CLABSI Risk Factors in the NICU: Potential for Prevention: A PICNIC Study
Bibliographic record
Abstract
OBJECTIVE Central-line-associated bloodstream infections (CLABSI) are an important cause of morbidity and mortality in neonates. We aimed to determine whether intra-abdominal pathologies are an independent risk factor for CLABSI. METHODS We performed a retrospective matched case-control study of infants admitted to the neonatal intensive care units (NICUs) of the Montreal Children's Hospital (Montreal) and the Royal Alexandra Hospital, Edmonton, Canada. CLABSI cases that occurred between April 2009 and March 2014 were identified through local infection control databases. For each case, up to 3 controls were matched (National Healthcare Safety Network [NHSN] birth weight category, chronological age, and central venous catheter (CVC) dwell time at the time of CLABSI onset). Data were analyzed using conditional logistic regression. RESULTS We identified 120 cases and 293 controls. According to a matched univariate analysis, the following variables were significant risk factors for CLABSI: active intra-abdominal pathology (odds ratio [OR], 3.4; 95% confidence interval [CI], 1.8-6.4), abdominal surgery in the prior 7 days (OR, 3.5; 95% CI, 1.0-10.9); male sex (OR, 1.7; 95% CI, 1.1-2.6) and ≥3 heel punctures (OR, 4.0; 95% CI, 1.9-8.3). According to a multivariate matched analysis, intra-abdominal pathology (OR, 5.9; 95% CI, 2.5-14.1), and ≥3 heel punctures (OR, 5.4; 95% CI, 2.4-12.2) remained independent risk factors for CLABSI. CONCLUSION The presence of an active intra-abdominal pathology increased the risk of CLABSI by almost 6-fold. Similar to CLABSI in oncology patients, a subgroup of CLABSI with mucosal barrier injury should be considered for infants in the NICU with active intra-abdominal pathology. Infect Control Hosp Epidemiol 2016;1446-1452.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".