Prognostic Impact of Nutritional Status After Transcatheter Edge‐to‐Edge Mitral Valve Repair: The MIVNUT Registry
Bibliographic record
Abstract
Background Malnutrition is associated with poor prognosis in several cardiovascular diseases. However, its prognostic impact in patients undergoing transcatheter edge‐to‐edge mitral valve repair (TEER) is not well known. This study sought to assess the prevalence, clinical associations, and prognostic consequences of malnutrition in patients undergoing TEER. Methods and Results A total of 892 patients undergoing TEER from the international MIVNUT (Mitral Valve Repair and Nutritional Status) registry were studied. Malnutrition status was assessed with the Controlling Nutritional Status score. The association of nutritional status with mortality was analyzed with multivariable Cox regression models, whereas the association with heart failure admission was assessed by Fine‐Gray models, with death as a competing risk. According to the Controlling Nutritional Status score, 74.4% of patients with TEER had any degree of malnutrition at the time of TEER (75.1% in patients with body mass index <25 kg/m 2 , 72.1% in those with body mass index ≥25 kg/m 2 ). However, only 20% had moderate–severe malnutrition. TEER was successful in most of patients (94.2%). During a median follow‐up of 1.6 years (interquartile range, 0.6–3.0), 267 (29.9%) patients died and 256 patients (28.7%) were admitted for heart failure after TEER. Compared with normal nutritional status moderate–severe malnutrition resulted a strong predictor of mortality (adjusted hazard ratio [HR], 2.1 [95% CI, 1.1–2.4]; P <0.001) and heart failure admission (adjusted subdistribution HR, 1.6 [95% CI, 1.1–2.4]; P =0.015). Conclusions Malnutrition is common among patients submitted to TEER, and moderate–severe malnutrition is strongly associated with increased mortality and heart failure readmission. Assessment of nutritional status in these patients may help to improve risk stratification.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".