133 Value-based care for healthy children with first episode of febrile neutropenia
Bibliographic record
Abstract
Abstract Primary Subject area Emergency Medicine - Paediatric Background While the management of febrile neutropenia in patients with cancer has clear, evidence-based guidelines, the management of previously healthy, immunocompetent children with a febrile illness and first episode of neutropenia is less understood. These patients are often similarly treated with empiric antibiotics and hospitalization despite studies demonstrating that this population, if they are well-appearing with a short history of neutropenia, is at low risk of serious bacterial infections. Therefore, less aggressive management should be considered in patients meeting low risk criteria. Objectives The aim of our quality improvement (QI) study was to decrease the number of unnecessary hospitalizations and empiric antibiotics prescribed by 50% over a 12-month period for otherwise healthy, well appearing patients presenting to the emergency department (ED) with a first episode of febrile neutropenia. Design/Methods A team of stakeholders from Hematology, Infectious Disease, Pediatrics and Emergency Medicine was assembled. A review of the literature, peer institutions and local practices of managing febrile neutropenia in healthy children was performed. Using the Model for Improvement, a guideline for the management of healthy children with first episode of febrile neutropenia was developed and refined using PDSA cycles. In January 2020, the guideline was launched for clinical use in the ED. Education, targeted audit and feedback, pathway modifications, and reminders were used to address knowledge gaps and staff turnover. A family of measures was analyzed using run charts and statistical process control (SPC) methods. Results Eighteen months of baseline data identified nineteen low risk patients with 84% either hospitalized and/or received antibiotics. It was also uncovered that many patients were misdiagnosed with neutropenia by excluding bands from the absolute neutrophil count (ANC). After the first twelve months of the intervention, sixteen patients met low risk criteria. Hospitalization and/or antibiotics use for this population decreased to 25% and all blood cultures were negative. Recognition of true severe febrile neutropenia also improved. Forty-one patients had a neutrophil count < 0.5, but an ANC > 0.5. Hospitalization and/or antibiotics use for this population decreased from 52% to 10%. Conclusion Through a multi-faceted, multidisciplinary QI study, we improved resource stewardship and value-based care by reducing unnecessary hospitalizations and antibiotics in low risk patients with a first episode of febrile neutropenia. Next steps include iterations to the guideline to increase impact along with sustainability planning. This work can easily be adopted by other pediatric and community sites caring for children.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".