Utility of Prior Cultures in Predicting Antibiotic Resistance of Bloodstream Infections Due to Gram-negative Pathogens
Bibliographic record
Abstract
Appropriate empiric antibiotic therapy in patients with bloodstream infections due to Gram-negative pathogens can improve outcomes. The utility of prior microbiologic results for determining empiric treatment in blood stream infections is unclear. We performed a multi-center cohort study of patients with Gram-negative bacteremia from April 2010 to March 2015 at hospitals in Canada and the United States. We analyzed univariate and multivariable models for predictors of antibiotic resistance. Test characteristics of prior non-screening Gram-negative cultures for determining the resistance of current bloodstream isolates were determined for different patient characteristics. Among 1,832 patients with Gram-negative bloodstream infection, 28% (504/1,832) of patients had a documented prior Gram-negative organism from a non-screening culture within the past 12 months. A most-recent prior Gram-negative organism resistant to a given antibiotic was strongly predictive of the current organism’s resistance to the same antibiotic. The overall specificity was 0.92 (95% CI:0.91–0.93) and positive predictive value was 0.66 (95% CI:0.61–0.70) for predicting antibiotic resistance. Specificities and positive predictive values ranged from (0.77 to 0.98) and (0.43 to 0.78) across different antibiotics, organisms, and patient subgroups. Increasing time between cultures was associated with a decrease in positive predictive value but not specificity. An heuristic based on a prior resistant Gram-negative could have been applied to 1 in 4 patients, and in these patients would have changed therapy in 1 in 5. In patients with confirmed or suspected bloodstream infection with a Gram-negative organism, identification of a most-recent prior isolate resistant to a drug of interest (within the last 12 months) is highly specific for resistance and should preclude the use of that antibiotic. All authors: No reported disclosures.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".