Laboratory Literature Review

Transcription

1 Laboratory Literature Review Lars Westblade Weill Cornell Medicine First Coast 2019 February 2, 2019

2 Financial Disclosures Accelerate Diagnostics, Inc., research funding BioFire Diagnostics, LLC., research funding

3 Objectives Summarize key diagnostic advances in infectious diseases diagnostics during 2018 Describe the methodology employed in the presented publications Critically evaluate presented data

4 Paper 1 A machine learning algorithm Smith et al., 2018 J Clin Microbiol 56:e

5 Study Design Blood culture Gram stains (468 slides) Automated microscopy (40 objective) Digitize slide G + N D G + G + N D G + G + G + Result: Gram positive Machine Learning: - Gram-positive cocci in pairs and chains (GPC PR/CH) - Gram-positive cocci in clusters (GPC CL) - Gram-negative rods (GNR) Slide courtesy of Kenneth P. Smith and James E. Kirby, Harvard

6 Change function parameters Machine Learning Workflow Human classified images Gram-positive cocci in chains What a human sees Transform using series of functions (a neural network) Classify images based on function output Image convoluted What the machine learning algorithm sees Evaluate accuracy Background GNR GPC PR/CH GPR CL Output Finish training Slide courtesy of Kenneth P. Smith and James E. Kirby, Harvard

7 Representative Image Collected by Automated Imaging Protocol Legend: A, intense background staining B, stain crystallization artifact C, diffuse background staining D, E, Gram-negative rods

8 Results Background GPC PR/CH GPC CL GNR Bacteria detected in 84.7% (160/189) slides by machine learning algorithm Sensitivity, >97% for GNR and GPC CL, 75% for GPC PR/CH mis-classified as GPC CL Specificity, >93% for all classifications In publication, 92.5% image crop accuracy post-publication, 99.8% image crop accuracy (reflects better sensitivity and specificity)

9 Pancholi et al., 2018 J Clin Microbiol 56:e Paper 2

10 Accelerate Pheno System Performs microbial identification and phenotypic antimicrobial susceptibility testing (AST) directly from positive blood culture broths Identification within 90 min using fluorescence in situ hybridization E. coli, piperacillin-tazobactam MIC value = 8 μg/ml, susceptible E. coli, piperacillin-tazobactam MIC value = 128 μg/ml, resistant AST within 7 h using time-lapse imaging and analysis of bacterial growth in the presence of antimicrobial (morphokinetic cellular analysis) Image: Malcolm Boswell (Accelerate Diagnostics, Inc.)

11 Study Design 13 sites tested positive blood cultures on the Pheno system using the Phenotest BC kit Fresh patient deidentified blood cultures and seeded (contrived) blood cultures 1940 samples included in the study: fresh seeded Identification performance assessed (compared to VITEK 2) AST performance assessed: - Essential agreement - Categorical agreement - Very major error - Major error - Minor error (broth microdilution or disk diffusion)

12 Gram-positive bacteria: - Staphylococcus aureus - Coagulase-negative staphylococci (CoNS) - Staphylococcus lugdunensis - Enterococcus faecium - Enterococcus faecalis - Streptococcus species Results Gram-negative bacteria: - Escherichia coli - Klebsiella species - Enterobacter species - Proteus species - Citrobacter species - Serratia marcescens - Pseudomonas aeruginosa - Acinetobacter baumannii Yeast: - Candida albicans - Candida glabrata Overall identification performance: - Sensitivity, 97.5% - Specificity, 99.5%

13 Results Overall Gram-positive AST performance: - Essential agreement (EA), 97.6% ( 90%) - Categorical agreement CA, 97.9% ( 90%) - Very major error (VME) rate, 1.0% ( 1.5%) - Major error (ME) rate, 0.7% ( 3.0%) - Minor error (me) rate, 1.3% meca/c-mediated -lactam resistance: - S. aureus: CA, 99.5% (1 ME) - CoNS: CA, 96.8% (1 ME, 5 VME [4 VMEs resolved]) - S. lugdunensis: CA, 100% Inducible clindamycin resistance: - S. aureus: not claimed (high ME and VME) - CoNS: CA, 97.8% (2 ME, 1 VME) - S. lugdunensis: CA, 100%

14 Overall Gram-negative AST performance: - Essential agreement (EA), 95.4% ( 90%) - Categorical agreement CA, 94.3% ( 90%) - Very major error (VME) rate, 0.5% ( 1.5%) - Major error (ME) rate, 0.9% ( 3.0%) - Minor error (me) rate, 4.8% Results

15 Paper 3 (MRSA) Kriegeskorte et al., 2018 J Clin Microbiol 56:e Commentary: Ford, 2018 J Clin Microbiol 56:e

16 mecc mecc encodes alternative -lactam resistance determinant (encodes a penicillin-binding protein 2a variant) Initially detected in livestock-associated MRSA Subsequently described in livestock-, wildlife-, and humanassociated MRSA: capable of causing human infection meca-mediated detection methods fail to detect meccharboring S. aureus due to low nucleotide and protein identity (70% and 63%, respectively) García-Álvarez et al., 2011 Lancet Infect Dis 11: Paterson et al., 2014 Trends Microbiol 22:42-47 Peterson et al., 2013 Clin Microbiol Infect 19:e16-22

17 Study Design 111 meccpositive S. aureus isolates 3 automated AST platforms: - Phoenix - MicroScan WalkAway - Vitek 2 5 chromogenic MRSA screening agars Cefoxitin disk diffusion Oxacillin broth microdilution

18 Results Very few isolates resistant to both cefoxitin and oxacillin Cefoxitin-resistant/oxacillin-susceptible most frequent AST pattern observed - could be a diagnostic clue for mecc When considering resistance to either cefoxitin or oxacillin: - Phoenix, 72/111 (64.9%) isolates tested resistant - MicroScan WalkAway, 108/111 (97.3%) isolates tested resistant - Vitek 2, 102/111 (91.9%) isolates tested resistant

19 Results All isolates detected by the 5 commercially available chromogenic MRSA screening agar For 3 media, a small percentage ( %) of isolates exhibited reduced growth (smaller colonies), but with the correct color change indicated for MRSA

20 Results Cefoxitin disk diffusion: 100% (111/111 isolates) resistant Oxacillin broth microdilution: 61.3% (68/111 isolates) resistant (38.7% [43/111 isolates] susceptible) Cefoxitin-resistant/oxacillin-susceptible most frequent AST pattern observed - could be a diagnostic clue for mecc García-Álvarez et al., 2011 Lancet Infect Dis 11: Paterson et al., 2014 Trends Microbiol 22:42-47 Peterson et al., 2013 Clin Microbiol Infect 19:e16-22

21 Detection of mecc-positive S. aureus Using PBP2a Immunoassays Dupieux et al., 2017 J Clin Microbiol 55:1961-3

22 Detection of mecc-positive S. aureus Using PBP2a Immunoassays Agglutination-based: PBP2 LAT (Ovoid, ThermoFisher Scientific) Lateral flow-based: PBP2a CCT (Alere Scarborough, Inc.)

23 Results Upon cefoxitin induction, PBP2a CCT test, 100% sensitivity But, cefoxitin-induction not described in the package insert PBP2a CCT discontinued and replaced with PBP2a SA CCT (Abbott)

24 Results During nasal screening, recovered phenotypically characterized MRSA meca- and mecc-negative (also negative for SCCmec-orfX junction) Positive for mecb mecb previously described in Macrococcus caseolyticus but not staphylococcal isolates Plasmid encoded Becker et al., 2018 Emerg Infect Dis 24:242-8

25 Not all S. aureus isolates are S. aureus? Shut the Front Door! Tong et al., 2015 Int J Syst Evol Microbiol 65:15-22

26 Chen et al., 2018 Int J Antimicrob Agents 52: Staphylococcus argenteus

27 It s Enough to mec you Crazy!

28 Liesman et al., 2018 J Clin Microbiol 56:e Naccache et al., 2018 J Clin Microbiol 56:e Dien Bard and Alby, 2018 J Clin Microbiol 56:e Paper 4

29 Study Design 291 residual CSF specimens Specimens tested by routine methods FilmArray meningitis/ encephalitis (M/E) panel 76 CSF specimens from the pre-vaccine era (characterized as bacteria positive)

30 Results Routine testing methods: - Bacterial culture - Fungal culture - Immunoassays (including cryptococcal antigen test) - Individual real-time PCR Overall positive percent agreement (PPA) of M/E panel - All routine testing, 85.6% (249/291) - Excluding C. neoformans/gattii results, 92.5% (223/241) PPA for various targets: - Viral, 90.1% (145/161) - Bacterial, 97.5% (78/80) - C. neoformans/gattii, 52% (26/50)

31 Results Poor agreement between M/E panel (nucleic acid target) and lateral flow cryptococcal antigen (CrAg) assay (antigen target) for detection of C. neoformans/gattii: PPA, 52.0% CrAg present for longer periods of time compared to organism/nucleic acid

32 Results When M/E panel compared to results of routine fungal culture and smear for 20 specimens (from 15 subjects), sensitivity: 92.8% 9 CSF specimens collected prior to antifungal therapy, 11 collected from patients on antifungal therapy

33 Conclusions M/E offers rapid (~60 min) syndrome-based approach for detection of select meningitis and encephalitis pathogens As with any diagnostic assay, take the whole clinical picture (e.g., other test results, clinical presentation) into account! More data required on performance of the M/E panel compared to CrAg, especially with specimens collected from patients at time of initial diagnosis of cryptococcal meningitis If your laboratory has implemented the M/E panel, ensure CrAg is still an orderable test for CSF studies

34 Hong et al., 2018 Diag Microbiol Infect Dis 92:210-3 Paper 5

35 Cell-Free DNA (cfdna) Extracellular DNA fragments released following cell death (lysis) Includes human and microbial DNA Half-life of cfdna in circulation is <2 h, suited to diagnose active infections where ongoing dynamics between pathogen and immune system High-throughput sequencing of microbial cfdna in plasma can identify pathogens

36 Cell-Free DNA (cfdna) Bacteria +750 DNA Viruses +100 Fungi +300 Other Eukaryotes +50 cfdna in plasma Fetal DNA Transplanted Tissue DNA Tumor DNA Pathogen DNA Image credit: Karius

37 Study Design 9 patients with confirmed invasive fungal disease Fungal Infection Sources: - Lung - Lymph node - Heart - Brain - Sternum - Small bowel Surplus EDTA plasma collected Highthroughput sequencing of cfdna

38 Results Full agreement, 55.6% (5/9) cases

39 Results Full agreement, 55.6% (5/9) cases Partial agreement, 22.2% (2/9) cases

40 Results Full agreement, 55.6% (5/9) cases Partial agreement, 22.2% (2/9) cases No agreement, 22.2% (2/9 cases)

41 Cybulski et al., 2018 Clin Infect Dis 67: Paper 6

42 Study Design Aims: - Determine whether patients with GI pathogens detected by multiplex PCR had comparable clinical characteristics to those diagnosed by conventional methods/culture (is PCR too sensitive for detecting GI pathogens?) - Measure impact of rapid diagnostics on clinical decisionmaking and therapy 9 mo prospective, multi-center study Stool specimens tested in parallel with FilmArray gastrointestinal (GI) panel and conventional methods Conventional methods: - Stool culture - O&P - Antigen tests - Viral PCR Laboratory and medical records reviewed Test turnaround time, clinical features, nature and timing of clinical decisions recorded

43 Results GI panel detected 1 pathogens (bacterial, viral, or parasitic) in 669/1887 (35.3%) of specimens When considering conventionally cultured stool pathogens: detected by stool culture detected by GI panel Compared to stool culture, the GI panel detected more: - Campylobacter species - Shigella - Shiga-like toxin-producing E. coli - Plesiomonas shigelloides - Yersinia enterocolitica

44 Improved clinical sensitivity of GI panel compared to conventional stool testing methods for detection of bacterial, parasitic and viral etiologies of GI disease Results Bar chart showing aggregated total for all GI pathogens detected by a particular conventional method, compared with the number of same organisms detected by the GI panel

45 Results Median time from collection to result was 18 h for GI panel and 47 h for culture Median time from collection to initiation of antimicrobial therapy was 22 h for GI panel and 72 h for culture Positive STEC results reported 47 h faster with GI panel and facilitated discontinuation of antimicrobials Patients diagnosed by GI panel more likely to receive targeted rather then empirical therapy than those diagnosed by culture Graph showing total cases where antimicrobials were prescribed based upon test results (targeted therapy) versus antimicrobials prescribed prior to having test results available (empirical therapy): measured on a monthly basis following implementation of the GI panel

46 Conclusions Clinical characteristics of GI panel and culture positive cases are comparable in nearly all respects PCR is not too sensitive PCR detected large number of patients with diarrheagenic E. coli: EAEC, EPEC and ETEC, that are not detected by conventional laboratory testing methods Patients with diarrheagenic E. coli have more prolonged symptoms, but are otherwise comparable to other patients with acute gastroenteritis and can benefit from treatment Multiplex PCR testing for GI pathogens increases sensitivity and speed of reporting, expands range of pathogens that can be detected, and provides clinically relevant results that impact management

47 Thoendel et al., 2018 Clin Infect Dis In Press Street et al., 2017 J Clin Microbiol 55: Paper 7

48 Study Design 408 sonicate fluids from resected arthroplasties Samples defined as PJI or aseptic failure based on IDSA criteria Sonicate fluid samples: PJI Aseptic failure Samples enriched for microbial DNA, whole-genome amplified, sequenced Data analyzed with 2 analytic platforms and compared to culture results

49 Results Detection of organisms in samples from uninfected aseptic failure rare Good agreement between culture-positive PJI group and metagenomics (86.1% of cases yielded identical results) For PJI culture-negative group, 56.1% of cases generated identical findings; metagenomics detected organisms in 43.9% of cases!

50 Results Many typically considered agents of PJI!

51 Results When comparing metagenomic data to all available culture data (e.g., intraoperative tissue, etc. taken anytime prior to surgery): an additional 31 PJI culture-positive cases ( ) Even when examining additional culture data, metagenomics identified potential pathogens in 31.3% of PJI culture-negative cases Authors suggest one could perform metagenomics of sonicate fluid at the point the initial PJI culture is negative

52 Acknowledgements M. Binnicker E. Burd C-A. Burnham K. Carroll C. Doern P. Edelstein F. Fang D. Green M. Holodniy R. Humphries S. Jenkins J. Kirby R. Liesman A. McAdam R. Patel M. Satlin T. Simner K. Smith E. Theel M. Thoendel

53 THANK YOU! The NYPH-WCMC Clinical Microbiology Laboratory Staff