35 Identifying and addressing improvement opportunities in the care of children with febrile neutropenia: a quality improvement initiative
Bibliographic record
Abstract
Abstract Background Management of febrile neutropenia in previously healthy, presumed immunocompetent children varies. Overtreatment impacts the patients, families, and the healthcare system. With guidance from a Canadian Paediatric Society (CPS) Practice Point, most well-appearing children with an episode of febrile neutropenia can be managed with reduced exposure to antibiotics and close outpatient follow-up. Objectives The aim of this initiative was to safely reduce antibiotic use in this low-risk patient population presenting to the emergency department (ED). Design/Methods A multidisciplinary team designed a quality improvement (QI) initiative from July 2023 – July 2024. Treatment with antibiotics was classified as indicated or non-indicated according to guidance from the CPS Practice Point. Patient management from the year prior to guideline publication (baseline) was compared to the intervention period. Interventions involved guideline dissemination, provider education, and point of care tools to facilitate clinical decision-making and follow-up. Outcome measures included the proportion of children receiving antibiotics, hospital admission, and appropriate laboratory follow-up. Re-presentation to ED and missed serious bacterial infections (SBI) were monitored as balancing measures. Outcomes were evaluated using descriptive statistics and statistical process control (SPC) charts. Results Three hundred and ninety-eight (398) children with febrile neutropenia were included. The proportion of non-indicated antibiotics was 6.7% at baseline. Due to the low baseline use of non-indicated antibiotics, an SPC was used to detect special cause variation. Special cause was demonstrated with 97 consecutive cases occurring without error (non-indicated antibiotics). Following the occurrence of special cause variation until the end of the study, only 1.6% of children received non-indicated antibiotics. There was no increase in re-presentations to ED (9% vs 8%; p= 0.65) nor missed SBI (0% vs 0%). Influenza was prevalent during the intervention period, accounting for 71% of positive viral tests (n=64/90) in children with febrile neutropenia. Severe neutropenia resolved in 90% of evaluable children, with a median duration of 44 days. Four children had persistent neutropenia and were referred to Paediatric Hematology; all were diagnosed with benign or immune neutropenia. Conclusion QI methodology can facilitate the timely adoption of best practices to align local clinical care with new national guidelines. Implementation of the CPS recommendations allowed low-risk children with febrile neutropenia to avoid unnecessary antibiotic exposure and hospital admission. This work demonstrates an excellent opportunity for ED providers and paediatricians to reduce low-value treatment while simultaneously enhancing patient- and family-centered care.Figure 1.(A) P chart: Administration of non-indicated antibiotics in patients presenting to the Alberta Children’s Hospital ED with fever and neutropenia, presented as a proportion of the total patients per month. (B) G chart: Number of cases between errors (error = receipt of non-indicated antibiotics) among patients presenting to the Alberta Children’s Hospital ED with fever and neutropenia. The mean from the baseline period was used as the center line, with upper (UCL) and lower control limits (LCL) at 3-sigma above and below the mean.
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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 | 0.039 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".