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Record W2801406507 · doi:10.1016/j.jescts.2018.03.003

One-year outcomes of concomitant mitral reduction annuloplasty repair with coronary artery bypass grafting for moderate ischemic mitral regurgitation

2018· article· en· W2801406507 on OpenAlexaff
Medhat Refaie, Mohammed Alshehri, Mohamed Sharaa, Laila A. Elhenawy

Bibliographic record

VenueJournal of the Egyptian Society of Cardio-Thoracic Surgery · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineEjection fractionCardiologyMitral regurgitationInternal medicineConcomitantMitral valve repairMitral valveArteryMitral valve replacementCoronary artery diseaseRevascularizationSurgeryMyocardial infarctionHeart failure

Abstract

fetched live from OpenAlex

Background Mitral valve repair for chronic ischemic mitral regurgitation (IMR) in the published literature has contradictory outcomes. Here, we report our center outcomes of reduction annuloplasty technique in addition to myocardial revascularization to treat ischemic IMR over a four-year period. Methods Between January 2011 and December 2014, a total of 40 patients were identified to have a reduced left ventricular ejection fraction who underwent first-time mitral valve reduction annuloplasty concomitantly with coronary artery bypass grafting to treat moderate IMR. Variations in left ventricular end-systolic volume index (LVESVI), left ventricular ejection fraction (LVEF), functional class, and mitral regurgitation (MR) was followed up after surgery and retrospectively analyzed. Results In our cohort, mitral reduction annuloplasty was successful in helping 33 (82%) patients to have no residual MR and to downgrade their MR from moderate (grade 3+) to trace (grade 1+) in 7 (18%) patients before leaving the operating room. A change from baseline value was observed secondary to correction of MR. At 3-months follow-up time; the mean MR grade that was 0.2 ± 0.1 by the intraoperative assessment post-bypass has increased significantly to be 1.8 ± 0.4 (p < 0.05). Similarly, at one year after surgery it increased to 1.9 ± 0.3, p < 0.05, compared to baseline value. Remarkably, our patients showed a significant increase in the mean LVEF (48 ± 1.1%, p < 0.05), at 3-month visit and 1-year visit post-surgery (47 ± 4.2%), %, p < 0.05) compared to the baseline values (39 ± 2.3%, p < 0.05 for both visits). While our patients showed gradually but non-significantly reduction in the mean LVESVI (57.6 ± 9.0 ml/m 2 , p > 0.05) at early follow-up time, mean LVESVI improved significantly to be 47.6 ± 11.0 ml/m 2 , p < 0.05 at the one year. This statistically significant increase in the LVEF% at 3-month visit, in addition to the reduction in the LVESVI after one year were capable to induce substantial clinical change in NYHA functional class , which was observed early (at 3-month visit) where 35 (88%) patients became in NYHA class I and II and later (at 1-year visit post-surgery) where 32 (80%) patients stayed in NYHA class I and II. No deaths were reported during the one-year follow-up resulting in 95% survival rate. During the one-year follow-up time, 7 (18%) were readmitted to the hospital due to non-cardiac indications. Early operative mortality (within 30 days after surgery) was 5%. One-year survival was 95%. The mean duration of follow-up was 1.1 ± 2.4 years (range, 1.0–3.5 years). Conclusions Mitral reduction annuloplasty concomitant to surgical myocardial revascularization may be sufficient to correct moderate IMR. It is associated with substantial improvement in early and intermediate-term survival rates, left ventricular functions , NYHA class and low incidence of recurrent 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.315
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2018
Admission routes1
Has abstractyes

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