Prescription Patterns of Proton-Pump Inhibitors and Antiplatelet Therapy as Risk Factors for Gastrointestinal Bleeding in Patients on Hemodialysis
Bibliographic record
Abstract
Background: There is an increased risk of gastrointestinal bleeding (GI) in patients with end stage kidney disease (ESKD) for reasons including uremic platelet dysfunction, co-morbid illness, and use of antiplatelet agents. Proton pump inhibitors (PPI) reduce GI bleeding and are recommended for high-risk patients such as those prescribed dual antiplatelet therapy (DAPT). Whether inappropriate prescription of DAPT and/or lack of appropriate use of PPIs contribute to gastrointestinal bleeding risk in hemodialysis patients is not currently known. Thus, the objective of our project was to determine whether patients with ESKD are appropriately prescribed DAPT and PPI therapy. Methods: A chart review was performed at a satellite hemodialysis unit of The Ottawa Hospital in Ontario, Canada in July 2023. Patients’ medical history and use of antiplatelets, PPIs, anticoagulants, non-steroidal anti-inflammatories (NSAIDs), and corticosteroids were identified. Patients’ indications for PPI and DAPT were elucidated. Subsequently, a note was added to the electronic medical record and communicated to the patients’ primary nephrologist stating if a patient was taking DAPT/PPI and whether these medications were indicated. Adjustments to medications could be made thereafter if needed. Results: Of 88 hemodialysis patients, 44 were on antiplatelet therapy (4 on DAPT), 1 on NSAID, 12 on corticosteroids, 7 on anticoagulants, 2 on histamine H2-receptor antagonists, and 39 on PPIs. Fourteen percent of PPI users had absolute indication for therapy. One patient in whom PPI therapy was indicated was not prescribed one. Three of 4 DAPT users met current indications for therapy; 1 had a prior indication for DAPT and after review with their primary nephrology team, the patient was reduced to single antiplatelet therapy. Conclusion: Only one patient in our study had an absolute indication for PPI but had not been prescribed one, and one patient prescribed DAPT no longer met criteria for continued use. Overall, prescribing patterns of DAPT and PPI are unlikely to be a major contributor to the increased risk of gastrointestinal bleeding in patients on hemodialysis at our center. Nevertheless, review of medications and medical history may improve outcomes in patients who have been inappropriately prescribed these therapies.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".