Frailty as a predictor of mortality and readmission rate in secondary mitral regurgitation
Bibliographic record
Abstract
INTRODUCTION: Selection in patients with functional mitral regurgitation (MR) to identify responders to interventions is challenging. In these patients, frailty might be used as a multidimensional parameter to summarize the resilience to stressors. Our objective was to evaluate frailty as a predictor of outcome in patients with moderate to severe secondary MR. METHODS: We conducted a single-center retrospective observational cohort study and included 239 patients with moderate to severe secondary MR aged 65 years or older between 2014 and 2020. Echocardiography was performed at baseline; frailty was evaluated using the clinical frailty scale (CFS). The combined primary endpoint was hospitalization for heart failure and all-cause mortality. RESULTS: A total of 53% (127) of all patients were classified as CFS 4 (living with mild frailty) or higher. Frail patients had a higher risk for the combined endpoint (hazard ratio, HR 3.70, 95% confidence interval, CI 2.12-6.47; p < 0.001), 1‑year mortality (HR 5.94, 95% CI 1.76-20.08; p < 0.001) even after adjustment for EuroSCORE2. The CFS was predictive for the combined endpoint (AUC 0.69, 95% CI 0.62-0.75) and outperformed EuroSCORE2 (AUC 0.54, 95% CI 0.46-0.62; p = 0.01). In sensitivity analyses, we found that frailty was associated with adverse outcomes at least in trend in all subgroups. CONCLUSION: For older, medically treated patients with moderate to severe secondary mitral regurgitation, frailty is an independent predictor for the occurrence of death and heart failure-related readmission within 1 year and outperformed the EuroSCORE2. Frailty should be assessed routinely in patients with heart failure to guide clinical decision making for mitral valve interventions or conservative treatment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".