Prescribing, deprescribing and potential adverse effects of proton pump inhibitors in older patients with multimorbidity: an observational study
Bibliographic record
Abstract
Background: Proton pump inhibitors (PPIs) contribute to polypharmacy and are associated with adverse effects. As prospective data on longitudinal patterns of PPI prescribing in older patients with multimorbidity are lacking, we sought to assess patterns of PPI prescribing and deprescribing, as well as the association of PPI use with hospital admissions over 1 year in this population. Methods: We conducted a prospective, longitudinal cohort study using data from the Optimizing Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older Adults (OPERAM) trial, a randomized controlled trial testing an intervention to reduce inappropriate prescribing (2016–2018). This trial included adults aged 70 years and older with at least 3 chronic conditions and prescribed at least 5 chronic medications. We assessed prevalence of PPI use at time of hospital admission, and new prescriptions and deprescribing at discharge, and at 2 months and 1 year after discharge, by intervention group. We used a regression with competing risk for death to assess the association of PPI use with readmissions related to their potential adverse effects, and all-cause readmission. Results: Overall, 1080 (57.4%) of 1879 patients (mean age 79 yr) had PPI prescriptions at admission, including 496 (45.9%) patients with a potentially inappropriate indication. At discharge, 133 (24.9%) of 534 patients in the intervention group and 92 (16.8%) of 546 patients in the control group who were using PPIs at admission had deprescribing. Among 680 patients who were not using PPIs at discharge, 47 (14.6%) of 321 patients in the intervention group and 40 (11.1%) of 359 patients in the control group had a PPI started within 2 months. Use of PPIs was associated with all-cause readmission (n = 770, subdistribution hazard ratio 1.31, 95% confidence interval 1.12–1.53). Interpretation: Potentially inappropriate use of PPI, new PPI prescriptions and PPI deprescribing were frequent among older adults with multimorbidity and polypharmacy. These data suggest that persistent PPI use may be associated with clinically important adverse effects in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".