Systematic review and meta-analyses of survivors of atrial switch operation for d-transposition of great arteries: continued need for surveillance for baffle complications and rhythm disturbances
Systematic review and meta-analyses of survivors of atrial switch operation for d-transposition of great arteries: continued need for surveillance for baffle complications and rhythm disturbances
Monday, May 20, 2019
Belmont Ballroom 2-3 (The Cosmopolitan of Las Vegas)
Background
The atrial switch (Mustard / Senning) operation for d-transposition of great arteries became obsolete with the advent of the arterial switch procedure. However a sizable number of Mustard / Senning (M/S) survivors still need to be cared for – long term data for outcomes and need for procedures is still unclear. Hence, we undertook a systematic review / meta-analysis to pool data and increasing the strength of current evidence.
Methods
A comprehensive search of Scopus database from inception through October 31, 2018 was conducted using predefined criteria. We included studies reporting at least survival data for 20 survivors of M/S operation with a follow up of 10 years or more. Meta-analysis was performed with Comprehensive meta-analysis software. Random effects modelling was used because of observational nature of the pooled data.
Results
20 eligible studies were pooled to get a sample of 3454 patients for a pooled follow up of 59142.7 patient-years. The need for re-intervention was estimated to be 0.6% / yr (CI: 0.4-0.8%) with the majority being baffle issues (0.5%/year; CI: 0.4-0.7%). Need for pacemaker was 0.5% / yr (CI: 0.4-0.7%). Only 22 patients were transplanted despite a high rate of right ventricular dysfunction (0.8% / yr; CI: 0.5-1.0%) and mortality (0.9%/yr; CI: 0.7-1.1%). Rate of sudden death was estimated at 0.3%/yr (CI: 0.2-0.4%).
Conclusions
Our pooled analysis provides a reference for surveillance of M/S survivors – in the current era, baffle stenosis / leak are easily managed via trans-catheter options. Also, risk of sudden death is high and risk prediction algorithms for same are needed.