Effect of post-discharge virtual wards on improving outcomes in heart failure and non-heart failure populations: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Unplanned hospital admissions in high-risk patients are common and costly in an increasingly frail chronic disease population. Virtual Wards (VW) are an emerging concept to improve outcomes in these patients. PURPOSE: To evaluate the effect of post-discharge VWs, as an alternative to usual community based care, on hospital readmissions and mortality among heart failure and non-heart failure populations. DATA SOURCES: Ovid MEDLINE, EMBASE, PubMed, the Cochrane Database of Systematic Reviews, SCOPUS and CINAHL, from inception through to Jan 31, 2017; unpublished data, prior systematic reviews; reference lists. STUDY SELECTION: Randomized trials of post-discharge VW versus community based, usual care that reported all-cause hospital readmission and mortality outcomes. DATA EXTRACTION: Data were reviewed for inclusion and independently extracted by two reviewers. Risk of bias was assessed using the Cochrane Collaboration risk of bias tool. DATA SYNTHESIS: In patients with heart failure, a post-discharge VW reduced risk of mortality (six trials, n = 1634; RR 0.59, 95% CI = 0.44-0.78). Heart failure related readmissions were reduced (RR 0.61, 95% CI = 0.49-0.76), although all-cause readmission was not. In contrast, a post-discharge VW did not reduce death or hospital readmissions for patients with undifferentiated high-risk chronic diseases (four trials, n = .3186). LIMITATIONS: Heterogeneity with respect to intervention and comparator, lacking consistent descriptions and utilization of standardized nomenclature for VW. Some trials had methodologic shortcomings and relatively small study populations. CONCLUSIONS: A post-discharge VW can provide added benefits to usual community based care to reduce all-cause mortality and heart failure-related hospital admissions among patients with heart failure. Further research is needed to evaluate the utility of VWs in other chronic disease settings.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".