Increased rates of proton pump inhibitor deprescription: a retrospective cohort of patients with upper gastrointestinal bleeding requiring endoscopic intervention
Bibliographic record
Abstract
Abstract Objective Proton pump inhibitors (PPIs) are widely prescribed but inappropriate indications and concerns over long-term side effects have led to recommendations to deprescribe PPIs in certain patients. We previously found a 4-fold increase in PPI deprescription in patients with esophageal strictures. This study aims to assess the PPI deprescription rate in patients with upper gastrointestinal bleeding (UGIB). Methods All patients from 2 gastroenterology practices who received endoscopic treatment for UGIB during the years of 2015-2022 were identified using physician billing codes. We defined PPI deprescription as either a 50% dose reduction, frequency reduction, or complete medication discontinuation at the time of endoscopic intervention compared to the established PPI therapy from the 3 months prior. We compared the rate of PPI deprescription between 2 time periods 2015-2018 (group 1) and 2019-2022 (group 2). Results Three hundred one UGIB managed with endoscopy were analyzed. Patients in group 2 had a significantly higher rate of PPI deprescription than group 1 (15% vs 4%; P < .002). Patients with peptic ulcer disease (PUD) had a significantly higher PPI deprescription during the second time period (16% vs 0%; P = .028). Among patients with repeat UGIB, 10% had their PPI deprescribed. Conclusions Proton pump inhibitor deprescription in patients with UGIB treated with endoscopic intervention was more common in the second time period. This corresponds to when PPI deprescription guidelines were distributed. Physicians should ensure the appropriate application of PPI deprescription guidelines and continuation of PPI therapy for patients with strong indications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".