Minimally Invasive Mitral Valve Repair with Artificial Chordae: Insights from a 6-Year Single-Center Study
Bibliographic record
Abstract
Purpose. Minimally invasive mitral valve repair (MIMVR) has been demonstrated to be safe and effective, but technical difficulty, outcome variation, and lack of standardized protocols undermine the utility of artificial chordae. This study aims to analyze the midterm outcomes of repair using artificial chordae through right minithoracotomy. Methods. A retrospective cohort study was conducted on consecutive patients who underwent MIMVR using artificial chordae at a single center in Vietnam between April 2016 and April 2022. Valve repairs were separated into two groups based on a previously validated complexity score: simple repair (Group 1) and intermediate-to-complex repair (Group 2). Demographic variables, comorbidities, operative characteristics, surgical outcomes, and follow-up data on survival and mitral regurgitation (MR) grade were analyzed. The learning curve was assessed by comparing the number of procedures with operation time and aorta cross-clamp time. Primary endpoints included survival and freedom from recurrent MR at four years. Results. Ninety patients were identified, including 41 simple and 49 intermediate-to-complex repairs. The mean age was 50.5 ± 12.9 years. Both groups had similar preoperative characteristics. The perioperative and postoperative outcomes were favorable, with no cases requiring mitral valve replacement. The median follow-up time was 30.3 months (18.2–40.4), and there were two (2.2%) cardiac deaths, with one in each group. The Kaplan–Meier survival estimates for Groups 1 and 2 at 12 and 24 months were 97% vs. 100% and 97% vs. 96%, respectively (95% CI = 0.05–12.2, <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>P</a:mi> <a:mo>=</a:mo> <a:mn>0.850</a:mn> </a:math> ), and estimates for freedom from recurrent MR were 97% vs. 92% and 97% vs. 88%, respectively (95% CI = 0.49–12.0, <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>P</c:mi> <c:mo>=</c:mo> <c:mn>0.260</c:mn> </c:math> ). There was a negative association between the volume of operations and the duration of operation and aortic cross-clamp time, leading to shorter durations. Conclusion. Based on our single-center experience, MIMVR using artificial chordae via right mini-thoracotomy can be safely and effectively performed in resource-limited countries for patients with MR. This approach has been shown to be applicable for a range of MR complexities, from simple to intermediate-to-complex MV repairs, and has demonstrated promising results in terms of midterm freedom from MR recurrence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.009 |
| Bibliometrics | 0.000 | 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; a candidate call from one teacher head, 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".