Meta-analysis of MitraClip and PASCAL for transcatheter mitral edge-to-edge repair
Bibliographic record
Abstract
BACKGROUND: Despite the promising results of both MitraClip and PASCAL systems for the treatment of mitral regurgitation (MR), there is limited data on the comparison of both systems regarding their safety and efficacy. We aim to compare both systems for MR. MATERIALS AND METHODS: Five databases were searched until October 2024. Original studies were only included and critically appraised using an adapted version of the Newcastle-Ottawa scale for observational cohort studies and the Cochrane risk of bias tool for randomized controlled trials. The risk ratio (RR) and mean difference (MD) with their corresponding 95% confidence interval (95% CI). RESULTS: From the database search, we identified 197 studies, of which eight studies comprising 1,612 patients who underwent transcatheter edge-to-edge repair with either MitraClip or PASCAL were included in this meta-analysis. The statistical analysis revealed no significant difference between the two devices in achieving a two-grade reduction in MR severity (RR = 0.95; 95% CI: [0.86, 1.04]; p = 0.28), one-grade reduction (RR = 1.17; 95% CI: [0.92, 1.49]; p = 0.19), or in cases with no improvement (RR = 1.23; 95% CI: [0.79, 1.90]; p = 0.36). Additionally, there were no significant differences between PASCAL and MitraClip regarding procedure time, procedural success, reinterventions, or all-cause mortality. However, PASCAL trended towards better residual MR reduction, although this was accompanied by moderate heterogeneity. Both devices demonstrated comparable safety profiles and were effective in reducing MR and improving cardiac function. CONCLUSION: MitraClip and PASCAL devices showed comparable safety profiles and procedural success rates. However, the analysis did not reveal a statistically significant difference between the two devices in reducing the severity of MR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.202 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".