Proton Pump Inhibitor Use and Associated Infectious Complications in the PICU: Propensity Score Matching Analysis
Bibliographic record
Abstract
OBJECTIVES: We aimed to evaluate the association between proton pump inhibitor (PPI) exposure and nosocomial infection (NI) during PICU stay. DESIGN: Propensity score matched analysis of a single-center retrospective cohort from January 1, 2017, to December 31, 2018. SETTING: Tertiary medical and surgical PICU in France. PATIENTS: Patients younger than 18 years old, admitted to the PICU with a stay greater than 48 hours. INTERVENTION: Patients were retrospectively allocated into two groups and compared depending on whether they received a PPI or not. MEASUREMENTS AND MAIN RESULTS: Seven-hundred fifty-four patients were included of which 231 received a PPI (31%). PPIs were mostly used for stress ulcer prophylaxis (174/231; 75%), but upper gastrointestinal bleed risk factors were rarely present (18%). In the unadjusted analyses, the rate of NI was 8% in the PPI exposed group versus 2% in the nonexposed group. After propensity score matching ( n = 184 per group), we failed to identify an association between PPI exposure and greater odds of NI (adjusted odds ratio 2.9 [95% CI, 0.9-9.3]; p = 0.082). However, these data have not excluded the possibility that there is up to nine-fold greater odds of NI. CONCLUSIONS: This study highlights the prevalent use of PPIs in the PICU, and the potential association between PPIs and nine-fold greater odds of NI is not excluded.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".