Identification of Blood Culture Isolates Directly from Positive Blood Cultures by Use of Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry and a Commercial Extraction System: Analysis of Performance, Cost, and Turnaround Time
Bibliographic record
Abstract
Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry represents a revolution in the rapid identification of bacterial and fungal pathogens in the clinical microbiology laboratory. Recently, MALDI-TOF has been applied directly to positive blood culture bottles for the rapid identification of pathogens, leading to reductions in turnaround time and potentially beneficial patient impacts. The development of a commercially available extraction kit (Bruker Sepsityper) for use with the Bruker MALDI BioTyper has facilitated the processing required for identification of pathogens directly from positive from blood cultures. We report the results of an evaluation of the accuracy, cost, and turnaround time of this method for 61 positive monomicrobial and 2 polymicrobial cultures representing 26 species. The Bruker MALDI BioTyper with the Sepsityper gave a valid (score, >1.7) identification for 85.2% of positive blood cultures with no misidentifications. The mean reduction in turnaround time to identification was 34.3 h (P < 0.0001) in the ideal situation where MALDI-TOF was used for all blood cultures and 26.5 h in a more practical setting where conventional identification or identification from subcultures was required for isolates that could not be directly identified by MALDI-TOF. Implementation of a MALDI-TOF-based identification system for direct identification of pathogens from blood cultures is expected to be associated with a marginal increase in operating costs for most laboratories. However, the use of MALDI-TOF for direct identification is accurate and should result in reduced turnaround time to 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 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 | 0.003 | 0.007 |
| 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 | 0.001 |
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".