Assessment of left ventricular global longitudinal strain (LVGLS) before and after MiitraClip therapy in patients with heart failure and secondary mitral regurgitation
Bibliographic record
Abstract
Abstract Background Mitral regurgitation (MR) is a common valvular disease that significantly contributes to heart failure (HF). Left ventricular (LV) dysfunction in MR is traditionally assessed using ejection fraction (EF), which may not detect early myocardial impairment. Global longitudinal strain (GLS) has demonstrated superior prognostic value compared to EF in the context of transcatheter mitral valve repair (TMVr) with MitraClip. Recent clinical trials have demonstrated mixed benefits of MitraClip. These discrepancies may stem from differences in patient selection, LV function, and study design. Purpose This study aims to assess the impact of MitraClip implantation on LV GLS in secondary MR patients. Unlike previous trials, which focused on EF, our meta-analysis emphasizes GLS as a more sensitive indicator of myocardial function. By synthesizing data from multiple studies, we seek to determine whether MitraClip improves LV function, reduces hospitalizations, and mortality. Additionally, we evaluate secondary outcomes such as reverse LV remodeling and changes in New York Heart Association (NYHA) classification to provide a comprehensive assessment of MitraClip’s therapeutic efficacy. Methods We searched PubMed, EMBASE and ScienceDirect and 18 studies (1254 patients) that reported GLS before and after MitraClip therapy were included. Cohort studies were assessed using the Newcastle-Ottawa Scale and clinical trials with Cochrane quality assessment tool. Publication bias was evaluated with Egger’s test and funnel plots. Random-effects meta-analysis was conducted in Jamovi (ver 2.5) and Comprehensive Meta-analysis software to pool GLS estimates, LV parameters, and clinical outcomes. Heterogeneity was assessed using I² statistics and Cochrane Q test, with leave-one-out sensitivity analyses ensuring robustness of findings. Results Post-procedure GLS improved from baseline, with pooled estimates of -0.377 at 30 days (95% CI: -1.55 to 0.80, p<0.001) and 0.101 at six months (95% CI: -0.095 to 0.302, p=0.308), though heterogeneity was significant (I² = 97%-99.2%). Hospitalisation rates post-MitraClip was 0.149 (95% CI: 0.094-0.204, p<0.001), while mortality was 0.315 (95% CI: 0.282 to 0.348, p<0.001). LVEF increased; 0.929 (95% CI: 0.898 to 0.960, p<0.001), but LV end-diastolic and end-systolic volumes decreased. NYHA class improved, with an odds ratio of 20.2 (95% CI: 6.5-62.4, p<0.001). Conclusion MitraClip therapy is effective in enhancing LV function as noted by significantly improved LV GLS and reducing complications in patients with secondary MR. LV remodeling markers show favorable trends albeit with higher heterogeneity. MitraClip is associated with reduced hospitalizations and improved NYHA status, though mortality benefits remain uncertain. These findings highlight GLS as a critical marker in assessing MitraClip’s impact, underscoring the need for further studies to refine patient selection and optimize outcomes.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".