Automation in Bacteriology

Transcription

1 Automation in Bacteriology Antony Croxatto, PhD Diagnostic department Institute of Microbiology Lausanne hospital university (CHUV) Switzerland

2 Statements Lausanne (CHUV), Switzerland: WASP (Walk Away Specimen Processor) (COPAN) (inoculation, gram) SIRWEB (I2A), MOLIS (V4H) (middleware, LIS) Tecan robots, Hamilton robots (Roche), GenXpert (Cepheid) (molecular biology) Bactec Fx (Becton-Dickinson) (blood culture) Vitek 2 (BioMérieux) (Identification, AST) Microflex LT MALDI-TOF MS instrument (Bruker Daltonics) (MS identification) Ongoing study sponsored by BD (imaging algorithms) BD Kiestra TLA installation in August 2017 (laboratory automation) 2

3 Laboratory automation in clinical bacteriology 1. General introduction 2. Technical features and workflow Capacity, throughput, technology Future developments 3. Imaging Telebacteriology New applications / Future developments 4. Performance / Impact / Gain 5. Conclusion

4 Automation in bacteriology About 70% of medical decisions depend on laboratory results Diagnostic tests: Great impact on health care Increase in sample number Limited budget Personnel shortages Quality issues Microbiology is too complex. Volumes too small. Automation Chemistry Haematology Molecular biology

5 Automation in bacteriology About 70% of medical decisions depend on laboratory results Diagnostic tests: Great impact on health care Increase in sample number Limited budget Personnel shortages Quality issues Microbiology is too complex. Volumes too small. Automation Chemistry Haematology Molecular biology New technologies (MALDI-TOF,...) Liquid-based transport devices Laboratories consolidation Microbiology

6 Williams et al. Automation in diagnostic bacteriology. J. clin. Path. 22 History Inoculab (Dynacon Inc --> BD) Innova (BD) The Autostreaker Incubators (Kiestra) WASP Previ-Isola InoqulA FA WASP Lab Tilton et al. Evaluation of an Automated Agar Plate Streaker. JCM, Mar. 1978, p InoqulA SA TLA WCA

7 Automation in bacteriology 5 main manufacturers Kiestra InoqulA Specimen processor WASP Specimen processor Previ-Isola Specimen processor Prelud Specimen processor AUTOPLAK Specimen processor WCA Work Cell Automation WASPLab Recitals Total Bacteriology Lab Automation TLA Total Lab Automation

8 Lab automation Inoculation Incubation Reading Follow up work

9 Lab automation Manual Automated Inoculation Incubation Imaging Reading Follow up work AUTOPLAK Previ-Isola WASP WASPLab / WCA PRELUD MAESTRO InoqulA TLA Preanalytical Specimen processors Incubators Digital imaging Telebacteriology Plates 9 delivery

10 Level of automation Inoculation InoqulA Partial lab automation Work Cell Automation Complete lab automation Total Lab Automation WASP WASPLab Previ-Isola

11 Installed systems Specimen processors Lab automation systems Number of installed systems (August 2015) BD Kiestra biomérieux Copan I2A 105 > (?) France- Belgian (34)? NTEhealthcare 4 (?) Spain

12 Laboratory automation in clinical bacteriology 1. General introduction 2. Technical features and workflow Capacity, throughput, technology Future developments 3. Imaging Telebacteriology New applications / Future developments 4. Performance / Impact / Gain 5. Conclusion

13 Inoculation systems InoqulA Previ-Isola WASP Images from BDKiestra and Lausanne Images from biomérieux Images from Copan and Lausanne

14 Partial Lab Automation WASPLab (Copan) Same level of automation: WCA (BD Kiestra)

15 Wasp Media storage 3 3 CO 2 Incubation Normal Incubation Samples Inoculation Image acquisition Image acquisition 5 Workbench Identification (bar code) 2 Manuel Input 4 Output Stacks or carrousel

16 Total Lab Automation (BD Kiestra) Complete Lab Automation

17 Samples SorterA 1 BarcodA 2 InoqulA 3 Media storage and distribution Identification (barcode) Inoculation FA/SA ProceedA ErgonomicA ErgonomicA Workbench Workbench Manuel input ProceedA ReadA Normal incubation ProceedA Stacker Destacker 5 5 ReadA CO 2 incubation Image acquisition Image acquisition 4 ProceedA Stacker Destacker

18 Automated/intelligent/smart incubators Media plates management Automated storage and incubation Each plate stored individually rapid availability (imaging, distribution) Rapid plates delivery upon request for follow-up work (MALDI-TOF, AST, small tests,...) Constant and uniform T (laminar flow) increased growth efficiency, decreased TAT Improved traceability (bar code reading and indexing) Improved laboratory security Normal or CO 2 atmosphere Automated digital imaging system High quality Imaging Several light sources (above, below, side). Various Imaging incubation s times (user s defined) Images Copan Images Kiestra

19 Laboratory automation in clinical bacteriology 1. General introduction 2. Technical features and workflow Capacity, throughput, technology Future developments 3. Imaging Telebacteriology New applications / Future developments 4. Performance / Impact / Gain 5. Conclusion

20 Specimen Incubation Pretreatment Inoculation Automated plates management Imaging Reading Microscopy MALDI AST Automated Manual

21 Sample Input track Specimen Incubation Pretreatment Inoculation Automated plates management Imaging Reading Microscopy Broth incubator reading Automated colony picking MALDI AST Bacterial suspension Automated Manual Ongoing devl. Ongoing devl. WASPLab ID: MALDI plate AST Cards Disk diffusion

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23 Laboratory automation in clinical bacteriology 1. General introduction 2. Technical features and workflow Capacity, throughput, technology Future developments 3. Imaging Telebacteriology New applications / Future developments 4. Performance / Impact / Gain 5. Conclusion

24 Digital Imaging High definition camera: 48Mp (WASPLAb) to 5Mp (BD Kiestra) Size of the files: about Mb (WASPLab) to 3Mb (BD Kiestra) Several light sources (top, bottom, side). Several imaging conditions Ex: BD Kiestra Ex: BD Kiestra Ex: WASPLab Zoom-in Ex: Biomérieux Zone measurement High quality images Ex: WASPLab Illumination sources Front light Back light Invisible colonies detection Haemolysis detection

25 Telebacteriology Training Consultations AST MALDI-TOF identification Small Tests (Oxidase, indole, prota, Clearview ) Images courtesy of BD Kiestra and Copan

26 Laboratory automation in clinical bacteriology 1. General introduction 2. Technical features and workflow Capacity, throughput, technology Future developments 3. Imaging Telebacteriology New applications / Future developments 4. Performance / Impact / Gain 5. Conclusion

27 Additional improvement of laboratory productivity? Development of intelligent algorithms and expert systems 28

28 Additional improvement of laboratory productivity and quality Development of intelligent algorithms and expert systems Intelligent algorithms Expert systems Quantification and Identification of colonies Detection of colonies Detection of growth induced changes Image acquisition Intelligent image acquisition reliable standardized data, multi-sources of information 29

29 Imaging Detection Quantification Identification Expert Systems Results Expert systems Identification and Identification of colonies Detection of colonies Detection of growth induced changes Intelligent image acquisition reliable standardized data, multi-sources of information 30

30 Transformation of a Culture Plate to a Digital Image (WASPLab) High resolution images Quality Optics Accurate coloration Wide field depth 1 mm 1 mm 1 mm Images: Courtesy of Copan 48 MegaPixe l 12 MegaPix el High Resolution 3 MegaPix el

31 Importance of Image Acquisition for Accurate Colony Information Extraction (OPTIS TM,BD Kiestra) Standardized data (multi-sources of information) internal calibration, correction and normalization of image acquisition Optimization of SNR and contrast for each pixel and each channel of the image 22 images/acquisition Images: Courtesy of Becton Dickinson 32

32 WASPLab Separate Algorithm for each plate type or each plate side (bi-plates) Software identifies bi-plate and reads growth on each side using different light/algorithm. Different media different acquisition conditions Images: Courtesy of Copan 33

33 3D imaging (WASPLab) Benefits for Image Analysis Morphology recognition Z Axis for automated colony picking (Colibrì) Images: Courtesy of Copan

34 Imaging Detection Quantification Identification Expert Systems Results Expert systems Quantification and Identification of colonies Detection of colonies Detection of growth induced changes Intelligent image acquisition OPTIS TM technology: reliable standardized data, multi-sources of information 35

35 Capability to recognize and evaluate growth Time x Time 0 Growth detection Images: Courtesy of Copan 36

36 Growth detection Classification Images: Courtesy of Becton Dickinson 37

37 Growth detection sensitivity Detection: Evaluation of the Algorithm to report positive and negative plates Croxatto et al. Submitted manuscript Sample type Media N Se Se CI (se) N Sp Sp CI (Sp) all all (96.2, 97.8) (90.1, 96.5) all CHROM (91.7, 95.6) (90.7, 98.4) all COL (98, 99.6) (67.9, 96.6) all MAC (98.6, 100) (89.5, 98.9) Isolates all (96.2, 98) (91.4, 99.1) Isolates CHROM (91.5, 96) (92.3, 100) Isolates COL (97.8, 99.7) (33.3, 100) Isolates MAC (98.7, 100) (61, 100) Urines all (94.5, 98.5) (86.6, 96) Urines CHROM (86.9, 97.2) (77.8, 100) Urines COL (97.3, 100) (65.2, 100) Urines MAC (92.1, 100) (88.1, 98.7) 1-specificity 0.8% (15/1796) false positives (dust, artifacts, FN reported by technicians,.) 2.6% (46/1796) false negatives (growth pattern not in database, FP reported by technicians, ) 38

38 Growth detection Growth detection: AST zone measurements Associated to expert system Human validation Images: Courtesy of BD and Copan 39

39 Imaging Detection Quantification Identification Expert Systems Results Expert systems Quantifying colonies Identification of colonies Detection of colonies Detection of growth induced changes Intelligent image acquisition OPTIS TM technology: reliable standardized data, multi-sources of information 40

40 Growth quantification (semi-quantification) Sort Plates by Colony Count (buckets) Manual review Auto-release < >10 5 No rules applied - All specimens will go to Manual Review (default) < >10 5 Specimens with a score < 10 3 will be Auto Reported, if user defined rule is set this way. The rest goes to Manual Review < >10 5 Specimens with a score < 10 4 will be Auto Reported, if user defined rule is set this way. The rest goes to Manual Review Quantification Accuracy: Algorithms > manual reading Manual reading: global agreement between 10 readers: 47.3% Images: Courtesy of BD 41

41 Imaging Detection Quantification Identification Expert Systems Results Expert systems Quantifying colonies Identification of colonies Detection of colonies Detection of growth induced changes Intelligent image acquisition OPTIS TM technology: reliable standardized data, multi-sources of information 42

42 Color Intensity Average RGB with bottom illumination and top illumination on black background Average LCH with top illumination Optical density in the R,G,B and L channels Presumptive ID CHROMAGAR Incubation time Intensity (different backgrounds and channels) Contextual info local density Morphology Morphology: Shape Circularity Area Perimeter... Contextual info local density Total N of objects Distance of object x to the centre of the plate and to the spot of inoculation Local contrast (temporal and spatial) Distance from other colonies,...

43 Classification (BD CHROMagar TM Orientation Medium ) Esc col Enterococcus sp. Sta sap Stc aga Sta aur Klebsiella Enterobacter Citrobacter Serratia Proteus Morganella Providencia KPN CKO CFR ECL EAE SMA MMO PMI PVU

44 Colony identification on chromogenic agar Isolates and Clinical urine samples colonies ESC COL ENTEROCCOCUS STA SAP STR AGA KESC PMP ESC COL 97,26% 0,10% 1,73% 0,17% 0,03% 0,10% ENTEROCCOCUS 0,01% 93,98% 0,00% 0,08% 0,28% 0,00% STA SAP 1,58% 0,32% 97,70% 1,16% 0,19% 1,46% STR AGA 0,02% 0,03% 0,00% 93,92% 0,00% 0,00% KESC 0,43% 5,39% 0,35% 4,16% 99,25% 0,59% PMP 0,70% 0,18% 0,22% 0,52% 0,25% 97,85% Count ESC COL: Escherichia coli STA SAP: Staphylococcus saprophyticus STR AGA: Streptococcus agalactiae KESC: Klebsiella-Enterobacter-Serratia-Citrobacter PMP: Proteus-Morganella-Providencia Croxatto et al. Submitted manuscript 45

45 Color Recognition on Chromogenic Plates Automatically Detects & Segregates Positive MRSA & VRE Samples Using Different Manufacturers Chromogenic Plates brightness of color Threshold volume biomérieux Type of color Intensity of color Training of algorithms for specific and defined detection and recognition Faron et al. JCM

46 Automated MRSA Detection 57,690 Samples Multi-Center Study Automatic Detection and Segregation of Positive from Negative MRSA Samples Using Different Manufacturers Chromogenic agar Sensitivity of 100% Specificity of 90-96% (varied by location) Detection of 153 Positive Plates that were Missed Manually Automated VRE Detection Faron et al. JCM ,730 Samples Multi-Center Study Automatic Detection and Segregation of Positive from Negative VRE Samples Using Colorex VRE or Oxoid VRE Sensitivity of 100% Specificity of 89.5% (varied by location) Detection of 498 Positive Plates that were Missed Manually Faron et al. JCM

47 Presumptive ID Phenotypic colony recognition (WASPLab) Size Morphology Growth Characteristics Images: Courtesy of Copan 48

48 Presumptive ID Phenotypic colony recognition (WASPLab) Detects and differentiates between potential Pathogens Performance? (quality of the data base) Image: Courtesy of Copan 49

49 Imaging Detection Quantification Identification Expert Systems Results Expert systems Quantification and Identification of colonies Detection of colonies Detection of growth induced changes Intelligent image acquisition reliable standardized data, multi-sources of information 50

50 Expert systems Growth results: Detection, quantification and identification Automated growth detection Automated quantification Automated identification (group or species) Automated zone measurements (AST) Type of specimen Patients data: Demographics (sex, age, origin,..) Location (hospitalized/ambulatory, unit, ) Clinical information Expert rules (urine, AST,..) Expert systems Interpretation: Auto-release or human reviewed for validation (Expert rules established to classify cultures for further manual review or auto-release) No growth, normal flora/contamination Follow-up work, ID/AST Report results 51

51 Applications BD Kiestra WASPLab Growth/No Growth Batch release Auto release YES (Any kind of plates) YES Chromagar /Blood agar... MacConkey/ CLED... (Semi) Quantification YES YES Identification Sorting Detection of sister colonies Quantification per isolate Color recognition Morphology recognition Zone reading with expert system for AST YES Chromagar (MRSA, VRE) YES Release? YES Release? YES 2019 YES Release? YES MH MHF YES (Any kind of plates)? YES Chromagar (MRSA, VRE) YES 2017 N of isolates on different media per sample YES Release? YES Release? YES 2018? YES MH MHF Release? 3D imaging NO YES For reading and automated colony picking Expert systems YES YES 52

52 Incubation Inoculation Automated plates management Imaging Reading Interpretation Decision making automation manual No Growth Growth Decision making Follow-up work ID / quant. Decision making AST 53

53 Intelligent algorithms and applications linked to expert systems Incubation Inoculation Automated plates management Imaging Intelligent algorithms Decision making Detection No Growth Growth Decision making Follow-up work Identification Quantification Expert Systems Expert Rules ID / quant. automation algorithms Decision making AST Zone measurements 54

54 The future of microbiology?! 55

55 Laboratory automation in clinical bacteriology 1. General introduction 2. Technical features and workflow Capacity, throughput, technology Future developments 3. Imaging Telebacteriology New applications / Future developments 4. Performance / Impact / Gain 5. Conclusion

56 Impact studies: General considerations Manufacturers (marketing): hoped-for performance inferred expectations Need for investigation of the true performance of total lab automation Automation assessment in objective, comparative and prospective clinical studies performed by independent laboratories. Peer-reviewed literature Specimen processors TLA, WCA FMLA WASPLab? Few peer-reviewed articles

57 Inoculation systems: Impact studies Higher yield of discrete colonies Higher N of detected morphologies (polymicrobial samples) Increased reproducibility decreased analytical variation (sample processing, disk diffusion) Increased accuracy Decreased hands-on time ( 50%) Shortening of time-to-results (ID/AST) Reduction of laboratory cost Better laboratory workflow Croxatto et al. JCM 2015 Kleefstra M. et al Abstr. 21st ECCMID/ 27th Int. Congr. Chemother., abstr P1801. Rydback J. et al Abstr. 21st ECCMID /27th Int. Congr. Chemother., abstr R2477. Froment P. et al. JCM Chapin K.C. et al Abstr. 112nd Gen. Meet. Am. Soc. Microbiol., abstr 734. G. Funke G. Et al th ECCMID. P887 Mischnik A. et al. JCM Bourbeau P, Swartz BL. JCM Jones G.A. et al. JCM Quiblier et al. JCM 2016 Hombach et al. JCM.2015 Complete/partial Lab automation systems: Impact studies Reduction of incubation time (urines: 24h 16h) Reduction of the TAT Reduction of the TAT compared to CM (30.6 hr faster): Automation + MALDI-TOF Bielliet al., paper presented at 25th ECCMID 2015, abstract EVO535 Mutters et al. Ann Lab Med 2014

58 Lab Automation systems: Increased productivity (Volume/FTE) Bentley et al. Automating the bacteriology laboratory Abstr. 21st ECCMID P FTE (full time equivalent) Humphrey et al Abstr. 21st ECCMID P Introduction of the BD Kiestra TLA system allowed a 2.6-fold increase in the LPI Copan personal communication Introduction of the WASPLab system allowed a decreased hands-on time and an increased LPI (SHL laboratory, The Netherlands)

59 Reduction of FTEs for some laboratory activities Identification & AST 18% Plates reading 26% Samples arrival LIS sample recording 1% 11% 40% Breaks Microscopy 2% 2% 3% 4% 7% 7% 8% 11% Inoculation Plates sorting and incubation Results verification Parasitology Orientation assays FTEs: (2.4) Laboratory management Sample arrival Pre-analytical phase Inoculation Incubation Reading Follow-up work Added value tasks FTE

60 Laboratory automation in clinical bacteriology 1. General introduction 2. Technical features and workflow Capacity, throughput, technology Future developments 3. Imaging Telebacteriology New applications / Future developments 4. Performance / Impact / Gain 5. Conclusions

61 Automatisation Le but n est pas de remplacer des techniciens expérimentés mais de les assister dans leurs tâches (pré-analytique, analytique, post-analytique) Amélioration de: l activité/productivité (augmentation des échantillons) la qualité (reproductibilité) la rapidité (obtention des résultats) diminution du temps de séjour, diminution du risque infections nosocomiales, amélioration du traitement diminution des coûts. La traçabilité (codes barres) Diminution de: Erreurs (codes barres, ensemencements des milieux,..) Variation analytique Activités monotones, répétitives et fatigantes repositionnement des personnes expérimentées à des postes clés de l activité de laboratoire (inspection pré- et post-analytique, interprétation, résolution de problèmes, innovation, R&D) Coûts.

62 Sample arrival Pre-analytical phase Inoculation Incubation Reading Follow-up work Added value tasks FTE Sample arrival Inoculation MANUAL AUTOMATION Sample arrival Inoculation Incubation and Imaging Reading ID MALDI AST Incubation and Imaging Reading ID MALDI AST DAY 1 DAY 2

63 No laboratory adaptation to automation (staff shifts, training, 24/7,...) Misuse of the tools, lack of reorganization. Expectations for increased productivity not achieved Crash of the automat (need a backup) A good support and maintenance is essential. Expensive maintenance budget. Dismiss of staff / staff escape (boring and lonely work?) Lab automation needs to be a laboratory project that includes everybody. The aim is not to replace experienced laboratory technicians but to assist them in their daily tasks. Only the eye is used (smelling or sensing of colony consistency disappear) More difficult to identify unusual/new species. Loss of microbiological knowledge Automation: Threats Decrease the analytical variability microbiological factory (you find what you are looking for) Connectivity Efficient bi-directional connectivity to LIS is essential and complex Installation/implementation of lab automation Complex and time consuming (several months to years)

64 Thanks for your attention Clinical Microbiology and Infection, Volume 22 Number 3, March 2016