Comparative Effectiveness of Transitional Care Services in Patients Discharged from the Hospital with Heart Failure: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
AIMS: To compare the effectiveness of transitional care services in decreasing all-cause death and all-cause readmissions following hospitalization for heart failure (HF). METHODS AND RESULTS: We searched PubMed, Embase, CINAHL, and Cochrane Clinical Trials Register for randomized controlled trials (RCTs) published in 2000-2015 that tested the efficacy of transitional care services in patients hospitalized for HF, provided ≥1 month of follow-up, and reported all-cause mortality or all-cause readmissions. Our network meta-analysis included 53 RCTs (12 356 patients). Among services that significantly decreased all-cause mortality compared with usual care, nurse home visits were most effective [ranking P-score 0.6794; relative risk (RR) 0.78, 95% confidence intervals (CI) 0.62-0.98], followed by disease management clinics (DMCs) (ranking P-score 0.6368; RR 0.80, 95% CI 0.67-0.97). Among services that significantly decreased all-cause readmission, nurse home visits were most effective [ranking P-score 0.8365; incident rate ratio (IRR) 0.65, 95% CI 0.49-0.86], followed by nurse case management (NCM) (ranking P-score 0.6168; IRR 0.77, 95% CI 0.63-0.95), and DMCs (ranking P-score 0.5691; IRR 0.80, 95% CI 0.66-0.97). There was no significant difference in the comparative effectiveness of services that improved each outcome. Nurse home visits had the greatest pooled cost-savings (3810 USD, 95% CI 3682-3937), followed by NCM (3435 USD, 95% CI 3224-3645), and DMCs (245 USD, 95% CI -70 to 559). Telephone, telemonitoring, pharmacist, and education interventions did not significantly improve clinical outcomes. CONCLUSION: Nurse home visits and DMCs decrease all-cause mortality after hospitalization for HF. Along with NCM, they also reduce all-cause readmissions, with no significant difference in comparative effectiveness. These services reduce healthcare system costs to varying degrees.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.041 |
| Bibliometrics | 0.005 | 0.006 |
| 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".