Strategies to Prevent Readmissions to Hospital for COPD: A Systematic Review
Bibliographic record
Abstract
Patients with chronic obstructive pulmonary disease (COPD) experience high rates of hospital readmissions, placing substantial clinical and economic strain on the healthcare system. Therefore, it is essential to implement evidence-based strategies for preventing these readmissions. The primary objective of our systematic review was to identify and describe the domains of existing primary research on strategies aimed at reducing hospital readmissions among adult patients with COPD. We also aimed to identify existing gaps in the literature to facilitate future research efforts. A total of 843 studies were captured by the initial search and 96 were included in the final review (25 randomized controlled trials, 37 observational studies, and 34 non-randomized interventional studies). Of the included studies, 72% (n = 69) were considered low risk of bias. The majority of included studies (n = 76) evaluated patient-level readmission prevention strategies (medication and other treatments (n = 25), multi-modal (n = 19), follow-up (n = 16), telehealth (n = 8), education and coaching (n = 8)). Fewer assessed broader system- (n = 13) and policy-level (n = 7) strategies. We observed a trend toward reduced all-cause readmissions with the use of medication and other treatments, as well as a trend toward reduced COPD-related readmissions with the use of multi-modal and broader scale system-level interventions. Notably, much of this evidence supported shorter-term (30-day) readmission outcomes, while little evidence was available for longer-term outcomes. These findings should be interpreted with caution, as considerable between-study heterogeneity was also identified. Overall, this review identified several evidence-based interventions for reducing readmissions among patients with COPD that should be targeted for future research.Supplemental data for this article is available online at https://doi.org/10.1080/15412555.2021.1955338 .
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".