Validation of Cognitive Impairment in Combination With Physical Frailty as a Predictor of Mortality in Patients With Advanced Heart Failure Referred for Heart Transplantation
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to validate our previous finding that frailty predicts early mortality in patients with advanced heart failure (AHF) and that including cognition in the frailty assessment enhances the prediction of mortality. METHODS: Patients with AHF referred to our Transplant Unit between November 2015 and April 2020 underwent physical frailty assessment using the modified Fried physical frailty (PF) phenotype as well as cognitive assessment using the Montreal Cognitive Assessment to identify patients who were cognitively frail (CogF). We assessed the predictive value of the 2 frailty measures (PF ≥ 3 of 5 = frail; CogF ≥ 3 of 6 = frail) for pretransplant mortality. RESULTS: Three hundred thirteen patients (233 male and 80 female; age 53 ± 13 y) were assessed. Of these, 224 patients (72%) were nonfrail and 89 (28%) were frail using the PF. The CogF assessment identified an additional 30 patients as frail: 119 (38%). Frail patients had significantly increased mortality as compared to nonfrail patients. Ventricular assist device and heart transplant-censored survival at 12 mo was 92 ± 2 % for nonfrail and 69 ± 5% for frail patients (P < 0.0001) using the CogF instrument. CONCLUSIONS: This study validates our previously published findings that frailty is prevalent in patients with AHF referred for heart transplantation. PF predicts early mortality. The addition of cognitive assessment to the physical assessment of frailty identifies an additional cohort of patients with a similarly poor prognosis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".