Comparison of Accelerate PhenoTest BC Kit and MALDI-TOF MS/VITEK 2 System for the rapid identification and antimicrobial susceptibility testing of gram-negative bacilli causing bloodstream infections
Bibliographic record
Abstract
Abstract Introduction: Our laboratory uses MALDI-TOF MS (MALDI) and the VITEK®2 system (DV2) directly from positive blood cultures (BC) for organism identification (ID) and antimicrobial susceptibility testing (AST). Our objective was to compare direct MALDI/DV2 to a commercial BC ID/AST platform, the Accelerate Pheno™ system, in the ID/AST of clinical and seeded BC positive for Gram-negative bacilli (GNB). Methods: BC positive for GNB were collected over a three month period and tested using AXDX, direct MALDI/DV2, and compared to conventional methods. A subset of sterile BC were seeded with multi-drug resistant GNB. Discrepancies in very major errors (VME) and major errors (ME) were confirmed with broth microdilution. Results: A total of 29 clinical samples and 35 seeded samples were analyzed. Direct MALDI had a higher ID failure rate (31.0%) compared to AXDX (3.4%) [p<0.001]. Time to ID/AST was 1.5/6.9 hours, 5.8/16.5 hours and 21.6/33.0 hours for AXDX, direct MALDI/DV2 and conventional methods, respectively (p<0.001). For clinical samples, AXDX and DV2 had essential agreement (EA)/categorical agreement (CA) >96%. For seeded samples, AXDX had EA, CA, VME, ME and mE of 93.2%, 89.0%, 2.2%, 0%, and 9.2%, respectively. AXDX had a large number of non-reports (6.1%), stemming from meropenem testing. DV2 had EA, CA, VME, ME and mE of 97.5%, 94.7%, 1.3%, 0%, and 4.4%, respectively. Conclusions: AXDX had fewer ID failures and more rapid ID/AST results. Direct MALDI/DV2 and AXDX both had high agreement for clinical samples but direct MALDI/DV2 had higher agreement when challenged with MDR GNB.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".