Laboratory automation in clinical microbiology

Clinical microbiology laboratories are the first and foremost in the defenses against existing and emerging infectious diseases and pandemics like COVID-19. Despite all the technological advantages, workflow in the microbiology laboratory has remained a largely manual process. An astounding 32 percent of technologists’ time in a microbiology lab is spent on manual pre-analytical processing and plating of specimens, while another 10 percent is spent physically transporting specimens within the laboratory.

By comparison, the technically demanding analytical examination and workup of cultures accounts for only 25-35 percent of technologists’ time. Such a large investment of personnel resources for each reported result can easily outweigh the cost of reagents. Additionally, reliance on manual specimen processing and result entry can lead to mislabeling, specimen mix-ups, and transcription errors. Indeed, 87-93 percent of laboratory errors occur in pre- and post-analytical phases of testing. Another challenge of manual culture analysis is the dependence on microbial growth, which typically requires 12-72 hours of incubation. At the same time, the available workforce of microbiology-trained laboratory technologists, as well as medical laboratory technologists overall, is rapidly shrinking. During the past 2 decades, consolidation and centralization of microbiology laboratory services have resulted in a testing environment favorable for automation. The idea of a completely automated microbiology workflow beginning with the arrival of specimens in the lab to the final reporting in the lab information system (LIS) grew in the field of clinical microbiology is in demand now and is evolving rapidly.

Digital image production and high-efficiency sequencing definitely could be the drivers of a technological shift for lab automation and precise diagnostics. Automated blood culture systems, mechanized microbial identification and antibiotic susceptibility and sensitivity testing system and real-time Polymerase Chain Reaction (RT-PCR) are now widely employed in many clinical laboratories. The companies namely BD Kiestra, Copan and bioMérieux are manufacturing and providing partial and full lab automation solutions so far to overcome some of the existing barriers to overcome some of the existing barriers to automation. The Copan’s WASPLab™ (WASPLab™) and Becton Dickinson’s Kiestra TLA (Kiestra TLA) are the two most widely accepted instruments available in the market. These systems include automatic culture testing techniques, such as sample inoculation and streaking, slide making, transport of specimen-containing media between instruments and automatic incubators and has upgraded sample preparation steps and shortened result reporting time.

Total laboratory automation can produce many benefits such as

  1. easy documentation and tracing of laboratory samples;
  2. cost-efficiency;
  3. reduce turn-around time for reagents and media being
  4. results in quicker disease identification and reduction in laboratory’s personnel dependence improving patient care.

Indeed, recent survey of TLA economic benefits in four different-sized labs showed increase in productivity up to 90% and cost reduction per specimen by up to 47%. All benefits together resulted in annual laboratory savings of up to $1.2 million.

There is a hope that complete mechanized automation will also include molecular diagnostic technologies, such as DNA extraction. Such a procedure is multi-stage and of course, requires experienced laboratory personnel. Automated nucleic acid (NA) extraction and analysis technologies will be hugely advantageous when they are required to be performed on large scale, as seen in SARS-CoV-2 pandemic[6, 8]. Lab automation in clinical microbiology lab can result in elimination of numerous processing steps from the workflow cascade, e.g., transport of plates to inoculation area, incubator, or reading desks or labeling and streaking of plates.

Metagenomics has represented a further milestone in clinical microbiology and introduces an appealing tool for the diagnosis of infectious diseases as it has shown to be functionally equivalent to culture techniques, but it can detect pathogens when they are missed by current laboratory methods; it could also constitute a promising tool to be integrated in infection control and clinical epidemiology.

The figure below depicts the “Functional scheme of a futuristic setting in an automatic clinical microbiology laboratory communication”. Data communication and sharing are central and are placed at three levels. The incoming samples are processed by automated phenotypic tests or by Next Generation Sequencing (NGS) at the central bacteriology laboratory. Machine learning approach is applied for acquiring and storing data from the sample, in tandem with the preparation of the initial clinical report. Technical and clinical experts evaluate the final report. Results are added to an electronic health record (EHR). EHR is further sent to internal or external sources.

Figure 1. Schematic representation of a possible future scenario in the dynamics of automated clinical microbiology laboratory networking. Clinical samples are analyzed by automated phenotypic tests or by NGS at the central bacteriology laboratory. Data acquisition, mining and elaboration of a first clinical report are performed by a machine learning approach. The final report is evaluated by technical and clinician experts and resulting information added to an electronic health record (EHR). EHR is then shared either internally (local server) or sent outside. Satellite laboratories and external facilities can also send the outcomes of rapid tests or other analyses to the central facility via a secured cloud and newly acquired information can be integrated in EHRs. NGS, next-generation sequencing.

The testing information and results move back and forth between external and satellite laboratories and internally placed sources. This information can also be included in EHR. Novel technologies have the potential to revolutionize the every-day clinical microbiological laboratory activities and development of novel products for rapid and accurate detection of pathogens from clinical samples is imperative.

The Micronbrane’s products, Devin® and PaRTI-Seq®, in this regard are specially designed to improve the turn-around-time for pathogen detection to less than 24 h which is crucial for lab automation. These products can be efficiently used with both manual and automatic protocols and are compatible with downstream applications including next-generation sequencing on all platforms, qPCR and endpoint PCR. With all the benefits of TLA, due to the diversity and complexity of the primary samples, the microbiology laboratory has been so slow to adopt an automated approach. The microbiology lab requires TLA solutions as unique as the specimens and cultures that are routinely encountered. These systems are now available, and data are beginning to emerge regarding the advantages to workflow, cost, and time to result. Some growing pains can be expected in the adoption of, and adaptation to TLA; however, the future appears to be bright and the possibilities wide for laboratories that embrace the new technology and approach to clinical microbiology.

References

  1. Bailey, A.L., N. Ledeboer, and C.D. Burnham, Clinical Microbiology Is Growing Up: The Total Laboratory Automation Revolution. Clin Chem, 2019. 65(5): p. 634-643.
  2. Vandenberg, O., et al., Consolidation of clinical microbiology laboratories and introduction of transformative technologies. Clinical microbiology reviews, 2020. 33(2): p. e00057-19.
  3. Zimmermann, S., Laboratory Automation in the Microbiology Laboratory: an Ongoing Journey, Not a Tale? Journal of Clinical Microbiology, 2021. 59(3): p. e02592-20.
  4. Croxatto, A., et al., Laboratory automation in clinical bacteriology: what system to choose? Clin Microbiol Infect, 2016. 22(3): p. 217-35.
  5. Barake, S.S., et al. Impact of automation process on microbiological laboratory efficiency. in Open Forum Infectious Diseases. 2017. Oxford University Press.
  6. Cherkaoui, A., et al., Implementation of the WASPLab™ and first year achievements within a university hospital. European Journal of Clinical Microbiology & Infectious Diseases, 2020. 39(8): p. 1527-1534.
  7. Cherkaoui, A., et al., Copan WASPLab automation significantly reduces incubation times and allows earlier culture readings. Clinical Microbiology and Infection, 2019. 25(11): p. 1430. e5-1430. e12.
  8. Dauwalder, O., et al., Does bacteriology laboratory automation reduce time to results and increase quality management? Clinical Microbiology and Infection, 2016. 22(3): p. 236-243.
  9. Peiffer-Smadja, N., et al., Machine learning in the clinical microbiology laboratory: has the time come for routine practice? Clinical Microbiology and Infection, 2020. 26(10): p. 1300-1309.
  10. Chiu, C.Y. and S.A. Miller, Clinical metagenomics. Nature Reviews Genetics, 2019. 20(6): p. 341-355.
  11. Leo, S., et al., Mini review: Clinical routine microbiology in the era of automation and digital health. Frontiers in Cellular and Infection Microbiology, 2020. 10.
  12. micronbrane, https://micronbrane.com/#products.
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