122 Comparing the External Validity of Clinical Prediction Tools Incorporating Serum Procalcitonin to Identify Febrile Infants (0-90 days) at Low Risk for Serious Bacterial Infection: A Retrospective Analysis
Bibliographic record
Abstract
Abstract Primary Subject area Hospital Paediatrics Background Procalcitonin (PCT), a serum inflammatory biomarker, has recently been incorporated into several clinical decision tools to identify febrile infants at low risk for serious bacterial infection (SBI). These include the Pediatric Emergency Care Applied Research Network (PECARN) tool, the “Step-by-Step” approach, and the “Laboratory-Score.” Our institution is one of a few in Canada to incorporate serum PCT routinely, allowing us to complete these clinical decision tools. Thus, the objectives of this study were to externally validate and compare these tools in a Canadian pediatric population, indirectly assessing the utility of serum PCT in clinical practice. Objectives The primary outcomes were to derive the sensitivity, specificity, and negative predictive value (NPV) of each stratification tool in predicting SBI. Design/Methods We retrospectively reviewed the medical records of all infants less than 90 days of age presenting to our emergency departments between April 2016 and October 2019 with fever without a source, who had sufficient investigations to apply one (or more) of the above clinical decision tools. Results We applied the PECARN tool to 51 cases, and had sufficient data to apply the Step-by-Step and Lab Score criteria to 43 of these patients. Seventeen of the 51 patients (33%) were identified to have a SBI. The PECARN and Step-by-Step tools both had NPV of 100%; both were sensitive enough to detect all patients with SBI. They had poor specificity (0.47 and 0.55 respectively). These two tools were in agreement in 38 of 43 (88%) cases. Though the Laboratory-Score had the highest positive predictive value (0.88) and specificity (0.85), it failed to identify 3 of 16 true cases of SBI and had a suboptimal sensitivity of 0.81. Conclusion The ability to identify febrile infants at low risk for SBI in a reliable way would have significant clinical potential to change practice. Given the strong NPV of both the PECARN and Step-by-Step tools, we conclude that their use, incorporating the measurement of serum PCT, may be of use in reducing pediatric hospitalization, use of empiric broad-spectrum antibiotics, and investigations such as lumbar punctures, in these low-risk patients. This study had a small sample size. We look forward to analyzing a larger population of febrile infants, particularly in infants of a chronologic age (28-90 days) more amenable to clinical practice change.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".