P-236. Impact of a New Dressing Protocol for the Prevention of Central Line-Associated Bloodstream Infections in one Neonatal Intensive Care Unit (NICU)
Bibliographic record
Abstract
Abstract Background Newborns in the neonatal intensive care unit (NICU) are vulnerable to infections due to their extremely fragile skin, underdeveloped immune systems, and usual long-term hospitalizations. These infants often require prolonged use of central venous catheters (CVCs). Bloodstream infections can arise from these CVCs and are a major cause of death in the NICU. Every CVC dressing change can damage their fragile skin, increasing infection risk, and potentially resulting in central line-associated bloodstream infections (CLABSIs). Centers for Disease Control and Prevention recommends dressing changes every seven days, more often if visibly damaged or dirty. Our objective was to evaluate the effectiveness of a new protocol and dressing optimized for long-term skin adhesion implemented in the Centre Hospitalier Universitaire Sainte-Justine (CHUSJ) NICU. Methods The CHUSJ NICU is a level III-IV NICU with 900 admissions a year from both inborn and outborn infants. CLABSI surveillance is done prospectively by the Infection Prevention and Control team. All CVCs installed in NICU patients are recorded daily. Inclusion criteria were all recorded NICU CVCs from January 1, 2017, to December 31, 2023. Segmented interrupted time-series analysis was conducted according to the three-stage intervention rollout (first for gestational age of 28 weeks or more, then large premature infants, then the entire unit), with a pre-intervention period of January 1, 2017, to May 1, 2021, modeling CLABSIs/1000 CVC days/month and adjusting for mean birth weight. Results 2653 CVCs and 71 CLABSIs were reported during the surveillance period. Pre-intervention rates were 1.85 CLABSIs/1000 CVC days. CLABSI rate ratios were: during initial intervention rollout 0.448 (95% confidence interval 0.407, 0.492), during the second stage 0.952 (0.932, 0.973), and since complete implementation 0.610 (0.581, 0.639) that of pre-intervention period rates, for the entire NICU population. Conclusion With reports of CLABSI rate rebounds during the COVID-19 pandemic and increasing NICU outbreaks from multidrug-resistant organisms with associated high mortality, new infection prevention protocols are crucial to combat these trends in CLABSI rates and severity of case outcomes, especially for the vulnerable NICU population. Disclosures All Authors: No reported disclosures
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".