Frailty Predicts Mortality after Heart Transplantation
Bibliographic record
Abstract
Background We have previously reported that combining cognitive assessment with the Fried frailty phenotype (FFP) enhances mortality prediction in advanced heart failure (AHF) patients referred for VAD or heart transplant (HTx) assessment (J Heart Lung Transplant 2016;35:1092-100). In this study, we examined the impact of frailty on post-HTx outcomes. Methods Ninety-six patients (53 men; 43 women) who underwent assessment of cognition and frailty within 12 months of HTx between 2013 and 2017 were included in the study. Frailty was defined as > 3 physical domains of the Fried Frailty Phenotype (FFP) or > 2 physical domains of the FFP plus cognitive impairment defined as a score of < 26/30 on the Montreal Cognitive Assessment (MoCA). Depression screening was also performed using the Depression in Medical Illness (DMI-10) score. Results Average time between frailty assessment and HTx was 4.5 + 3.1 months. Thirty were classified as frail (F), and 66 were not frail (NF). There was no significant difference in age (48.2±14 F vs 50.6±15, p=0.46), or BMI (24.9±5.5 vs 24.8±4.2, p=0.92) between groups, but a higher proportion of women than men were frail (42% vs 23%, p < 0.05). Pre-transplant mechanical support was utilised in a similar proportion of F (n=8/30) and NF (n=12/66) patients (p = 0.34). As expected, mean MoCA scores were lower in the F group (24 ± 4 vs 26 ± 3, p=0.0037). Depression as defined by a DMI score > 9 was also more common in the F group (53% vs 26%, p = 0.01). Frailty was an independent predictor of all-cause mortality after HTx with 1 yr survival 74 + 9% in the F group, compared to 98 + 2% in the NF group (p = 0.0003). There were trends towards longer median intubation times, ICU and hospital length of stay in the F group but differences were not significant. Conclusions Similar to our previous finding that frailty was an independent predictor of mortality in AHF patients referred for HTx assessment, frailty was also an independent predictor of mortality after HTx. These findings may help us better identify patients who will benefit most from transplant. NHMRC Program Grant 1074386.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".