Frailty Phenotype and Deficit Accumulation Frailty Index in Predicting Recovery After Transcatheter and Surgical Aortic Valve Replacement
Bibliographic record
Abstract
BACKGROUND: Frailty phenotype and deficit-accumulation frailty index (FI) are widely used measures of frailty. Their performance in predicting recovery after surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR) has not been compared. METHODS: Patients undergoing SAVR (n = 91) or TAVR (n = 137) at an academic medical center were prospectively assessed for frailty phenotype and FI. Outcomes were death or poor recovery, defined as a decline in ability to perform 22 daily activities and New York Heart Association class 3 or 4 at 6 months after surgery. The predictive ability of frailty phenotype versus FI and their additive value to a traditional surgical risk model were evaluated using C-statistics, net reclassification improvement (NRI), and integrated discrimination improvement. RESULTS: TAVR patients had higher prevalence of phenotypic frailty (85% vs 38%, p < .001) and greater mean FI (0.37 vs 0.24, p < .001) than SAVR patients. In the overall cohort, FI had a higher C-statistic than frailty phenotype (0.74 vs 0.63, p = .01) for predicting death or poor recovery. Adding FI to the traditional model improved prediction (NRI, 26.4%, p = .02; integrated discrimination improvement, 7.7%, p < .001), while adding phenotypic frailty did not (NRI, 4.0%, p = .70; integrated discrimination improvement, 1.6%, p = .08). The additive value of FI was evident in TAVR patients (NRI, 42.8%, p < .01) but not in SAVR patients (NRI, 25.0%, p = .29). Phenotypic frailty did not add significantly in either TAVR (NRI, 6.8%, p = .26) or SAVR patients (NRI, 25.0%, p = .29). CONCLUSIONS: Deficit-accumulation FI provides better prediction of death or poor recovery than frailty phenotype in older patients undergoing SAVR and TAVR.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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".