A meta-review of evidence on heart failure disease management programs: the challenges of describing and synthesizing evidence on complex interventions
AuthorSavard, LA; Thompson, DR; Clark, AM
University of Melbourne Author/sThompson, David
Document TypeJournal Article
CitationsSavard, L. A., Thompson, D. R. & Clark, A. M. (2011). A meta-review of evidence on heart failure disease management programs: the challenges of describing and synthesizing evidence on complex interventions. TRIALS, 12 (1), https://doi.org/10.1186/1745-6215-12-194.
Access StatusOpen Access
BACKGROUND: Despite favourable results from past meta-analyses, some recent large trials have not found heart failure (HF) disease management programs to be beneficial. To explore reasons for this, we evaluated evidence from existing meta-analyses. METHODS: Systematic review incorporating meta-review was used. We selected meta-analyses of randomized controlled trials published after 1995 in English that examined the effects of HF disease management programs on key outcomes. Databases searched: MEDLINE, EMBASE, Cochrane Database of Systematic Reviews (CDSR), DARE, NHS EED, NHS HTA, Ageline, AMED, Scopus, Web of Science and CINAHL; cited references, experts and existing reviews were also searched. RESULTS: 15 meta-analyses were identified containing a mean of 18.5 randomized trials of HF interventions +/- 10.1 (range: 6 to 36). Overall quality of the meta-analyses was very mixed (Mean AMSTAR Score = 6.4 +/- 1.9; range 2-9). Reporting inadequacies were widespread around populations, intervention components, settings and characteristics, comparison, and comparator groups. Heterogeneity (statistical, clinical, and methodological) was not taken into account sufficiently when drawing conclusions from pooled analyses. CONCLUSIONS: Meta-analyses of heart failure disease management programs have promising findings but often fail to report key characteristics of populations, interventions, and comparisons. Existing reviews are of mixed quality and do not adequately take account of program complexity and heterogeneity.
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