Cognitive impairment improves the predictive validity of physical frailty for mortality in patients with advanced heart failure referred for heart transplantation
Bibliographic record
Abstract
© 2016 International Society for Heart and Lung Transplantation Background The aim of this study was to identify whether the addition of cognitive impairment, depression, or both, to the assessment of physical frailty provides better outcome prediction in patients with advanced heart failure referred for heart transplantation (HT). Methods Beginning in March 2013, all patients with advanced heart failure referred to our Transplant Unit have undergone a physical frailty assessment using the Fried frailty phenotype. Cognition was assessed with the Montreal Cognitive Assessment and depression with the Depression in Medical Illness questionnaire. We assessed the value of 4 composite frailty measures: physical frailty (PF ≥ 3 of 5 = frailty), “cognitive frailty” (CogF ≥ 3 of 6 = frail), “depressive frailty” (DepF ≥ 3 of 6 = frail), and “cognitive-depressive frailty” (ComF ≥ 3 of 7 = frail) in predicting outcomes. Results Frailty was assessed in 156 patients (109 men, 47 women), aged 53 ± 13 years, and with a left ventricular ejection fraction of 27% ± 14%. Inclusion of cognitive impairment or depression in the definition of frailty increased the proportion classified as frail from 33% using PF to 42% using ComF. During follow-up, 28 patients died before ventricular assist device implantation or HT. Frailty was associated with significantly lower ventricular assist device- and HT-free survival, with CogF best capturing early mortality: 12-month survival for non-frail and frail cohorts was 81% ± 5% vs 58% ± 10% (p < 0.02) using PF and 85% ± 5% vs 56% ± 9% (p < 0.002) using CogF. Combining the Depression in Medical Illness score with PF or CogF did not strengthen the relationship between frailty and mortality. Conclusions The addition of cognitive impairment to the assessment of PF strengthened its capacity to identify advanced heart failure patients referred for HT who are at high risk of early death.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".