Transitional Care for Patients With Congestive Heart Failure: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: We aimed to determine the impact of transitional care interventions (TCIs) on acute health service use by patients with congestive heart failure in primary care and to identify the most effective TCIs and their optimal duration. METHODS: We conducted a systematic review and meta-analysis of randomized controlled trials, searching the Medline, PsycInfo, EMBASE, and Cochrane Library databases. We performed a meta-analysis to assess the impact of TCI on all-cause hospital readmissions and emergency department (ED) visits. We developed a taxonomy of TCIs based on intensity and assessed the methodologic quality of the trials. We calculated the relative risk (RR) and a 95% confidence interval for each outcome. We conducted a stratified analysis to identify the most effective TCIs and their optimal duration. RESULTS: We identified 41 randomized controlled trials. TCIs significantly reduced risks of readmission and ED visits by 8% and 29%, respectively (relative risk = 0.92; 95% CI, 0.87-0.98; P = .006 and relative risk = 0.71; 95% CI, 0.51-0.98; P = .04). High-intensity TCIs (combining home visits with telephone followup, clinic visits, or both) reduced readmission risk regardless of the duration of follow-up. Moderate-intensity TCIs were efficacious if implemented for a longer duration (at least 6 months). In contrast, low-intensity TCIs, entailing only followup in outpatient clinics or telephone follow-up, were not efficacious. CONCLUSIONS: Clinicians and managers who implement TCIs in primary care can incorporate these results with their own health care context to determine the optimal balance between intensity and duration of TCIs. High-intensity interventions seem to be the best option. Moderate-intensity interventions implemented for 6 months or longer may be another option.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| 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 | 0.000 |
| 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".