Abstract 15083: Exercise Left Ventricular Ejection Fraction Predicts Long-term Brain Natriuretic Peptide Levels in Patients Undergoing Surgery for Severe Secondary Ischemic Mitral Regurgitation
Bibliographic record
Abstract
Background and Objective: Surgical treatment of severe secondary ischemic mitral regurgitation (IMR) may improve symptoms and functional capacity, however there are few data on its effect on long-on the evolution of heart failure. Time-course changes in brain natriuretic peptide (BNP) are a good marker of the heart failure status and outcomes. We investigated the association between the exercise stress echocardiographic (ESE) parameters and the changes in brain natriuretic peptide (BNP) following surgery for secondary IMR. Methods: We prospectively analyzed data on 50 patients (median age: 67, 61-64 y; EF: 35, 34-40%), undergoing mitral valve annuloplasty or replacement and coronary artery bypass graft (CABG). A valve annuloplasty with undersized ring was performed in 20 patients (40%) and a replacement in 30 (60%). A six minute walking test (6-MWT), BNP levels and ESE were performed at 1 year and at median follow-up (FU) of 6 years (4-7). Results: BNP level was: 388 (329-441) pg/ml before surgery, 175 (142-743) pg/ml at 1 y, and 123 (100-979) pg/ml at last FU (p=0.2). The relative changes of BNP from baseline to last FU significantly correlated with exercise tricuspid annulus plane systolic excursion (TAPSE) at last FU (r= -0.7, p<0.001), with preoperative and FU exercise LVEF, respectively ( r=-0.7 p= 0.01) (r=-0.93, p<0.001).On multivariable analysis, preoperative exercise EF was strongly and independently associated with independent BNP levels at last FU and with the changes in BNP from baseline to last FU. Conclusions: Despite surgical treatment of severe secondary IMR, BNP levels progressively increased over time in nearly 50% of the patients. Lower preoperative and 1-year FU exercise-stress EF was associated with increased levels of BNP during FU..
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".