Prevalence and prognostic significance of malnutrition in patients with secondary mitral regurgitation undergoing transcatheter edge‐to‐edge repair
Bibliographic record
Abstract
BACKGROUND: Malnutrition is associated with poor prognosis in several cardiovascular diseases; however, its role in patients with secondary mitral regurgitation (SMR) is poorly known. AIMS: To evaluate the impact of nutritional status, assessed using different scores, on clinical outcomes in patients with SMR undergoing transcatheter edge-to-edge repair (TEER) in a real-world setting. METHODS: A total of 658 patients with SMR and complete nutritional data were identified from the MIVNUT registry. Nutritional status has been assessed using controlling nutritional status index (CONUT), prognostic nutritional index (PNI), and geriatric nutritional risk index (GNRI) scores. Outcomes of interest were all-cause mortality and all-cause mortality or heart failure (HF) hospitalization. RESULTS: Any malnutrition grade was observed in 79.4%, 16.7%, and 47.9% of patients by using CONUT, PNI, and GNRI, respectively, while moderate to severe malnutrition was noted in 24.7%, 16.7%, and 25.6% of patients, respectively. At a median follow-up of 2.2 years, 212 patients (32.2%) died. Moderate-severe malnutrition was associated with a higher rate of all-cause mortality (HR: 2.46 [95% CI: 1.69-3.58], HR: 2.18 [95% CI: 1.46-3.26], HR: 1.97 [95% CI: 1.41-2.74] for CONUT, PNI, and GNRI scores, respectively). The combined secondary endpoint of all-cause mortality and HF rehospitalization occurred in 306 patients (46.5%). Patients with moderate-severe malnutrition had a higher risk of the composite endpoint (HR: 1.56 [95% CI: 1.20-2.28], HR: 1.55 [95% CI: 1.01-2.19], HR: 1.36 [95% CI: 1.02-1.80] for CONUT, PNI, and GNRI scores, respectively). After adjustment for multiple confounders, moderate-severe malnutrition remained independently associated with clinical outcomes. CONCLUSIONS: Moderate-severe malnutrition was common in patients with SMR undergoing TEER. It was independently associated with poor prognosis regardless of the different scores used.
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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) | 0.000 | 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 | 0.000 | 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".