Frailty as a predictor of mortality and readmission rate in secondary mitral regurgitation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it