Models to predict bacteremia in the emergency department: a systematic review
Bibliographic record
Abstract
OBJECTIVES: Blood cultures are ordered in emergency departments for 15% of patients with suspected infection. The diagnostic yield varies from 2% to 20%. Thirty-day mortality in patients with bacteremia is high, doubling or tripling the rate in patients with the same infection but without bacteremia. Thus, finding an effective model to predict bacteremia that is applicable in emergency departments is an important goal. Shapiro's model is the one traditionally used as a reference internationally. The aim of this systematic review was to compare the predictive power of bacteremia risk models published since 2008, when Shapiro's model first appeared. MATERIAL AND METHODS: We followed the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, searching in the following databases for articles published between January 2008 and May 31, 2023: PubMed, Web of Science, EMBASE, Lilacs, Cochrane, Epistemonikos, Trip Medical Database, and ClinicalTrials.gov. No language restrictions were specified. The search terms were the following Medical Subject Headings: bacteremia/bacteraemia/blood stream infection, prediction model/clinical prediction rule/risk prediction model, emergencies/emergency/emergency department, and adults. Observational cohort studies analyzing diagnostic yield were included; case-control studies, narrative reviews, and other types of articles were excluded. The Newcastle-Ottawa Scale was used to score quality and risk of bias in the included studies. The results were compared descriptively, without meta-analysis. The protocol was included in the PROSPERO register (CRD42023426327). RESULTS: Twenty studies out of a total of 917 were found to meet the inclusion criteria. The included studies together analyzed 33 182 blood cultures, which detected 5074 cases of bacteremia (15.3%). Eleven studies were of high quality, 7 of moderate quality, and 2 of low quality. The area under the receiver operating characteristic curve (AUC) of Shapiro's model varied from 0.71 to 0.83. Sensitivity was as high as 98%, and specificity ranged from 26% to 69%. Three models with high scores for quality were also supported by both internal and external validation studies: Lee's model (AUC, 0.81; sensitivity 68%; specificity, 81%), the 5MPB-Toledo model (AUC, 0.906 to 0.946), and the MPB-INFURG-SEMES model (AUC, 0.924; sensitivity, 97%; specificity, 76%. CONCLUSION: The 5MPB-Toledo and MPB-INFURG-SEMES are useful for assessing the true risk of bacteremia in patients attended in emergency departments.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".