The association between proton pump inhibitor use and the risk of adverse kidney outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Existing epidemiological studies illustrate that proton pump inhibitors (PPIs) may be related to adverse kidney outcomes. To date, no comprehensive meta-analysis has been conducted to evaluate and quantify this association. Methods: We performed a systematic review and meta-analysis of studies to assess the association between PPI use and the risk of adverse kidney outcomes. We searched MEDLINE, Embase, SCOPUS, Web of Science, CINAHL, Cochrane Library and grey literature with no language restrictions (through 31 October 2016). Adverse kidney outcomes were acute interstitial nephritis (AIN), acute kidney injury (AKI), chronic kidney disease (CKD) and end-stage renal disease (ESRD). The risk ratios (RRs) and confidence intervals (CIs) were pooled using a random effects model. The strength of evidence (SOE) for each outcome was assessed using the Grading of Recommended Assessment, Development and Evaluation system. Results: Of 2037 identified studies, four cohort and five case-control studies with ∼2.6 million patients were included. Of these, 534 003 (20.2%) were PPI users. Compared with non-PPI users, PPI users experienced a significantly higher risk of AKI [RR 1.44 (95% CI 1.08-1.91); P = 0.013; SOE, low] and CKD [RR 1.36 (95% CI 1.07-1.72); P = 0.012; SOE, low]. Moreover, PPIs increased the risk of AIN [RR 3.61 (95% CI 2.37-5.51); P < 0.001; SOE, insufficient] and ESRD [RR 1.42 (95% CI 1.28-1.58); P < 0.001; SOE, insufficient]. Conclusion: PPI usage was associated with adverse kidney outcomes; however, these findings were based on observational studies and low-quality evidence. Additional rigorous studies are needed for further clarification.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.046 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".