Effectiveness of Comprehensive Disease Management Programmes in Improving Clinical Outcomes in Heart Failure Patients. A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Disease management programmes (DMP) have been advocated to improve long term outcomes of heart failure (HF) patients. AIMS: To summarise the evidence supporting DMP effectiveness in improving HF clinical outcomes. METHODS: Eligible studies were located through a systematic literature search. Only randomised controlled trials (RCTs), enrolling HF patients, and allocating them to DMP or usual care (UC), were included. Information on study setting and design, participants' characteristics and interventions tested were collected. A study quality assessment was performed. Main clinical outcomes assessed were: all-cause mortality and (re)hospitalisations, HF-related (re)hospitalisations and mortality. Meta-analysis was performed according to both Yusuf-Peto method and random effects model. RESULTS: Thirty-three RCTs were included. Mortality was significantly reduced by DMP compared to UC: OR = 0.80 (CI 0.69-0.93, p = 0.003). All-cause and HF-related hospitalisation rates were also significantly reduced: OR = 0.76 (CI 0.69-0.94, p < 0.00001) and OR = 0.58 (CI 0.50-0.67, p < 0.00001), respectively. Different DMP approaches appeared to be equally effective (sensitivity analyses). CONCLUSION: DMP reduce mortality and hospitalisations in HF patients. Because various types of DMP appear to be similarly effective, the choice of a specific programme depends on local health services characteristics, patient population, and resources available.
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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.070 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".