Skip to main content
Journal of Antimicrobial Chemotherapy logoLink to Journal of Antimicrobial Chemotherapy
. 2019 Jan 25;74(Suppl 1):i2–i5. doi: 10.1093/jac/dky529

Rapid microbial identification and antimicrobial susceptibility testing to drive better patient care: an evolving scenario

Volkan Özenci 1,2, Gian Maria Rossolini 3,4,
PMCID: PMC6382029  PMID: 30690543

Abstract

Antimicrobial chemotherapy for septic patients begins with empirical therapy and can be subsequently revised when the results of microbiological testing become available. In recent years, a number of novel technologies for the microbiological diagnosis of sepsis have been developed that return results in a shorter timeframe compared with conventional diagnostic approaches. These novel technologies aid antimicrobial stewardship when treating septic patients by reducing the time to appropriate antimicrobial chemotherapy. Advantages and limitations of these technologies should be well understood upon their introduction in the diagnostic workflow. Increasingly popular DNA-based technologies primarily focus on the rapid identification of pathogens, but information on antimicrobial susceptibility is lacking or limited to a few clinically relevant resistance markers. Thus, DNA-based molecular techniques can complement conventional technologies but cannot replace them. On the other hand, a novel technology that provides both rapid identification of bacterial pathogens and a rapid phenotypic antibiogram with MIC values, and which starts from positive blood cultures, is a very promising approach for fast diagnosis of sepsis. To fully leverage the advantages offered by novel diagnostic technologies for sepsis requires a careful introduction into the laboratory workflow, following an evaluation by a health technology assessment approach. It may also require some reshaping of the workflow (e.g. to process the positive blood cultures on a 24/7 schedule) and of the laboratory organization (e.g. by creating a laboratory subsection for fast diagnosis of sepsis).

Sepsis: balancing antimicrobial chemotherapy with antimicrobial stewardship and the antibiotic resistance crisis

Sepsis is an emergency that requires the prompt administration of antimicrobial chemotherapy active against the causative pathogen(s).1,2 While a sepsis diagnosis is initially based on clinical evaluation and some clinical chemistry parameters, it takes substantially longer (up to 3–4 days) for identification (ID) and antimicrobial susceptibility testing (AST) of the infecting pathogen(s). Under these circumstances, antimicrobial chemotherapy must start on an empirical basis and can subsequently be revised as soon as the results of microbiological testing become available. Microbiological diagnosis, therefore, remains the rate-limiting step for the selection of definitive antimicrobial chemotherapy, and the development of diagnostic technologies that provide faster results [often referred to as ‘fast clinical microbiology’ (FCM)] has been strongly advocated to support antimicrobial stewardship programmes (ASPs).1,3,4

The recent pandemic of MDR pathogens has greatly increased the need for ASPs, to both improve clinical outcomes and reduce the selective pressures generated by the use of broad-spectrum antibiotics. In fact, the dissemination of MDR pathogens consistently reduces the chances of selecting an appropriate empirical therapy while promoting the empirical use of very broad-spectrum agents.

Emerging FCM technologies in the diagnosis of sepsis

Diagnostic microbiology has remained relatively unchanged for decades, but recent years have witnessed a remarkable effort to develop novel technologies for faster microbiological diagnosis of sepsis. Some of these technologies work with positive blood cultures, while a few work directly from blood specimens. All of them return information on pathogen ID and, in some cases, antimicrobial resistance profiles in a shorter timeframe compared with the conventional diagnostic workflow, which involves subculture followed by ID and AST carried out from isolated colonies. A synopsis of FCM technologies for the diagnosis of sepsis from positive blood cultures is shown in Table 1. The various technologies have advantages and limitations that should be considered upon their introduction into the diagnostic laboratory workflow.

Table 1.

The principal FCM technologies for processing positive blood cultures for the diagnosis of sepsis

Method No. of ID targets Panel coverage (%)a ID accuracy, monomicrobial (%) Resistance markers No. of AST antimicrobials References
BioFire FilmArray® BCID panelb 8 Gram-positive/11 Gram-negative bacteria + 5 fungi 80–93 82–92 mecA, vanA/B, blaKPC 5
PNA FISH/QuickFISH®c 4 Gram-positive/4 Gram-negative bacteria + 5 fungi 90–100 6,7
Prove-it™ Sepsis 60 bacteria + 13 fungi 86 95–98 mecA 8
Unyvero BCU Blood Culture Applicationb 12 Gram-positive/14 Gram-negative bacteria + 8 fungi 90 96 vanA, vanB, mecA, mecC, ermA, aac(6′)aph(2″), aacA4, blaNDM, blaKPC, blaVIM, blaCTX-M, blaIMP, blaOXA-23, blaOXA-24/40, blaOXA-48, blaOXA-58 9
Verigene® (GP/GN)b 12 Gram-positive/8 Gram-negative bacteria 90–97 84–99 mecA, vanA/B, blaCTX-M, blaKPC, blaNDM, blaVIM, blaIMP, blaOXA 10,11
MALDI-TOF MS (Bruker/Vitek® MS)b NA 100 61–98 12,13
Short-term culture + MALDI-TOF MS NA 100 78–92 14
Accelerate Pheno™ systemb 7 Gram-positive/8 Gram-negative bacteria + 2 fungi 81–83 86–100 methicillin resistance and MLSb phenotypic screens 8 Gram-positive/15 Gram-negative 15

NA, not applicable.

a

Species coverage rate.

b

European Conformity (CE) marked and FDA cleared.

c

Bacterial QuickFISH is CE marked and FDA cleared. Candida QuickFISH is not FDA cleared.

The crucial factors for FCM technologies are cost-effectiveness, timeliness of the results and the value of the returned information. There is no universal consensus on the definition of ‘fast’, but it is reasonable to define it as obtaining the result within a working day shift (i.e. ∼8 h).16 Concerning the value of the returned information, it is without doubt that detailed and accurate information on the ID and the AST of the pathogens is desired. The species coverage rate of clinical isolates with these technologies is in general high, reaching 100% in the case of MALDI-TOF MS. On the other hand, with molecular technologies based on DNA analysis, coverage depends on the panel of probes included in the test and may be broad spectrum or focused on specific subsets of sepsis pathogens (e.g. Gram-positives or Gram-negatives) (Table 1). Unfortunately, the majority of the current fast technologies focus on rapid ID of microorganisms. The information on the antimicrobial susceptibilities of the pathogens is not available or limited to a few clinically relevant resistance genes (e.g. mecA for methicillin resistance in staphylococci, vanA and vanB for glycopeptide resistance in enterococci, and carbapenemase and ESBL genes in Gram-negatives). Moreover, technologies based on the detection of resistance genes only include a limited number of resistance markers. The presence of these markers only indicates a possible resistance phenotype to some antibiotics and does not reliably inform on the complete susceptibility profile or return MIC values. These are major limitations of the so-called molecular antibiogram.17 Therefore, there is an obvious need for fast diagnostic technologies with phenotypic AST, such as the Accelerate PhenoTest® BC Kit, in which fluorescence in situ hybridization (FISH) to provide organism ID within 90 min is combined with morphokinetic cellular analysis (MCA) to provide a phenotypic antibiogram with MIC values in a timeframe of 6–7 h starting from a positive blood culture, as a fully automated standalone system.15

Given their limitations, most of the fast diagnostic technologies are solely regarded as complementary tools to standard methods in diagnostic clinical microbiology, such that the results obtained from these tests are delivered to clinicians only as preliminary results that should then be confirmed by standard methods. From this perspective, the availability of fast diagnostic technologies, such as the one combining FISH and MCA, that provide the same information and could replace standard methods represents a significant advance. It is therefore time to re-evaluate the actual need for confirmatory standard methods for these fast diagnostic technologies in the clinical routine.

The available evidence for FCM

The ultimate goal of establishing fast diagnostic technologies in clinical microbiology is to improve patient care. However, it is very challenging to evaluate the clinical impact of these technologies (i.e. mortality and morbidity rates of patients with sepsis) due to the complexity of the pathogenesis of sepsis, the local microbiological epidemiology, including the types and antimicrobial susceptibility profiles of microorganisms recovered from blood cultures, and the local antimicrobial policies that play a decisive role in the eventual outcome of septic patients.

A recent systematic review and meta-analysis showed that implementation of molecular methods for the rapid diagnosis of sepsis has clear advantages in terms of reducing the time to administration of appropriate antimicrobial chemotherapy and reducing the defined daily dose of antimicrobial medications.16 However, the study design and the populations evaluated by these studies were heterogeneous, and the quality of studies was variable. Therefore, additional evidence is warranted for a better assessment of the impact of fast diagnostic technologies on outcomes of sepsis, including an evaluation of the importance of the accuracy of the test results, the coverage of the test panel, the information obtained from susceptibility testing, and eventually the post-analytical factors, including overall antimicrobial use, which can also be influential.18,19

Exploitation of FCM: the need to reshape the laboratory workflow

Full exploitation of the advantages offered by FCM requires a thoughtful introduction of the various technologies in the routine laboratory workflow, as well as some reshaping of the workflow itself.20 A substantial shortening of the time to result is possible: down to 1–2 h for ID and detection of resistance determinants with molecular technologies. Using two technologies in a single assay has recently made it possible to obtain ID results in 90 min with FISH and complete phenotypic AST with MCA in 7 h. This shortening, however, possibly mandates processing the positive blood cultures on a 24/7 schedule, although a consistent advantage in terms of time-to-result reduction can also be achieved in laboratories processing positive blood cultures on a 12/6 schedule (which is still adopted by many laboratories).

The introduction of novel diagnostic technologies into the established routine laboratory workflow has an impact on human resources requirements and laboratory budget. Since most of the novel diagnostic technologies are additions to the conventional workflow, their introduction usually requires a net increment of budget and personnel, which should be subjected to an evaluation by a health technology assessment approach.21 From this perspective, fast technologies such as FISH and MCA, that can be substitutive rather than complementary to the standard technologies, can be advantageous, since they eventually have a lower impact on the additional resources needed.

Concluding remarks

The recent development of state-of-the-art fast technologies can potentially improve the impact of clinical microbiology results on clinical outcomes for sepsis. Modern laboratories, equipped with a repertoire of possibilities for the laboratory diagnosis of sepsis, however, face a challenge in establishing these methods in the clinical routine. Positive blood culture followed by subcultures on agar plates and standard phenotypic identification and AST was a ‘one protocol for all samples’ approach, but is quickly becoming out of date as a stand-alone approach. On the other hand, for large laboratories, it is currently almost impossible to routinely implement fast technologies on all clinical samples. Therefore, there is an increasing need to establish algorithms for the use of fast technologies with selected clinical samples as seen in the triage models implemented in emergency rooms.

In the organization of the clinical microbiology laboratory, a subsection dedicated to sepsis-patient diagnostics incorporating the fast microbiology technologies should possibly be foreseen, in which suitable human and technological resources and specific clinical competences are present. In fact, interpretation of the fast microbiology results can be a challenge for clinical laboratories as well as clinicians, and at the same time these results can be crucial to the implementation of efficient ASP.

The concept of personalized treatment has long been established in cancer and autoimmune diseases, including multiple sclerosis and rheumatoid arthritis. Revising the diagnosis and treatment of autoimmune diseases towards personalized medicine will be a revolutionary step towards having more effective and safer therapeutic options.22 In the era of the establishment of state-of-the-art diagnostic methods for sepsis, we believe it is time to start discussing the term ‘personalized diagnostics’ in clinical microbiology.

Transparency declarations

V. O. has none to declare. G-M. R. has served as a consultant (C) to and/or received congress lecture fees (L) and/or research grants (R) from Accelerate (C, L, R), Alifax (R), Arrow (R), Beckman Coulter (C, L), Becton-Dickinson (C, R), bioMérieux (C, R), Cepheid (C, L, R), Checkpoints (R), Curetis (C), Elitech (C, R), Estor (R), Liofilchem (R), Roche (C), Seegene (C, R) and ThermoFisher (C, L).

This article forms part of a Supplement sponsored by Accelerate Diagnostics Inc.

References

  • 1. Cohen J, Vincent JL, Adhikari NK. et al. Sepsis: a roadmap for future research. Lancet Infect Dis 2015; 15: 581–614. [DOI] [PubMed] [Google Scholar]
  • 2. Whiles BB, Deis AS, Simpson SQ.. Increased time to initial antimicrobial administration is associated with progression to septic shock in severe sepsis patients. Crit Care Med 2017; 45: 623–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Peker N, Couto N, Sinha B. et al. Diagnosis of bloodstream infections from positive blood cultures and directly from blood samples: recent developments in molecular approaches. Clin Microbiol Infect 2018; 24: 944–55. [DOI] [PubMed] [Google Scholar]
  • 4. Barlam TF, Cosgrove SE, Abbo LM. et al. Implementing an antibiotic stewardship program: guidelines by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America. Clin Infect Dis 2016; 62: e51–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Altun O, Almuhayawi M, Ullberg M. et al. Clinical evaluation of the FilmArray blood culture identification panel in identification of bacteria and yeasts from positive blood culture bottles. J Clin Microbiol 2013; 51: 4130–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Klingspor L, Lindback E, Ullberg M. et al. Seven years of clinical experience with the Yeast Traffic Light PNA FISH: assay performance and possible implications on antifungal therapy. Mycoses 2018; 61: 179–85. [DOI] [PubMed] [Google Scholar]
  • 7. Forrest GN. PNA FISH: present and future impact on patient management. Expert Rev Mol Diagn 2007; 7: 231–6. [DOI] [PubMed] [Google Scholar]
  • 8. Aittakorpi A, Kuusela P, Koukila-Kahkola P. et al. Accurate and rapid identification of Candida spp. frequently associated with fungemia by using PCR and the microarray-based Prove-it Sepsis assay. J Clin Microbiol 2012; 50: 3635–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Burrack-Lange SC, Personne Y, Huber M. et al. Multicenter assessment of the rapid Unyvero Blood Culture molecular assay. J Med Microbiol 2018; 67: 1294–301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Ledeboer NA, Lopansri BK, Dhiman N. et al. Identification of Gram-negative bacteria and genetic resistance determinants from positive blood culture broths by use of the Verigene Gram-negative blood culture multiplex microarray-based molecular assay. J Clin Microbiol 2015; 53: 2460–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Siu GK, Chen JH, Ng TK. et al. 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 2015; 10: e0139728.. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Faron ML, Buchan BW, Ledeboer NA.. Matrix-assisted laser desorption ionization-time of flight mass spectrometry for use with positive blood cultures: methodology, performance, and optimization. J Clin Microbiol 2017; 55: 3328–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Scott JS, Sterling SA, To H. et al. Diagnostic performance of matrix-assisted laser desorption ionisation time-of-flight mass spectrometry in blood bacterial infections: a systematic review and meta-analysis. Infect Dis (Lond) 2016; 48: 530–6. [DOI] [PubMed] [Google Scholar]
  • 14. Altun O, Botero-Kleiven S, Carlsson S. et al. Rapid identification of bacteria from positive blood culture bottles by MALDI-TOF MS following short-term incubation on solid media. J Med Microbiol 2015; 64: 1346–52. [DOI] [PubMed] [Google Scholar]
  • 15. Pancholi P, Carroll KC, Buchan BW. et al. Multicenter evaluation of the Accelerate PhenoTest BC kit for rapid identification and phenotypic antimicrobial susceptibility testing using morphokinetic cellular analysis. J Clin Microbiol 2018; 56: pii: e01329–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Buehler SS, Madison B, Snyder SR. et al. Effectiveness of practices to increase timeliness of providing targeted therapy for inpatients with bloodstream infections: a laboratory medicine best practices systematic review and meta-analysis. Clin Microbiol Rev 2016; 29: 59–103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Arena F, Giani T, Pollini S. et al. Molecular antibiogram in diagnostic clinical microbiology: advantages and challenges. Future Microbiol 2017; 12: 361–4. [DOI] [PubMed] [Google Scholar]
  • 18. Banerjee R, Teng CB, Cunningham SA. et al. Randomized trial of rapid multiplex polymerase chain reaction-based blood culture identification and susceptibility testing. Clin Infect Dis 2015; 61: 1071–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Osthoff M, Gurtler N, Bassetti S. et al. Impact of MALDI-TOF-MS-based identification directly from positive blood cultures on patient management: a controlled clinical trial. Clin Microbiol Infect 2017; 23: 78–85. [DOI] [PubMed] [Google Scholar]
  • 20. Banerjee R, Ozenci V, Patel R.. Individualized approaches are needed for optimized blood cultures. Clin Infect Dis 2016; 63: 1332–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Mathes T, Jacobs E, Morfeld JC. et al. Methods of international health technology assessment agencies for economic evaluations—a comparative analysis. BMC Health Serv Res 2013; 13: 371. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Tavakolpour S. Towards personalized medicine for patients with autoimmune diseases: opportunities and challenges. Immunol Lett 2017; 190: 130–8. [DOI] [PubMed] [Google Scholar]

Articles from Journal of Antimicrobial Chemotherapy are provided here courtesy of Oxford University Press

RESOURCES