Outcomes comparison between robotic and conventional mitral valve surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Robotic-assisted cardiac surgery has emerged as a popular minimal invasive cardiac surgery approach, as it provides several advantages compared to conventional and other minimally invasive approaches. Mitral valve surgeries, including repair and replacement, are currently the most common cardiac surgeries performed with a robotic approach. However, there are concerns that surgeon's learning curve and prolonged operation time associated with this approach could compromise the surgical outcomes, hence the low acceptance of the technique in the clinical setting. In addition, despite various studies comparing robotic and conventional mitral surgery, it remains unclear whether the former would give comparable, if not better, outcomes. Purpose This study aims to compare the outcomes of robotic and conventional sternotomy mitral valve surgeries. Methods A comprehensive literature search was performed through Pubmed, CENTRAL, and ScienceDirect for studies comparing robotic and sternotomy approach for mitral valve repair and replacement. Studies were screened with our eligibility criteria, and their quality was examined using the Newcastle-Ottawa scale. The primary outcome analysed in this study was the perioperative mortality. Results Twelve studies involving 4300 patients (2223 experienced robotic surgery) were included. Pooled analysis showed that patients who underwent robotic surgery had a significant decrease in perioperative mortality compared to those who underwent sternotomy surgery (RR 0.33, 95% CI 0.18, 0.60, p=0.0003, I2=0%). Moreover, ICU length of stay was also shorter in the robotic group (MD −13.67, 95% CI −20.04, −7.29, p<0.0001, I2=93%). Re-operation risk due to bleeding was not significantly different between both groups (RR 1.13, 95% CI 0.79, 1.62, p=0.51, I2=0%). Egger's test result showed no evidence of small-study effects (p=0.83), and the funnel plot appeared symmetrical, meaning there was no publication bias. Conclusion Results from our meta-analysis refute the current concerns limiting the acceptance of robotic approach in mitral valve surgeries, showing significantly lower perioperative mortality and ICU length of stay, as well as a comparable re-operation risk due to bleeding with the conventional approach. Funding Acknowledgement Type of funding sources: None.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".