Proton Pump Inhibitors Are Not Associated With Acute Kidney Injury in Critical Illness
Bibliographic record
Abstract
Abstract Recent epidemiologic data linking proton pump inhibitor (PPI) use to acute and chronic kidney dysfunction is yet to be validated in other populations, and mechanisms have not been explored. Using a large, well phenotyped inception cohort of 15 063 critically ill patients, we examined the risk of acute kidney injury (AKI), as defined by the Kidney Disease Improving Global Outcomes criteria guidelines, according to prior use of a PPI, histamine‐2 receptor antagonist (H2RA), or neither. A total of 3725 (24.7%) patients reported PPI use prior to admission, while 905 (6.0%) patients reported H2RA use. AKI occurred in 747 (20.0%) and 163 (18.0%) of PPI and H2RA users respectively, compared to 1712 (16.2%) of those not taking acid suppressive medications. In unadjusted analysis, PPI and H2RA users had a 28% (95%CI 1.17‐1.41, P < .001) and 10% (95%CI 0.91‐1.30, P = .31) higher risk of AKI compared to those taking neither class of medication. However, in sequential models that included adjustment for demographics, cardiovascular comorbidities, indications for PPI use, and severity of illness, the effect of PPI on the risk of AKI was attenuated, and in the adjusted analysis, PPI was not associated with AKI (OR 1.02; 95%CI 0.91‐1.13, P = .73). The presence of sterile pyuria and hypomagnesemia did not modify the association between PPI use and AKI. In summary, after adjustment for demographics, illness severity, and the indication for PPI use, PPI use prior to admission is not associated with critical illness AKI.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".