2 Successful intervention to reduce central line-associated bloodstream infection rate in adult intensive care unit at a specialized tertiary care hospital in riyadh, saudi arabia
Bibliographic record
Abstract
Background Central line-associated bloodstream infection (CLABSI) surveillance in the adult intensive care unit (ICU) in King Abdullah Specialized Children Hospital showed a higher CLABSI rate during the first quarter of 2018. CLABSI is associated with a significant increase in morbidity, mortality, length of hospitalization, and the cost of healthcare. The aim of the current study was to evaluate the impact of a multifaceted improvement project aiming to reduce the rate of CLABSI. Methods This was an interventional surveillance study. A Plan, Do, Check, Act (PDCA) quality improvement approach was used. The intervention was initiated in March 2018. It focused on the following aspects: standardizing the central line (CL) maintenance practices, creating a designated cart for CL insertion and maintenance, increasing compliance with aseptic techniques and CL insertion and maintenance bundles, educating the healthcare workers and patients on CLABSI prevention, environmental cleaning and disinfections, and instantaneous feedback to the stakeholders about CLABSI events. The intervention engaged multiple partners including infection control, nurses, and physicians. Surveillance methods and CLABSI definition was done according the US National Healthcare Safety Network. Results During 2018, a total of ten CLABSI events were detected during 2919 central-line days. They included four, four, two, and no events in the first, second, third, and fourth quarters, respectively. After intervention, the rate significantly decreased from 5.2 per 1000 central line-days during the second quarter of 2018, to 3.9 during the third quarter of 2018, and zero during the fourth quarter of 2018 (Mantel-Haenszel chi-square p value of 0.034). Conclusion A multidisciplinary multifaceted improvement project using quality improvement tools to enforce the evidence-based preventive practices has been successful in reducing the CLABSI rate. The implementation of the improvement project needs to be continued to maintain zero or low 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".