26 Derivation and validation of a clinical decision rule to discriminate bacteremia from contaminants among children with a positive blood culture in the emergency department
Bibliographic record
Abstract
Abstract Background Blood cultures are commonly performed to rule out bacteremia in children seen in the emergency department (ED), which if missed, can progress to sepsis and even death. While a true bacteremia is potentially life-threatening and requires urgent treatment, many cases of positive blood cultures are contaminants leading to unnecessary antibiotic exposures and hospitalizations. Objectives We aimed to derive and validate a clinical decision rule to discriminate bacteremia from contaminants among children seen in the ED with a preliminary positive blood culture. Design/Methods This study includes two retrospective cohorts of children with positive blood cultures from a Canadian paediatric ED from January 2018 until May 2024. The primary outcome was true bacteremia defined using two-step standardized approach based on the bacteria involved and the clinical outcome assessment adjudicated by two reviewers. Predictors of bacteremia were derived from a literature review and a consensus of experts. We used Classification and Regression Tree models to derive a highly sensitive clinical decision rule to distinguish between true bacteremia and contamination. The validity was assessed by measuring the proportion of children with true bacteremia classified at high or moderate risk by the clinical decision rule (sensitivity) and the proportion of contaminants classified at low risk by the rule (specificity). For participants discharged home at the index visit, the clinical utility of the rule was measured by comparing the clinical decision rule to the treating physician's management. Results A total of 574 children, including 285 cases of bacteremia were included in the derivation phase, and 173 (including 83 bacteremia) in the validation cohort. Derived from the final selected model, we were able to classify children into three categories (high, moderate and low risk). Children at high risk of bacteremia were identified based on the initial Gram stain (Gram positive bacteria in pair or chain, or all Gram negative bacteria). In the absence of high-risk Gram stain criteria, children were at moderate risk if they had any one of three risks factors (Culture positive in less than 17 hours; Internal devices; Suspicion of osteo-articular infection). Children without any of the four criteria were classified as low risk. This clinical decision rule demonstrated a sensitivity of 100% (95%CI: 98-100%) and specificity of 65% (95%CI: 59-70%) in the derivation cohort. In the validation cohort, the clinical decision rule demonstrated a sensitivity of 99% (95%CI: 94-100%) and a specificity of 60% (95%CI: 50-70%). Applying the rule to the 43 children initially discharged in the validation cohort would decrease the number of admissions from 34 to 21 without missing a true bacteremia case. Conclusion We created a highly sensitive clinical decision rule to identify true bacteremia among children seen in the ED with a preliminary positive blood culture. The use of this clinical decision rule will decrease unnecessary testing and antibiotics in a subset of patients while ensuring treatment of children at high risk of true bacteremia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".