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Record W4403645761 · doi:10.1128/spectrum.02276-24

Multi-year comparison of VITEK MS performance for identification of rarely encountered pathogenic Gram-negative organisms (GNOs) in a large integrated Canadian healthcare region

2024· article· en· W4403645761 on OpenAlexaffabout
Deirdre L. Church, Thomas P. Griener, Daniel B. Gregson

Bibliographic record

VenueMicrobiology Spectrum · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Identification and Susceptibility Testing
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBacilliClinical microbiology16S ribosomal RNAMicrobiologyMedicineBiologyBacteriaGenetics

Abstract

fetched live from OpenAlex

ABSTRACT This multi-year study (2014–2019) compared identification of rare and unusual Gram-negative organisms (GNOs) by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) (VITEK MS, bioMérieux, Laval Que.) to 16S rRNA gene sequencing (16S) according to our laboratories routine workflow; 16S is done if initial MALDI-TOF MS gave discordant, wrong, or no results. GNB isolates were first analyzed by standard phenotypic methods and MALDI-TOF MS using direct deposit-full formic acid extraction; proteomics was repeated if no result occurred. Medically approved 16S analyses were done using fast protocols. Isolate sequences were analyzed using the Integrated Database Network System (IDNS3) bacterial database (SmartGene, Lausanne, Switzerland). Three hundred thirty-one GNOs including 251 (76%) aerobic Gram-negative bacilli (GNB), 63 (19%) fastidious Gram-negative coccobacilli (fGNCBs), and 17 (5%) Campylobacterales (CAMPB) isolates were recovered from 304 specimens; >1 isolate was recovered from 19 (6%). GNOs were mainly recovered from blood cultures (31.6%) and lower respiratory specimens (43%) (one-half were isolated from cystic fibrosis patients). Accurate genus vs species identities were obtained for 67.7% and 32.5% aerobic GNBs, 73% and 60% fGNCBs, and 23.5% CAMPB (with no discrepant species), respectively. Wrong or no results were obtained for 81 (32.3%) aerobic GNBs, 17 (27%) fGNCBs, and 13 (72.2%) CAMPB. No results or misidentifications occurred for 33% of aerobic GNBs, 26% of fGNCBs, and 76.5% of CAMPB due to absence of species in the instrument’s database. VITEK MS performance remained stable for aerobic GNBs and fGNCBs but improved for CAMPB with addition of Campylobacter rectus and Campylobacter curvus to the database. 16S remains important for identification of GNOs when proteomics fails. IMPORTANCE Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has transformed the identification of commonly encountered Gram-negative organisms (GNOs) in the clinical laboratory, but rare and unusual organisms continue to challenge the technology. This study verified performance of VITEK MS for identification of a broad range of rare and unusual clinical GNO isolates by our large reference laboratory workflow over a multi-year period. Although most GNOs were accurately identified by MALDI-TOF MS, a small number of clinical isolates (~1%–6%) required 16S sequencing for identification depending on the GNO category. Approximately one-third of aerobic Gram-negative bacilli (GNBs) and two-thirds of Campylobacterales could not be accurately identified by proteomics due to lack of an organism in the instrument’s database. MALDI-TOF MS databases should be continuously updated and validated, and laboratories should have a workflow for identification of unusual or rarely encountered aerobic, fastidious, and Campylobacterales GNOs that includes 16S rRNA gene sequencing whenever proteomics cannot give a definitive identification.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.283
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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