Clinical Frailty Scale as a predictor of adverse outcomes following aortic valve replacement: a systematic review and meta-analysis
Bibliographic record
Abstract
Objectives Assessment of frailty prior to aortic valve intervention is recommended in European and North American valvular heart disease guidelines. However, there is a lack of consensus on how it is best measured. The Clinical Frailty Scale (CFS) is a well-validated measure of frailty that is relatively quick to calculate. This meta-analysis sought to examine whether the CFS predicts mortality and morbidity following either transcatheter aortic valve implantation (TAVI) or surgical aortic valve replacement (SAVR). Methods Nine electronic databases were searched systematically for data on clinical outcomes post-TAVI/SAVR, where patients had undergone preoperative frailty assessment using the CFS. The primary endpoint was 12-month mortality. TAVI and SAVR data were assessed and reported separately. For each individual study, the incidence of adverse outcomes was extracted according to a CFS score of 5–9 (ie, frail) versus 1–4 (ie, non-frail), with meta-analysis performed using a random effects model. Results Of 2612 records screened, nine were included in the review (five TAVI, three SAVR and one which included both interventions). Among 4923 TAVI patients, meta-analysis showed 12-month mortality rates of 19.1% for the frail cohort versus 9.8% for the non-frail cohort (RR 2.53 (1.63 to 3.95), p<0.001, I2=83%). For the smaller cohort of SAVR patients (n=454), mortality rates were 20.3% versus 3.9% for the frail and non-frail cohorts, respectively (RR 5.08 (2.31 to 11.15), p<0.001, I2=5%). Conclusions Frailty, as determined by the CFS, was associated with an increased mortality risk in the 12 months following either TAVI or SAVR. These data would support its use in the preoperative assessment of elderly patients undergoing aortic valve interventions.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".