Performance of direct-from-blood culture bottle rapid phenotypic antimicrobial susceptibility testing for Gram-negative bacteremia at a children’s hospital
Bibliographic record
Abstract
Abstract Background Rapid and accurate antimicrobial susceptibility testing (AST) is essential for managing Gram-negative bacteraemia. However, a major limitation of conventional phenotypic AST is its slow turnaround time, delaying targeted therapy. Objectives To evaluate the performance and turnaround time of direct-from-blood culture AST using an automated phenotypic system in paediatric patients with Gram-negative rod bacteraemia. Methods We retrospectively reviewed 135 positive blood cultures with Gram-negative rods from a tertiary paediatric hospital between January 2021 and December 2023. Blood cultures that yielded polymicrobial organisms were excluded from the study. Direct AST was performed by preparing bacterial suspensions directly from positive blood culture broth and analyzed using an automated phenotypic AST system (BD Phoenix™ M50). Conventional AST from isolated colonies served as the reference. Essential agreement (EA), categorical agreement (CA), and error rates [very major error (VME), major error (ME), minor error (mE)] were assessed. Time to AST result was compared between methods. Results The direct AST method reduced the median turnaround time by 24.0 h compared with conventional AST (P < 0.0001). Overall EA and CA were 99.5% and 99.6%, respectively. No VMEs were observed. ME and mE rates were low at 0.25% and 0.25%, respectively, with discrepancies tending to indicate greater resistance by direct AST. Conclusions Direct-from-blood culture AST using an automated phenotypic system provides rapid, accurate susceptibility results in paediatric Gram-negative bacteraemia. This approach enables earlier reporting and may improve clinical outcomes and antimicrobial stewardship without additional resource burden.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".