A Practical Two‐Stage Frailty Assessment for Older Adults Undergoing Aortic Valve Replacement
Bibliographic record
Abstract
OBJECTIVES: Despite evidence, frailty is not routinely assessed before cardiac surgery. We compared five brief frailty tests for predicting poor outcomes after aortic valve replacement and evaluated a strategy of performing comprehensive geriatric assessment (CGA) in screen-positive patients. DESIGN: Prospective cohort study. SETTING: A single academic center. PARTICIPANTS: Patients undergoing surgical aortic valve replacement (SAVR) (n = 91; mean age = 77.8 y) or transcatheter aortic valve replacement (TAVR) (n = 137; mean age = 84.5 y) from February 2014 to June 2017. MEASUREMENTS: Brief frailty tests (Fatigue, Resistance, Ambulation, Illness, and Loss of weight [FRAIL] scale; Clinical Frailty Scale; grip strength; gait speed; and chair rise) and a deficit-accumulation frailty index based on CGA (CGA-FI) were measured at baseline. A composite of death or functional decline and severe symptoms at 6 months was assessed. RESULTS: The outcome occurred in 8.8% (n = 8) after SAVR and 24.8% (n = 34) after TAVR. The chair rise test showed the highest discrimination in the SAVR (C statistic = .76) and TAVR cohorts (C statistic = .63). When the chair rise test was chosen as a screening test (≥17 s for SAVR and ≥23 s for TAVR), the incidence of outcome for screen-negative patients, screen-positive patients with CGA-FI of .34 or lower, and screen-positive patients with CGA-FI higher than .34 were 1.9% (n = 1/54), 5.3% (n = 1/19), and 33.3% (n = 6/18) after SAVR, respectively, and 15.0% (n = 9/60), 14.3% (n = 3/21), and 38.3% (n = 22/56) after TAVR, respectively. Compared with routinely performing CGA, targeting CGA to screen-positive patients would result in 54 fewer CGAs, without compromising sensitivity (routine vs targeted: .75 vs .75; P = 1.00) and specificity (.84 vs .86; P = 1.00) in the SAVR cohort; and 60 fewer CGAs with lower sensitivity (.82 vs.65; P = .03) and higher specificity (.50 vs .67; P < .01) in the TAVR cohort. CONCLUSIONS: The chair rise test with targeted CGA may be a practical strategy to identify older patients at high risk for mortality and poor recovery after SAVR and TAVR in whom individualized care management should be considered. J Am Geriatr Soc 67:2031-2037, 2019.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".