Regional Analgesia in Pediatric Cardiothoracic Surgery: A Bayesian Network Meta-Analysis.
Summary
Across 24 RCTs (n=1602), all 13 regional techniques reduced 24-hour opioid consumption after pediatric cardiothoracic surgery. Thoracic retrolaminar block had the largest opioid-sparing effect; time to first rescue was longest with pectoral nerve blocks, and PONV incidence was lowest with epidural and transversus thoracis muscle plane blocks. Heterogeneity limits indirect comparisons.
Key Findings
- Network meta-analysis of 24 RCTs (n=1602) across 13 regional techniques showed universal reduction in 24-hour opioid use.
- Thoracic retrolaminar block ranked best for opioid consumption reduction; pain score advantages were modest except immediately postoperative.
- Time to first rescue analgesic was longest with pectoral nerve blocks; PONV incidence was lowest with epidural and transversus thoracis muscle plane blocks.
- Indirect comparisons were limited by heterogeneity across studies.
Clinical Implications
Consider thoracic retrolaminar block for maximal opioid-sparing and pectoral nerve blocks for prolonged analgesia; epidural and transversus thoracis muscle plane blocks may lower PONV. Individualize technique based on surgical approach, expertise, and risk profile, and standardize outcome tracking.
Why It Matters
This analysis provides comparative efficacy data to guide block selection in a high-stakes pediatric population where opioid minimization and recovery optimization are critical.
Limitations
- Heterogeneity in block techniques, dosing, and outcome measures limits precision of indirect comparisons.
- Sparse head-to-head trials between certain blocks; safety outcomes variably reported.
Future Directions
Conduct adequately powered head-to-head RCTs with standardized dosing, sedation, and safety outcomes; evaluate long-term recovery metrics and enhanced recovery pathways.
Study Information
- Study Type
- Meta-analysis
- Research Domain
- Treatment
- Evidence Level
- I - Synthesis of randomized controlled trials using Bayesian network meta-analysis.
- Study Design
- OTHER