Prognostic utility of global longitudinal strain (GLS) in patients with severe primary mitral regurgitation undergoing mitral valve surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Mitral valve (MV) surgery is recommended for the treatment of chronic severe mitral regurgitation (MR). However, the optimal timing of surgery in patients with asymptomatic severe primary MR remains elusive. Global longitudinal strain (GLS) may provide additional prognostic value in predicting postoperative outcomes in patients with chronic severe primary mitral regurgitation (MR) undergoing MV surgery. Purpose We aimed to determine the prognostic value of GLS in predicting postoperative mortality and LV dysfunction among patients with chronic severe primary MR. Methods Systematic search of Pubmed, Cochrane library, Clinicaltrials.gov, and Herdin.ph from inception to July 2019, using the terms “mitral insufficiency”, “mitral regurgitation”, “mitral valve surgery”, and “global longitudinal strain” was done without language restriction. We included and extracted data from cohort studies of patients with chronic severe primary MR who underwent MV surgery and which GLS, mortality and ejection fraction. We used the Newcastle Ottawa Scale to assess the quality of included studies. Review Manager 5.3 was used to perform analysis. Forest plots with summary hazard ratios (HR) and odds ratios (OR) with 95% confidence intervals (CI), I2 test for heterogeneity, and funnel plots were reported. Results Our search yielded 12 cohort studies with 2,843 patients; 7 prospective and 5 retrospective studies were included in the qualitative synthesis with 11 good quality studies. Cut-off GLS values ranged from −21.7% to −18.1%. In terms of postoperative all-cause mortality, the summary HR for worse versus better preoperative GLS is 1.22 (95% CI 1.04–1.44, p value <0.ehab724.16031, I2 95%). In terms of postoperative LV dysfunction, the summary OR is 1.74 (95% CI 1.14–2.66, p value 0.01, I2 94%) with significant heterogeneity. Conclusion Left ventricular GLS has prognostic value in terms of predicting postoperative mortality and LV dysfunction. However, significant heterogeneity exists between studies. Larger studies with well-defined inclusion criteria need to be performed and standardized GLS cut-offs need to be determined. Funding Acknowledgement Type of funding sources: None. GLS in Predicting All-Cause MortalityGLS in Predicting Post-op Dysfunction
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".