107 Addition of CRP and Respiratory Viral Testing for Risk-Stratification of Febrile Young Infants
Bibliographic record
Abstract
Abstract Primary Subject area Emergency Medicine - Paediatric Background Febrile young infants are frequently brought for medical attention. Although most have viral illnesses, approximately 10% have serious bacterial infections (SBIs) including urinary tract infections, bacteremia or bacterial meningitis. Risk-stratification must have high sensitivity given the consequences of a missed SBI diagnosis. The most widely used risk-stratification criteria rely upon the serum white blood cell count (WBC) and urinalysis (UA), but predate the widespread use of CRP and respiratory virus testing (RVT), both of which offer enhanced ability to detect high-risk infants. CRP and RVT have never been evaluated together or in combination with traditional biomarkers, particularly for the detection invasive bacterial infections (IBI), bacteremia and meningitis. Objectives Compare the diagnostic utility of CRP and RVT in combination with WBC and UA for risk-stratification of febrile young infants. Design/Methods This was a retrospective study of healthy term febrile infants ≤ 90 days old evaluated in an urban tertiary Pediatric Emergency Department from 2010-2018. Only infants with both blood and urine cultures were included. Test characteristics for detecting SBI and IBI were calculated for tests individually and in composite. Results Of 3461 included infants, 442 (12.7%) had SBIs and 56 (1.6%) had IBIs. Overall use of CRP (25%) and RVT (78%) increased over time, and 698 (22%) infants had complete testing with UA, WBC, CRP and RVT. When considering individual diagnostic tests (Table 1), risk of SBI with a normal CRP (1.4%), negative UA (0.18%) or positive RVT (2.6%) was lower than with a normal WBC (6.7%) alone. Risk of IBI was lowest among infants with a positive RVT (0.19%; vs 0.57%, 0.59%, 0.70% for CRP, UA, WBC respectively), and RVT performed best for detection of IBI (Sn 0.857; NPV 0.996). When risk-stratifying infants using tests in combination (Table 2), 43/3339 (1.3%) infants had a SBI despite meeting low-risk criteria with a normal WBC and UA (Sn 0.900; NPV 0.980), compared to 4/698 (0.57%) with a normal WBC, UA, CRP and positive RVT (Sn 0.951; NPV 0.981). IBI was present in 10/3339 (0.3%) infants stratified as low-risk by a normal WBC and UA (Sn 0.818; NPV 0.995), compared to 0/698 (0%) with a normal WBC, UA, CRP and positive RVT (Sn 1.00; NPV 1.00). Subanalysis restricted only to infants with complete testing yielded similar results. Conclusion This is the first study to compare use of CRP and RVT in addition to traditional biomarkers for febrile young infants. Use of these widely available tests in combination may improve risk-stratification to reduce unnecessary invasive testing and hospitalizations. Prospective efficacy and cost-effectiveness studies are warranted.
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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 | 0.001 |
| 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".