Reducing ambulatory central line‐associated bloodstream infections: A family‐centered approach
Bibliographic record
Abstract
BACKGROUND: Ambulatory central line-associated bloodstream infections (CLABSIs) cause significant morbidity and mortality, especially in pediatric oncology. Few studies have had interventions directed toward caregivers managing central lines (CL) at home to reduce ambulatory CLABSI rates. We aimed to reduce and sustain our ambulatory CLABSI rate by 25% within 3 years of the start of a quality improvement intervention. PROCEDURE: Plan-do-study-act cycles were implemented beginning April 2016. The main intervention was a family-centered CL care skill development curriculum for external CLs. Training began upon hospital CL insertion, followed by an ambulatory teach-back program to achieve home caregiver CL care independence. Other changes included: standardizing ambulatory nurse CL care practice (audits, a train the nurse trainer process, and workshops for independent home care agencies); developing aids for trainers and caregivers; providing supplies for clean surfaces; wide dissemination of the program; and minimizing opportunities of CLABSI (e.g., standardizing timing of CL removal). The outcome measure was the ambulatory CLABSI rate (excluding mucosal barrier injury laboratory-confirmed bloodstream infection), compared pre intervention (January 2015 to March 2016) to post intervention, including 2 years of sustainability (April 2016 to June 2023), using statistical process control charts. We estimated the total number of CLABSI and associated healthcare charges prevented. RESULTS: The ambulatory CLABSI rate decreased by 52% from 0.25 to 0.12 per 1000 CL days post intervention, achieved within 27 months; 117 CLABSI were prevented, with $4.2 million hospital charges and 702 hospital days avoided. CONCLUSIONS: Focusing efforts on home caregivers CL care may lead to reduction in pediatric oncology ambulatory CLABSI rates.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".