Ministernotomy or minithoracotomy for minimally invasive aortic valve replacement: a Bayesian network meta-analysis
- PMID: 25694971
- PMCID: PMC4311162
- DOI: 10.3978/j.issn.2225-319X.2014.08.01
Ministernotomy or minithoracotomy for minimally invasive aortic valve replacement: a Bayesian network meta-analysis
Abstract
Background: Establishing the relative merits of ministernotomy (MS) and minithoracotomy (MT) approaches to minimally invasive aortic valve replacement (MIAVR) is difficult given the limited available direct evidence. Network meta-analysis is a Bayesian approach that can combine direct and indirect evidence to better define the benefits and risks of MS and MT.
Methods: Electronic searches were performed using six databases from their inception to June 2014. Relevant studies utilizing a minimally invasive approach for aortic valve replacement were identified. Data were extracted and analyzed according to predefined clinical endpoints. Both traditional and Bayesian meta-analysis approaches were conducted.
Results: Compared to full sternotomy, MT was associated with longer cardiopulmonary bypass (CPB) duration (WMD, 9.99; 95% CI, 3.91, 16.07; I(2)=55%; P=0.001) and cross-clamp duration (WMD, 7.64; 95% CI, 2.86, 12.42; P=0.002; I(2)=74%). When compared to MS using network meta-analysis, no significant difference in duration was detected. Postoperative outcomes including 30-day mortality, stroke, and reoperation for bleeding and wound infection were comparable between MS and MT using both traditional and Bayesian meta-analysis techniques.
Conclusions: The current evidence demonstrates that MIAVR via MS or MT is a safe and efficacious alternative to conventional median sternotomy. MT may be associated with longer CPB and cross-clamp durations, but has similar post-operative outcomes compared to MS. An individualized approach tailored to both the patient and surgical team is likely to provide optimal outcomes.
Keywords: Aortic valve replacement; Bayesian; ministernotomy; minithoracotomy; network meta-analysis.
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