Multi-year comparison of VITEK MS performance for identification of rarely encountered pathogenic gram-positive organisms (GPOs) in a large integrated Canadian healthcare region
Bibliographic record
Abstract
ABSTRACT This multi-year study (2014–2019) compared identification of rare and unusual gram-positive organisms (GPOs) by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) (VITEK MS; bioMérieux, Laval, Quebec) to 16S rRNA gene sequencing (16S) according to our laboratory routine workflow. 16S is done if initial MALDI-TOF MS results are discordant or wrong, or there are no results. GPO isolates were first analyzed by standard phenotypic methods and MALDI-TOF MS using direct deposit with full formic acid extraction; MALDI-TOF 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 bacterial database (SmartGene, Lausanne, Switzerland). 655 GPO isolates were recovered from 648 specimens; >1 isolate was recovered from 7 (1%). A total of 451 (68.9%) aerobic gram-positive bacilli (GPBs) and 204 (31.1%) aerobic gram-positive cocci (GPCs) were mainly recovered from bloodstream infections (35%), sterile fluids and deep tissues (35%), and abscesses/deep wounds (17%). Accurate genus vs species identities were obtained for 59% and 49.4% GPB, and 81% and 53.9% GPC, respectively. Wrong or no results were obtained for 9% and 31% of GPB and 7% and 12% of GPC; 15% of GPBs and 5.3% of GPC identification errors occurred due to absence from the instrument’s database. VITEK MS performance remained stable for GPB and GPC isolates due to few species additions to the database. VITEK MS databases need to be continually updated to include an increasing number of rare and unusual GPOs causing invasive human infections. 16S remains important for identification of GPOs where MALDI-TOF fails. IMPORTANCE MALDI-TOF MS has transformed the identification of commonly encountered GPOs in the clinical laboratory, but rare and unusual bacteria continue to challenge the technology. This study verified the performance of VITEK MS for identification of a broad range of rare and unusual clinical GPO isolates by our large reference laboratory workflow over a multi-year period. Although most GPOs were accurately identified by MALDI-TOF MS, a small number of common GPC isolates (6.3%) (i.e., Enterococcus / Staphylococcus / Streptococcus ) requiring sequencing for identification were studied. Approximately 13% of aerobic GPBs and 5.3% of GPCs could not be accurately identified by MALDI-TOF 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 the identification of unusual or rarely encountered GPOs that includes 16S rRNA gene sequencing whenever MALDI-TOF cannot give a definitive identification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".