovial fluid tests, including levels of C-reactive protein, human neutrophil elastase, total neutrophil count, alpha-defensin, and culture were performed prior to next-generation sequencing.Of these 86 samples, 30 were alpha-defensin-positive and culture-positive (Group I), 24 were alpha-defensin-positive and culture-negative (Group II) and 32 were alpha-defensin-negative and culture-negative (Group III). The authors found that among the 30 culture-positive samples in Group I, NGS detected at least 1 organism in 26 of the samples. In 25 (96%) of these, there was concordance between the bacteria detected in culture and the predominant organism detected by NGS sequencing.In another four samples with relatively low levels of inflammatory biomarkers, culture was positive but NGS was negative. A total of ten samples had a positive NGS result and a negative culture. In five of these, alpha-defensin was positive and the levels of inflammatory markers were high. In the other five, alpha-defensin was negative and the levels of inflammatory markers were low. While NGS detected several organisms in each sample, in most samples with a higher probability of infection, there was a predominant organism present, while in those presumed not to be infected, many organisms were identified with no predominant organism. The authors concluded that pathogenscausing periprosthetic infection in both culture-positive and culture-negative samples of synovial fluid could be identified by NGS. The authors note that a limitation to the study may be the lack ofclinical information about these patients as the sampleswere retrieved from an anonymized reservoir andtherefore not able to determine whether the patients wereinfected or not. The study did not evaluate another molecular technique in parallel with NGS. The authorsonly testedsynovial fluid, and hence a direct comparison to tissue orother samples could not be made. The authors state that theirfindings suggest that NGS holds great promise for the detection of potential pathogensfrom the synovial fluid of patients with a periprostheticinfection.

Haider et al (2019) state that high-throughputDNA sequencing of the paranasal sinus microbiome has potential in the diagnosis and treatment of sinusitis. The author conducted a case series chart review at a single tertiary care academic medical center toevaluate the use of high-throughput DNA sequencing to diagnose sinusitis of odontogenic origin.A chart review was performed of DNA sequencing results from the sinus aspirates obtained under endoscopic visualization in 142 patients with sinusitis. The identification of any potentially pathogenic bacteria associated with oral flora in a sample was classified as a positive result for sinusitis of odontogenic etiology. The sensitivity, specificity, and predictive values of using high-throughput DNA sequencing to diagnose sinusitis of odontogenic etiology were determined, with the patient's computed tomography sinus scan as the reference standard. On computed tomography scans, an odontogenic source was determined by the presence of a periapical lucency perforating the schneiderian membrane. The authors found that 7of the 142 patients enrolled in this study had an odontogenic source based on computed tomography scans. Relative to this reference standard, high-throughput DNA sequencing produced a sensitivity of 85.7% (95% CI, 42.1%-99.6%), a specificity of 81.5% (95% CI, 73.9%-87.6%), a positive predictive value of 19.4% (95% CI, 13.1%-27.7%), and a negative predictive value of 99.1% (95% CI, 94.7%-99.9%). The authors concluded that thisstudy supports the use of high-throughput DNA sequencing in supplementing other methods of investigation for identifying an odontogenic etiology of sinusitis.The authors noted that there are several limitations noted in the current study.First, CT evidence of an odontogenic source was used asthe diagnostic standard for determining odontogenic sinusitis. This may underestimate the number of cases of odontogenic sinusitis, since not all cases will manifest withapparent radiographic findings.This in turn might skew sensitivity and specificity calculations.Second, the retrospective design of thisstudy limits the use of other criteria for the confirmatorydiagnosis of odontogenic sinusitis.A prospective study addressing this question might better combine physical examinationand dental consultation, with CT images, in establishing agold standard for the diagnosis of odontogenic sinusitis. The authors state that despite these limitations, high-throughput DNA sequencingof sinus cavity purulence may offer a sensitive test for identifying patients who would benefit from additional investigation for odontogenic sinusitis, and that the results of this study lay the groundwork for future studies that examinethe use of high-throughput DNA sequencing in the diagnosisand treatment of sinusitis.

Dixon and colleagues (2020) conducted anextensive review and analysis of the available literature on the topic of metagenomic sequencing in urological science.The search yielded a total of 406 results, and manual selection of appropriate papers was subsequently performed. Only one randomised clinical trial comparing metagenomic sequencing to standard culture and sensitivity in the arena of urinary tract infection was found. The authors concluded that their paperexplores the limitations of traditional methods of culture and sensitivity and delves into the recent studies involving new high-throughput genomic technologies in urological basic and clinical research, demonstrating the advances made in the urinary microbiome in its entire spectrum of pathogens and the first attempts of clinical implementation in several areas of urology. Finally, this paper discusses the challenges that must be overcome for such technology to become widely used in clinical practice. The authors state that "althoughNGS is at an early stage of its development,its ability to quickly detect and identify the entire spectrumof microbes present within a sample with accuracy, andits capacity to predict phenotypic resistance patterns viagenomic data proves its superiority to the slower, traditionalmethods of culture and sensitivity. However, at this point intime, there are still limitations in precisely defining leadingpathogen(s) which can contribute to the development of UTIand sufficiently distinguish them from other contaminatingor commensal strains. The implementation of NGS in clinical laboratories will certainly demand a great deal of carefulthought to ensure patient confidentiality while simultaneously storing data in a manner which will optimize publicbenefit".

Goswami et al (2022) state that NGS technology, including 16S rRNA gene bacterial profiling, is an emerging diagnostic modality that is showing promise for detecting and precisely identifying a wide spectrum of microbial DNA present within a clinical sample. The authors conducted a prospective multicenter study to investigate the application of NGS pathogen detection to nonunion fracture. Samples were collected from 54 patients undergoing open surgical intervention for preexisting long-bone nonunion (n = 37) and control patients undergoing fixation of an acute fracture (n = 17). Intraoperative specimens were sent for dual culture and 16S rRNA gene-based microbial profiling using MicroGenDX NGS test.DNA was extracted and amplified via a PCR using forward and reverse primers flanking the 16S ribosomal rRNA gene. The amplified DNA was then sequenced on an Illumina MiSeq platform. Sequence reads were then denoised to remove short sequences and clustered into operational taxonomic units (OTUs). OTUs were then assigned taxonomy using a MicroGenDX curated taxonomic reference database.For clinical scoring of positive and negative samples by 16S rRNA gene sequencing, samples were required to pass amplification and pass an internally validated reporting threshold of at least more than 1000 classifiable microbial reads after quality control. This positive/negative score was used for evaluating utility against nonunion groups. In the study, patients were followed for a minimum of 6 months using comparative analyses aimed to determine whether microbial NGS diagnostics could discriminate between nounions that healed during follow-up versus persistent nonunion. Among the 37 patients undergoing open surgical intervention, 22 had achieved union by 6 months and 15 were considered to have persistent nonunions. Of those that had persistent nonunion, 10 patients (67%) had a positiveNGS test and 5 (33%) had a negative test.The authors concluded that positive NGS detection was significantly correlated withpersistent nonunion (p = 0.048), andsuggest that the fracture-associated microbiome may be a significant risk factor for persistent nonunion. The authors note limitations to their study. The study included a small sample size and did not use other molecular techniques in parallel with NGS, thus no direct comparisons can be made between NGS and other molecular techniques studied. Furthermore, the study did notcorrelate the observed NGS signal with subsequent clinical outcomes and determine whether these pathogens indeed escape detection by culture and are implicated in subsequent failure to reach union. Thus, future work is needed to establish the optimal sampling methodology and bioinformatic quality control for reporting of clinical NGS data.

Multiplex Immunoassay Tests

Generally, the multiplex immunoassay platform applies technology to simultaneously measure multiple target analytes in a single biological sample. There are various types of multiplex immunoassays in use, or being developed, for identifying bacterial antigens in infectious disease.

MeMed BV

The MeMed BV (MeMed Diagnostics, Ltd.) is a blood test, used in conjunction with the MeMed Key analyzer, that simultaneously evaluates three independent immunoassays and quantitatively measures three host biomarkers (C-reactive protein [CRP], interferon γ-induced protein 10 [IP-10], and TNF-related apoptosis-inducing ligand [TRAIL]) to produce a host response score (ranging from 0 to 100) for differentiating between bacterial and viral infection.

In order to increase diagnostic accuracy and achieve better treatment guidance of infectious disease, Oved and colleagues (2015)developed and validated a promising signature that combines novel and traditional host-proteins for differentiating between bacterial and viral infections. The authors state that bacterial-induced host proteins such as procalcitonin, C-reactive protein (CRP), and Interleukin-6, are routinely used to support diagnosis of infection. However, their performance is negatively affected by inter-patient variability, including time from symptom onset, clinical syndrome, and pathogen species. The authors initiallyconducted a "bioinformatic screen to identify putative circulating host immune response proteins. The resulting 600 candidates were then quantitatively screened for diagnostic potential using blood samples from 1002 prospectively recruited patients with suspected acute infectious disease and controls with no apparent infection. For each patient, three independent physicians assigned a diagnosis based on comprehensive clinical and laboratory investigation including PCR for 21 pathogens yielding 319 bacterial, 334 viral, 112 control and 98 indeterminate diagnoses; 139 patients were excluded based on predetermined criteria". The authors report that the best performing host-protein was TNF-related apoptosis-inducing ligand (TRAIL) (area under the curve [AUC] of 0.89; 95% confidence interval [CI], 0.86 to 0.91), which was consistently up-regulated in viral infected patients. The authors "further developed a multi-protein signature using logistic-regression on half of the patients and validated it on the remaining half. The signature with the highest precision included both viral- and bacterial-induced proteins: TRAIL, Interferon gamma-induced protein-10, and CRP (AUC of 0.94; 95% CI, 0.92 to 0.96)". The authors report that the signature was superior to any of the individual proteins (p<0.001), as well as routinely used clinical parameters and their combinations (p<0.001). The authors state that it remained robust across different physiological systems, times from symptom onset, and pathogens (AUCs 0.87-1.0). The authors conclude that "accurate differential diagnosis provided by this novel combination of viral- and bacterial-induced proteins has the potential to improve management of patients with acute infections and reduce antibiotic misuse". The authors do note a potential limitation of the study is theheterogeneity of the patient cohort (multiple clinical syndromes, pathogen species, and time from onset of symptoms). The diverse cohortmakes it more challenging to control for confounding factors. Although they did not identify significant confounders, follow-up studies on homogenous subgroups are warranted. In addition, follow-up time course studies that assess whether the signature can predict response to treatment and patient prognosis are also warranted.

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Eden et al (2016)describe a sub-study (of the Curiosity study) whichevaluated the diagnostic accuracy of a novel host-biomarker assay (TRAIL, IP-10, and CRP) for discriminating bacterial and viral etiologies in a sub-population of the emergency department (ED). The referencestandard was based on microbiological confirmation plusadjudication by an expert panel after review of all participant clinical, laboratory, radiological, microbiological andfollow-up data.A “true diagnosis” requiredpositive microbiological confirmation plus a unanimousexpert panel, i.e., all three panel members independentlyassigned bacterial or viral etiology.The expert panel was blinded tothe test result and test performers were blinded to thereference standard.Of the 744 participants that met the Curiosity studyinfectious disease inclusion criteria, 428 participants withsuspected infections were recruited at the ED, of which 155had a confirmed etiology (128 viral and 27 bacterial [ormixed co-infection]). The authors state thatthe combinatorial signature of all three biomarkers exhibited the greatest diagnostic accuracy,yielding a sensitivity of 96% [95% confidence interval: 78%,100%] and specificity of 93% [87%, 97%], significantly betterthan the individual proteins. Furthermore,the signature outperformed routine lab parameters suchas white blood cell count (sensitivity 56% [35%, 75%] andspecificity 84% [77%, 90%]; cut-off 15,000 cells/ml) and absolute neutrophil count (sensitivity 59% [39%, 78%] andspecificity 88% [81%, 93%]; cut-off 10,000 cells/ml). The authors concluded thatthe diagnostic performance data support that a host-biomarker signaturecomprising TRAIL, IP-10 and CRP represents a promisingnew tool for aiding ED clinicians in determining the bacterial versus viral etiology of infectious disease; however, future clinical studiesare required to examine the usefulness of this host biomarker signature in safely decreasing unnecessaryantibiotic prescription at the ED.

ImmunoXpert (MeMed) is a novel assay combining three proteins: TRAIL, IP-10, and CRP. Investigators, van Houten et al (2017), conducted aprospective, double-blind, international, multicenter study (OPPORTUNITY) to externally validate the diagnostic accuracy of this novel assay in differentiating between bacterial and viral infections and to compare this test with commonly used biomarkers in childrenwith lower respiratory tract infection or clinical presentation of fever without source. The investigators recruited777 children, of whom 577 were assessed. The investigators identified majority diagnosis when two of three panel members agreed on a diagnosis and unanimous diagnosis when all three panel members agreed on the diagnosis. The investigators calculated the diagnostic performance (i.e., sensitivity, specificity, positive predictive value, and negative predictive value) of the index test in differentiating between bacterial (index test positive) and viral (index test negative) infection by comparing the test classification with the reference standard outcome. The investigators found that the majority of the panel diagnosed 71 cases as bacterial infections and 435 as viral infections. In another 71 patients there was an inconclusive panel diagnosis. The assay distinguished bacterial from viral infections with a sensitivity of 86.7%, a specificity of 91.1%, a positive predictive value of 60.5%, and a negative predictive value of 97.8%. In the more clear cases with unanimous panel diagnosis (n=354), sensitivity was 87.8%, specificity 93.0%, positive predictive value 62.1%, and negative predictive value 98.3%. The investigators concluded that thisdiagnostic assay based on CRP, TRAIL, and IP-10 has the potential to reduce antibiotic misuse in young children.

A commentary by Esposito and Principi (2017) on the OPPORTUNITY study (van Houten et al, 2017)notes some study limitations that preclude its routine use in clinical practice, such as: the test requires advanced laboratory techniques not used outside a hospital setting, collected data was obtained from small sample size for which none had an underlying disease that might modify host response to infection, the definition of cause of infection used in studies that have tried to differentiate bacterial and viral infection varies, and respiratory infections are frequently classified on the basis of clinical and radiological findings and results of a microbiological assessment of nasopharyngeal swabs. The authors state that "it is well known that the investigation into upper respiratory secretions in children can be confounding and lead to the erroneous classification of a lower respiratory disease, and that bacteria and viruses can simply be carried and could have no association with the cause of a disease". The authors conclude that future studies using a larger study population with various characteristics are needed to confirm the results of host protein-based assays.

Srugo et al (2017)performed a double-blind, multicenter evaluation of anovel assay that integrates host-proteins (TRAIL, IP-10, and CRP) for differentiation between bacterial and viral disease in febrile children. Thecohort included 361 pediatric patients, with 239 viral, 68 bacterial, and 54 indeterminate reference standard diagnoses. The reference standard diagnosis was based on predetermined criteria plus adjudication by experts blinded to assay results. Assay performers were blinded to the reference standard. Assay cutoffs were predefined. The authors found that the assay distinguished between bacterial and viral patients with 93.8% sensitivity (95% confidence interval: 87.8%-99.8%) and 89.8% specificity (85.6%-94.0%); 11.7% had an equivocal assay outcome. The assay outperformed CRP (cutoff 40 mg/L; sensitivity 88.2% [80.4%-96.1%], specificity 73.2% [67.6%-78.9%]) and procalcitonin testing (cutoff 0.5 ng/mL; sensitivity 63.1% [51.0%-75.1%], specificity 82.3% [77.1%-87.5%]). The authors concluded that their evaluationconfirmed high assay performance in febrile children, and that the assay was significantly more accurate than CRP, procalcitonin, and routine laboratory parameters. However, additional studies are warranted to support its potential to improve antimicrobial treatment decisions.

A commentary by Kimberlin and Poole (2017) review the study conducted by Srugo et al (2017) on thenovel assay that integrates host-proteins (TRAIL, IP-10, and CRP) for differentiation between bacterial and viral disease in febrile children (ImmunoXpert assay, MeMed Diagnostics, Ltd.). The authors point out that a number of confirmatory investigations are required, as the published studies that have assessed the ImmunoXpert assay have used specimens that were frozen at -80 degrees Celsius; thus, prospective trial designs to determine the performance characteristics of the test in a "more real-world manner", which includes use of refrigerated specimens, are warranted. Furthermore, the authors agree that the assay needs to also be assessed in the population of infants less than 3 months of age, as well as immunocompromised children, which has a need for improved diagnostics to drive decision-making in an evidence-based fashion and can reliably distinguish bacterial from viral infections. The authors state that "If the assay is validated in these future studies, performance of randomized trial designs that assess how knowledge of the assay result impacts clinical care should be considered, as has been done with influenza testing". The authors acknowledge that the work of Srugo et al substantially advances the opportunity to one day be able to more accurately assess bacterial infections.

Ashkenazi-Hoffnung and colleagues (2018) conducted a prospective observational study to compare the diagnostic performance of a host-protein signature (comprising of TRAIL, IP-10 and CRP) to other biomarkers for differentiating between bacterial and viral disease in children and adults with respiratory infection and fever without source.Comparator method was based on expert panel adjudication. Signature and biomarker cutoffs and prediction rules were predefined. Of 493 potentially eligible patients, 314 were assigned unanimous expert panel diagnosis and also had sufficient specimen volume. The resulting cohort comprised 175 (56%) viral and 139 (44%) bacterial infections. Signature sensitivity 93.5% (95% CI 89.1–97.9%), specificity 94.3% (95% CI 90.7–98.0%), or both were found to be significantly higher (all p values < 0.01) than for CRP, procalcitonin, interleukin-6, human neutrophil lipocalin, white blood cell count, absolute neutrophil count, and prediction rules. Signature identified as viral 50/57 viral patients prescribed antibiotics, suggesting potential to reduce antibiotic overuse by 88%. The authors state that the host-protein signature demonstrated superior diagnostic performance in differentiating viral from bacterial respiratory infections and fever without source; however, future utility studies are warranted to validate potential to reduce antibiotic overuse.

Stein et al (2018) state that anovel host-protein assay outperforms routine parameters for distinguishing between bacterial and viral lower respiratory tract infections. The authors compared the diagnostic accuracy of a new assay that combines 3 host-biomarkers (TRAIL, IP-10, CRP) with parameters in routine use to distinguish bacterial from viral lower respiratory tract infections (LRTIs). The study cohort included 184 potentially eligible pediatric and adult patients. Reference standard diagnosis was based on adjudication by an expert panel following comprehensive clinical and laboratory investigation (including respiratory PCRs). Experts were blinded to assay results and assay performers were blinded to reference standard outcomes. The evaluated cohort included 88 bacterial and 36 viral patients (23 did not fulfill inclusion criteria; 37 had indeterminate reference standard outcome). The authors state that the assay distinguished bacterial from viral LRTI patients with sensitivity of 0.93±0.06 and specificity of 0.91±0.09, outperforming routine parameters, including WBC, CRP and chest x-ray signs. The authors conclude that these findings support the assay's potential to help clinicians avoid missing bacterial LRTIs or overusing antibiotics. The authors state that a key study limitation is the lack of abroadly applicable reference (i.e., “gold”) standard that can reliably discriminate between these etiologies.Expert panel diagnosis is widely employed in theabsence of a gold standard, as it is in this study.Other study limitationsinclude the age heterogeneity and relatively small sample size.

Carlton et al (2021) conducted a systematic review toassess the diagnostic accuracy of biomarker combinations to rapidly differentiate between acute bacterial or viral respiratory tract infections(RTI) etiology at the point-of-care in order to guide antibiotic treatment.Twenty observational studies (3514 patients) were identified. Eighteen were judged at high risk of bias. For bacterial etiologies, sensitivity ranged from 61 to 100 percent and specificity from 18 to 96 percent. For viral etiologies, sensitivity ranged from 59 to 97 percent and specificity from 74 to 100 percent. Studies evaluating two commercial tests were meta-analyzed. For ImmunoXpert, the summary sensitivity and specificity were 85 percent (95% CI 75%-91%, k = 4) and 86 percent (95% CI 73%-93%, k = 4) for bacterial infections, and 90 percent (95% CI 79%-96%, k = 3) and 92 percent (95% CI 83%-96%, k = 3) for viral infections, respectively. FebriDx had pooled sensitivity and specificity of 84 percent (95% CI 75%-90%, k = 4) and 93 percent (95% CI 90%-95%, k = 4) for bacterial infections, and 87 percent (95% CI 72%-95%; k = 4) and 82 percent (95% CI 66%-86%, k = 4) for viral infections, respectively. The authors concluded that combination ofbiomarkers show potential clinical utility in discriminating the etiology of RTIs; however, there are limitations due to a high proportion of studies with high risk of bias, which preclude firm conclusions. The authors state "current research is overshadowed with bias and is insufficient to make recommendations, especially in primary care where the evidence is entirely lacking", and that "future research should aim to grow the evidence base in primary care and experimentally evaluate patient outcomes and cost-effectiveness".

Basharat and Horton (2021) author the Horizon Scan which provides an overview of emerging point-of-care tests for differentiating bacterial and viral infections to health care stakeholders in Canada. The report includes review of rapid molecular tests and immunoassays such as MeMed's ImmunoXpert.The report also describes the evidence about the diagnostic accuracy of certain tests and their effect on reducing antibiotic prescribing. The systematic review by Carlton et al (2021) is included in the report. The authors concluded that the "emerging evidence suggests that point-of-care tests could be effective tools as part of antibiotic stewardship programs, but further studies assessing specific devices in randomized controlled trials are recommended by researchers and health technology assessment agencies. Monitoring the continued development of devices and the testing landscape, especially in post-pandemic health care, will be important for decision-makers".

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Hainrichson et al (2022)evaluated the analytical performance of a new point-of-need platform for rapid and accurate measurement of a host-protein score that differentiates between bacterial and viral infection.The system comprises a dedicated test cartridge (MeMed BV) and an analyzer (MeMed Key). In each run, three host proteins (TRAIL, IP-10 and CRP) are measured quantitatively and a combinational score (0–100) computed that indicates the likelihood of Bacterial versus Viral infection (BV score). Serum samples collected from patients with acute infection representing viral (0≤score<35), equivocal (35≤score≤65), or bacterial (65<score≤100) scores based on pre-defined score cutoffs were employed for the analytical evaluation studies as well as samples from healthy individuals. To assess reproducibility, triplicate runs were conducted at 3 different sites, on 2 analyzers per site over 5 non-consecutive days. Lower limit of quantitation (LLoQ) and analytical measurement range were established utilizing recombinant proteins. Sample stability was evaluated using patient samples representative of BV score range (0–100). The authors state that theMeMed Key and MeMed BV passed the acceptance criteria for each study. In the reproducibility study, TRAIL, IP-10 and CRP measurements ranged with coefficient of variation from 9.7 to 12.7%, 4.6 to 6.2% and 5.0 to 11.6%, respectively. LLoQ concentrations were established as 15pg/mL, 100pg/mL and 1mg/L for TRAIL, IP-10 and CRP, respectively. The authors concluded thatthe analytical performance, along with diagnostic accuracy established in the Apollo clinical validation study (NCT04690569), supports that MeMed BV run on MeMed Key can serve as a tool to assist clinicians in differentiating between bacterial and viral infection. The authors note that alimitation of the LLoQ, hook effect and linearity studies determining the analytical measurement range was the need to employ recombinant samples. Another limitation is the relatively short amount of time (120 min) that TRAIL, IP-10 and CRP measurements were established as stable in unspun serum samples, which could constrain ease-of-use in some settings. Additional stability studies examining spun serum samples are warranted to further facilitate ease-of-use.

Glossary of Terms

Table: Glossary of Terms
Metagenomicsa molecular tool used to analyze DNA acquired from environmental samples, in order to study the community of microorganisms present, without the necessity of obtaining pure cultures (Ghosh et al, 2019)
Metagenomic next-generation sequencing (mNGS)
  • a shotgun sequencing approach in which all of the nucleic acid (DNA and RNA) in a clinical sample (i.e., cerebrospinal fluid, plasma, respiratory secretions, urine, stool, or tissue) is sequenced at a very high depth (UCSF, 2022)
  • a culture-independent and unbiased hypothesis-free method to diagnose pathogens (Gu et al, 2019; Lee, 2019)
  • running all nucleic acids in a sample, which may contain mixed populations of microorganisms, and assigning these to their reference genomes to understand which microbes are present and in what proportions (Lee, 2019)
Next-generation sequencing (NGS)any of several high-throughput (or massively parallel) sequencing methods whereby thousands to billions of nucleic acid fragments can be simultaneously and independently sequenced (Lee, 2019; Gu et al, 2019)
Shotgun sequencinga laboratory technique for determining the DNA sequence of an organism’s genome. The method involves randomly breaking up the genome into small DNA fragments that are sequenced individually. A computer program looks for overlaps in the DNA sequences, using them to reassemble the fragments in their correct order to reconstitute the genome (NIH/NHGRI, 2022)

bioanalytical methods in which the quantitation of the analyte depends on the reaction of an antigen (analyte) and an antibody (Darwish, 2006);

bioanalytical method that measures the presence or concentration of analytes ranging from small molecules to macromolecules in a solution through the use of an antibody or an antigen as a biorecognition agent (Ju et al, 2017).


The above policy is based on the following references:

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