Compliance With Central Line Maintenance Bundle and Infection Rates
Bibliographic record
Abstract
BACKGROUND: Reliable bundle performance is the mainstay of central line-associated bloodstream infections (CLABSI) prevention despite an unclear relationship between bundle reliability and outcomes. Our primary objective was to evaluate the correlation between reported bundle compliance and CLABSI rate in the Solutions for Patient Safety network. The secondary objective was to identify which hospital and process factors impact this correlation. METHODS: We examined data on bundle compliance and monthly CLABSI rates from January 11 to December 21 in 159 hospitals. The correlation (adjusting for temporal trend) between CLABSI rates and bundle compliance was done at the network level. Negative binomial regression was done to detect the impact of hospital type, central line audit rate, and adoption of a comprehensive safety culture program on the association between bundle compliance and CLABSI rates. RESULTS: During the study, hospitals reported 27 196 CLABSI on 20 274 565 line days (1.34 CLABSI/1000 line days). Out of 2 460 133 observed bundle opportunities, 2 085 700 (84%) were compliant. There was a negative correlation between the monthly bundle reliability and monthly CLABSI rate (-0.35, P <.001). After adjusting for the temporal trend, the partial correlation was -0.25 (P = .004). On negative binomial regression, significant positive interaction was only noted for the hospital type, with Hospital Within Hospital (but not freestanding children's hospitals) revealing a significant association between compliance ≥95% and lower CLABSI rates. CONCLUSIONS: Adherence to best practice guidelines is associated with a reduction in CLABSI rate. Hospital-level factors (hospitals within hospitals vs freestanding), but not process-related (central line audit rate and safety culture training), impact this association.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".