A Tsunami of Change, Automation in Clinical Microbiology

Transcription

1 A Tsunami of Change, Automation in Clinical Microbiology Nathan A Ledeboer Associate Professor of Pathology Medical College of Wisconsin Medical Director, Microbiology and Molecular Pathology Interim Co-Director, Clinical Chemistry Dynacare Laboratories and Froedtert Hospital Medical Director, Laboratory Outreach and Reference Services Dynacare Laboratories Milwaukee, WI

2 The Problem of Microbiology

3 Current Culture Workflow Blood drawn in ER, ICU, hospital floors Culture positive Pathogen group Pathogen ID Pathogen resistance Bottle culture Gram stain Samples plated for sub-culturing Resistance testing t=0 t=8h t=24-36h t=48-72h

4 The Phases of Automation Start with PreAnalytics Why we Automate The Instruments The Process The Data Potential Pitfalls What is to Come

5 The Challenge of Microbiology

6 Standardization on Transport Device Liquid Microbiology Standardized specimen transporters Easier to stock clinic rooms/ordering easier Easier to educate our clinical staff on the appropriate device Ensure specimen integrity Boric Acid Tube URINE C&S Vial STOOL ESwab (Copan) skin and soft tissue swabs, throat, GBS. BETTER PATHOGEN RECOVERY, MORE HOMOGENEOUS SPECIMEN Quality Data drove implementation and administrative buy in

7 Flocked swab doesn t have the internal part Sample remains close to the surface of the swab Easy release of the sample into liquid transport medium Liquid transport medium works as a broth of microorganisms

8 Gram Stains We Practice What Fontana We Teach C, et al. BMC Research Notes, 2009.

9 Eswab and Gram Stain Genital specimens (N=80) collected in Eswab, two smears were prepared from each specimen, one manually, by directly streaking the flocked swab of the ESwab sample on a glass slide, the other smear was prepared with 10ul of sample on the WASP. 72/80 specimens were concordant for both manual and WASP smear preparations. Epithelial cells and leucocytes were detectable in ESwab samples with proper staining morphology in both Gram stain preparations. THE EIGHT DISCREPANCIES WERE NEGATIVE IN THE MANUAL PREPARED SMEAR WHILE 8 GRAM NEGATIVE RODS WERE ONLY PRESENT IN THE WASP PREPARED SMEARS. An agreement of % was found and was dependent on the type of bacteria. J. Steenbergen*, M. Stalpaert. Comparison of manual and WASP automation Gram smear preparation with specimens collected in Eswab. ECCMID 2013

10 RECOVERY COMPARISON OF COMMONLY ENCOUNTERED MICROORGANISMS IN A CLINICAL SETTING Grover Smith and Tammye L. Jackson. ASM 2012

11 Organism recovery from different swab systems Evaluation of bacterial recovery and viability from three different swab transport systems. Tan, Thean; Ng, Lily; Sim, Diana; Cheng, Yvonne; Min, Melissa Pathology. 46(3): , April 2014.

12 CFU CFU Spiked Study (Anaerobes) 1. Suspension made in saline 2. Diluted to ~10 5 CFU/mL in eswab 3. Plated by WASP using 1 µl loop 4 C 25 C Study demonstrates recovery of anaerobes at 4 o C to 48 h and recovery of anaerobes at 25 o C to 24 hours.

13 Wound culture Higher total CFU/mL recovered w/ eswab Better recovery of CoNS, Enterococcus spp. Statistically similar recovery for others Saegeman et al., Eur J Clin Microbiolo Infect Dis (2011)

14 WHY WE AUTOMATE

15 Why Automate? Potential answer to shrinking workforce Need to staff when plates are to be read, not just 9-5 Answer to ergonomic realities Quality of life issues/cost to organization Labs are consolidating can do more potentially with less but perhaps larger Better quality product consistent plating Pressure for decreased TAT from receipt to results Pressure to be open 24/7 Can be adapted to various size labs

16 Why Automate? continued. Pre-analytical processing of specimens reduces time to incubation increased quality, consistency in plating Digital Microbiology imaging analysis to aid the CLS Useful for training/documentation Quality Assurance Remote locations less skilled CLS Decrease costs through more efficient use of resources

17 Need for lab automation Driven by declining skilled labor and cost pressures in today s economic climate Macro trends are driving the need for automation The biggest driver of automation is the lack of qualified microbiologists and med techs Key market trends Lack of experienced technologists, supervisors, pathology and microbiology PhDs Decreased financial incentive for in-patient testing and increased incentive for shorter LOS Number of med tech programs US % Med tech enrollment US annual 8,296-65% 2,871 Increasing volume and lab consolidation pressures Pricing and reimbursement pressure Need for sample traceability/ chain of custody Lab professionals eligible for retirement US 2010, percent Need for better coordination between the lab physician pharmacist 60% 40% Eligible Not eligible

18 Trends to Automation? The Industry is Changing Specimens increasing on average 10-15% per year Laboratory consolidation Reimbursement Workforce Less students choose Medical Technology: reduction of 30-50% Pay for technologists is substandard Quality Physicians are demanding more services, in less time Traceability

19 Manual Processing Microbiology too complex to automate Specimen Diversity Collection Device Diversity Diversity of Techniques Diversity of Media The human element Technologists are faster than machines Humans are capable of thinking, machines are not Humans are flexible Automation considered too Expensive Small volumes Only the large labs can automate

20 And Don t Forget:

21 Manual Processing Microbiology too complex to automate Specimen Diversity Collection Device Diversity Diversity of Techniques Diversity of Media The human element Technologists are faster than machines Humans are capable of thinking, machines are not Humans are flexible Automation considered too Expensive Small volumes Only the large labs can automate

22 Advances in Identification

23 The Future of Mass Spectrometry Continued migration to mass spectrometry for microbial ID based on performance and cost Automation will simplify the set-up and further drive down costs Continued expansion of applications

24 Time course of the numbers of total isolates misidentified using phenotypic identification (PID*), isolates confirmed by a second PID* and isolates confirmed by molecular identification (ID**) over 11 years of routine identification in our clinical laboratory. Seng P et al. J. Clin. Microbiol. 2013;51:

25 Time course of the numbers of isolates of 128 rare species, 48 of which were identified using phenotypic identification (PID), and 75 of which were identified using molecular identification (ID). Seng P et al. J. Clin. Microbiol. 2013;51:

26 Mass Spectrometry: the Key to Proteomics Success Elements of a Mass Spectrometer Samples (nhplc/pipet) Ionisation Method (nesi/maldi) Mass Analyser (Orbitrap/ToF) Detector Data System Sample ESI= Electrospray ionization HPLC = high performance liquid chromatography MALDI = matrix assisted laser desorption ionization

27 Available Automation

28 ErgonomicA Workbench InoqulA FA/MI ReadA Compact BarcodA SorterA

29

30 Pre-Sorting of urine cultures 1ul 0 CFU/ml 24 cultures per screen 10 4 CFU/ml shows as approximately 10 colonies 10 5 CFU/ml shows as approximately 100 colonies

31 The Nuts and Blots of Automating a Microbiology Laboratory

32 Sample Mix 30% Distribution of Sample types 70% 87% 13% Sample Not for culture Culture Samples for WASP automation Culture Samples remain manual Specimen types for automation # samples/ d Urine 264 Wounds 40 Nosocomial - MRSA 25 Group B Screen 24 Throat 21 Sputum 17 Feces 15 Group A Screen 15 Gynecology 13 Nosocomial - VRE 1 Eyes 1 Ears 1 Nose 1

33 Construction of the Physical Plant Plan way ahead for any construction requirements This is a long-term capital investment, make sure your layout will be sufficient for the next 10 years or more Avoid selecting a layout that will work, but is not optimal as changing layout in the future will only increase costs Plan a construction budget with a 20% contingency for changes in scope of project Confirm that your floors can accommodate the weight of instruments Are your doors and elevators adequate?

34 Sizing Your Automation Consider if you want to build in capacity or if you will expand as you need additional capacity How will this impact ROI Will future budget $ be available? Will your vendor assure you that new components will be reverse-compatible? Will you add additional functionality in the future?

35 Connectivity Engage your LIS vendor before you decide to purchase an automation system You will need an interface, what are your options? Will an existing interface suffice? We had an interface with WASP, but would not work with WASPLab as WASPLab needed bidirectional interface Is there a middleware solution? A new interface (generated by both the lab and LIS vendor) will take 6 months or longer to build

36 More LIS Take a long-term approach Can you use your automation software as a middleware Will need connectivity to susceptibility testing systems, MALDI, other ID systems Consider the future, can you add rapid ID results, next-generation sequencing results? Consider if you will be changing LIS vendors at any time in the future, what impact will that have?

37 Does Automation Ultimately Work and Benefit Patient Care

38 Four-category scale used for evaluating the quality of bacterial isolation. Froment P et al. J. Clin. Microbiol. 2014;52:

39 Distribution of recovered and nonrecovered microorganisms from the polymicrobial suspensions by the manual and automated methods. Froment P et al. J. Clin. Microbiol. 2014;52:

40 Variability of the quality of isolation at the laboratory team level (15 technicians) and improvement obtained with the instrument over each technician. Froment P et al. J. Clin. Microbiol. 2014;52:

41 The future of diagnostic bacteriology Matthews S, et al. CMI, 2011.

42 Comparison of recovery rates of enteropathogens from stool cultures for a one-and two-year-period before and after introduction of automatic inoculation using Previ Isola Mischnik A., et al. Annals of Laboratory Medicine, 2015

43 Comparison of sensitivities and specificities of manual/ls swab to WASP/ESwab for the recovery of S. agalactiae No. with indicated test result Method and swab type True positive False positive True negative False negative Total no. Sensitivity (%) Specificity (%) Direct plating Manual/LS swab WASP/ESwab a b Enrichment culture Manual/LS swab WASP/ESwab c d Buchan B et al JCM

44 Recovered Species Quiblier C et al JCM

45 CFU Correlation between WASP and Manual Streaking Quiblier C et al JCM

46 Performance of Kiestra total laboratory automation combined with MS in clinical microbiology practice When Kiestra full laboratory automation was combined with MALDI-ToF MS: Pathogen identification using Kiestra TLA combined with MS resulted in a 30.6 h time gain per isolate compared to CM. Pathogens were successfully identified in 98.4% (249/253) of all tested isolates. Early microbial identification without susceptibility testing led to an adjustment of antibiotic regimen in 12% (24/200) of patients. Did not evaluate the effect of automation alone on TAT or accuracy of identification Mutters N et al. Annals of Laboratory Medicine. 2014;34:

47 VALIDATION OF URINE SPECIMENS 92 urine specimens were processed on the WASPLab, images were captured at 0, 18, and 24 hours. Plate images were initially viewed onscreen after 18 h incubation. Negative cultures were automatically unloaded, negative result confirmed and discarded Positive cultures designated as pathogens requiring further workup, fecal contaminated, pathogens <10,000 cfu/ml, or normal skin flora. The plates were extracted from the WASPLab incubator and sent to the specified canister, manually read, and compared to the on-screen image. 76 of the 92 cultures were designated as positive 100% concordance between manual read and WASPLab interpretation for 16 negative cultures Of the 76 positive cultures, 78% concordance between manual interpretation and WASPLab. 17 cultures (22%) where the on-screen image and manual plate reading interpretations did not match. 13 were due to overcalling a potential Enterococcus species on-screen, when the colony was actually a normal skin flora Corrected through technologist education 4 were due to missing a pathogen in heavily mixed cultures on the manual read Turnaround was reduced by ~18 hours Riebe K, Poster at ASM 2015

48 Pitfalls

49 What are the potential pitfalls? Exciting technology - looks easy to implement LOTS OF MOVING PARTS Space Allocation/Remodel costs Could be significant issues for some How do these systems fit into small AND large labs Will small labs benefit? What is the cutoff? Systems need to be modular to accommodate size differences Once a system is implemented how do you keep it running smoothly? What training is necessary? Slide courtesy of S. Novak

50 Pitfalls Continued Verification What should a laboratory do to verify the system meets stated performance characteristics? Expectations - impact of system maintenance? What warrants re-verification? Or Checks? Who keeps track of this? On a busy shift with multiple personnel it s important to have this worked out Vendors need to help Mean time to failure (MTF) data on instrument extremely important Instrument down time can lead to stresses on staff, poor morale and a non-acceptance of automation Slide courtesy of S. Novak

51 Costs Equipment Initial investment Business case this is most difficult (important) part WE NEED to prove ROI return on investment - prior to purchase What assurances are vendors giving us? For a large lab could consume large % of system capital budget It s own project with special funding Change management What is change management is there a cost to this? Have we considered this concept fully in the laboratory before?? How will the automation impact the staffing?? Information Technology needs has to be considered! Costs of remodel Facilities Typically have to plan far enough in advance for most hanges Slide courtesy of S. Novak

52 Considerations Change Management/ Staff acceptance LIS- Complex integration with automation Impact on other areas Integration of current systems Redundancy and backup for downtime Technology enhancements Impact of growth on staffing requirements after adoption of automation Impact on Safety

53 What is to Come

54 How can we use these images for automation Software analysis - Image differentials Time = 24 hours Time = 0 hours Differential

55 The Algorithm

56 How it Works

57 Result Definitions MP/AP = Manual Positive, Automation Positive (TP) MN/AN = Manual Negative, Automation Negative (TN) MN/AP = Manual Negative, Automation Positive (FP) MP/AN = Manual Positive, Automation Negative (FN)

58 Results Performance of WASPLab TM digital imaging compared to manual reading Clinical test site No. of specimens tested Results (no.) a Performance (% [95% CI]) b MP/AP MN/AN MN/AP MP/AN Sensitivity Specificity Prevalence ( (95-100) 96) 2.1% ( (89-100) 90) 1.6% ( (91-100) 93) 7.3% ( (90-100) 91) 4.6% Total ( (90-100) 91) 2.4% a MP/AP, manual Pos automation Pos; MN/AN, manual Neg/automation Neg; MN/AP, manual Neg/automation pos; MP/AN, manual pos/automation Neg. b CI, confidence interval. Faron et al. 2016

59 Discrepant categorization Automation positive 2 nd Manual positive Residual Matrix Borderline Colors Faron et al. 2016

60 Discrepant analysis Discrepant analysis of Manual Negative/Automation Positive Plates Discrepant Category MN/AP a Automation Positive 2 nd Manual Positive Residual Matrix Borderline Colors Number of plates a Manual Negative/Automation Positive Faron et al. 2016

61 Is there difference between agar? Comparison of 3 Chromogenic Agars for the detection of MRSA Chromogenic media No. of specimens tested Results (no.) a Performance (% [95% CI]) b MP/AP MN/AN MN/AP MP/AN Sensitivity Specificity MRSASelect (99-100) 90.7 (90-91) chromid MRSA BD Chromagar MRSA (97-100) 92.4 (91-93) (99-100) 90.7 (90-91) a MP/AP, manual Pos/automation Pos; MN//AN, manual Neg/automation Neg; MN/AP, manual Neg/automation pos; MP/AN, manual pos/automation Neg. b CI, confidence interval. Faron et al. 2016

62 Can we use this software for VRE screening? 3 sites Specimens (n=104,730) Rectal Eswabs TM Media (n=2) Colorex VRE (BioMed Diagnostics) Oxoid VRE (Thermo Fisher Scientific) Reference method Manual reading Discrepant analysis Images reviewed by supervisor Compare Automated

63 Representative Images Negative Positive Break through growth Colorex VRE Oxoid VRE Faron et al. 2016

64 Results Performance of WASPLab TM digital imaging of VRE plates compared to manual reading Clinical test site No. of specimens tested Results (no.) a Performance (% [95% CI]) b MP/AP MN/AN MN/AP MP/AN Sensitivity Specificity 1 11,438 1,474 9, (99-100) 91.6 (91-92) 2 75,518 2,822 64,535 8, (99-100) 88.8 (88-89) 3 17,774 2,107 14,315 1, (99-100) 91.4 (91-92) Total 104,730 6,403 87,979 10, (99-100) 89.5 (89-90) PPV c (%) NPV c (%) Prevalence % % % % a MP/AP, manual Pos automation Pos; MN/AN, manual Neg/automation Neg; MN/AP, manual Neg/automation pos; MP/AN, manual Pos/automation Neg. b CI, confidence interval. c PPV, Positive Predictive Value; NPV. Negative Predictive Value Faron et al. 2016

65 Discrepant analysis Discrepant analysis of Manual Negative/Automation Positive Plates Discrepant Category MN/AP a Automation Positive 2 nd Manual Positive Residual Matrix/Yeast Borderline Colors Total number of plates 10, ,234 1,616 Colorex VRE Oxiod VRE a Manual Negative/Automation Positive Faron et al. 2016

66 Comparison of agars Comparison of 2 Chromogenic Agars for the detection of VRE using automated scoring Results (no.) a Chromogenic media No. of specimen s tested Performance (% [95% CI]) b MP/AP MN/AN MN/AP MP/AN Sensitivity Specificity Colorex VRE 86,956 4,296 73,664 8, (99-100) 89.1 (89-89) Oxoid VRE 17,774 2,107 14,315 1, (99-100) 91.4 (91-92) a MP/AP, manual Pos/automation Pos; MN/AN, manual Neg/automation Neg; MN/AP, manual Neg/automation Pos; MP/AN, manual Pos/automation Neg. b CI, confidence interval. c PPV, Positive Predictive Value; NPV. Negative Predictive Value Faron et al. 2016

67 Incorporating into the laboratory Negative Specimens Batch viewing 40 images/page Batch report Non-negative Specimens Still requires Technologist View on HD monitor Positive vs Matrix or Yeast Standard of care

68 Manual Processing Technologist Labor is $40.00/hour (w/benefits) Automated Processing $6.40 in labor/negative specimen 9.6 min/negative specimen a $1.33 in labor/negative specimen ~2 min/negative specimen Cost of negative workup for the study (n = 87,979) $563, in labor $117, in labor Savings = $445, a. Shadel et al. Surveillance for vancomycin-resistant enterococci: type, rates, costs, and implications.

69 Can it Quantitate?

70 Questions?