Novel blood protein biomarkers for distinguishing bacterial from non-bacterial infection in children: a case–control study
Bibliographic record
Abstract
Abstract Background Distinguishing bacterial from viral or inflammatory conditions in hospitalised children is clinically challenging due to overlapping clinical features and limitations of existing diagnostics. Culture-based tests are slow, and commonly used biomarkers such as C-reactive protein (CRP) lack sufficient accuracy, contributing to empirical antibiotic use and antimicrobial resistance. We aimed to evaluate the diagnostic performance of novel blood protein biomarkers for bacterial infection in children admitted with infection or inflammatory illness. Methods We performed a prospective case–control study of children ≤16 years admitted to the Royal Belfast Hospital for Sick Children (2020–2023) with bacterial infection, viral infection or inflammatory illness. Plasma samples were analysed for previously reported novel biomarkers and signatures (LCN2; TRAIL+IP-10+CRP; E-selectin+IL18+ NCAM1+LCN2+IFN-γ+LG3BP and E-selectin+IL18+ NCAM1+LCN2+IFN-γ). The diagnostic accuracy of individual biomarkers and biomarker signatures was assessed using ROC curves. Results Fifty-two children were included (13 bacterial, 20 viral, 19 inflammatory). All evaluated biomarker signatures and LCN2 distinguished bacterial from viral infection (AUC 0.819– 0.935), with the TRAIL+IP-10+CRP signature achieving the highest accuracy (AUC 0.935). In bacterial–inflammatory comparisons, performance was lower (AUCs 0.607–0.745); the E-selectin+IL-18+NCAM1+LCN2+IFN-γ signature performed best (AUC 0.745) and outperformed CRP (AUC 0.595). A novel three-protein signature (E-selectin+LCN2+IFN-γ) had a significantly higher AUC than CRP for distinguishing bacterial infections from non-bacterial (viral and inflammatory combined). Conclusions Several novel host protein biomarkers and signatures had a higher diagnostic accuracy than CRP for differentiating bacterial from non-bacterial illness (viral and inflammatory) in hospitalised children. These findings support the potential of biomarker-guided diagnostics to improve accuracy and facilitate earlier antibiotic de-escalation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".