The effect of deprescribing interventions on mortality and health outcomes in older people: An updated systematic review and meta‐analysis
Bibliographic record
Abstract
AIMS: Previous systematic reviews suggest that deprescribing may improve survival, particularly in frail older people. Evidence is rapidly accumulating, suggesting a need for an updated review of the literature. METHODS: We updated a 2016 systematic review and meta-analysis to include studies published from inception to 26 April 2024 from specified databases. Studies in which older people had at least one medication deprescribed were included and grouped by study designs and targeted medications. The risk of bias was assessed using the Cochrane tool and the Newcastle-Ottawa tool. Odds ratios (OR) or mean differences were calculated as the effect measures using either the Mantel-Haenszel or generic inverse-variance method with fixed- or random-effects meta-analyses. The primary outcome was mortality. Secondary outcomes were adverse drug withdrawal events, physical health, cognitive function, quality of life and effect on medication regimen. Subgroup analyses were performed based on age and intervention types. RESULTS: A total of 259 studies (reported in 286 papers) were included in this updated review. Deprescribing polypharmacy did not result in a significant reduction in mortality in both randomized (OR 0.96, 95% confidence interval [CI] 0.84-1.09) and non-randomized studies (OR 0.70, 95% CI 0.36-1.38). Further subgroup analyses of randomized studies on deprescribing polypharmacy demonstrated a significant reduction in mortality in the young old (aged 65-79) (OR 0.71, 95% CI 0.51-0.99) and when patient-specific interventions were applied (OR 0.79, 95% CI 0.63-0.99). CONCLUSIONS: Deprescribing can be achieved with potentially important benefits in terms of improved survival, particularly when patient-specific interventions are applied and initiated early in the young old.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".