Frailty Impact on Kidney Transplantation in Older People
Bibliographic record
Abstract
Introduction: Kidney transplantation (KT) is increasing in older people. This cohort are vulnerable to frailty, which affects KT outcomes. This study investigated the impact of frailty on quality of life (QoL) and clinical outcomes in older KT candidates and recipients, improving understanding in this population specifically. Methods: KT in Older People (KTOP): Impact of Frailty on Outcomes was a prospective, single-center, longitudinal, observational study. Older people (aged ≥ 60 years) listed for KT were recruited. Frailty was assessed using the Edmonton Frail Scale (EFS). Patient-reported outcomes (PROs) were evaluated using validated questionnaires. Waitlist and KT clinical outcomes were recorded. Descriptive, comparative, and mixed-effect analyses were used to determine PRO and clinical outcome variation by frailty. Results: Two hundred ten participants were recruited, of which 120 were transplanted. At recruitment, 17.2% were frail and 19.4% were vulnerable. Frailty was associated with poorer PROs after KT across all questionnaires. Vulnerable/frail recipients experienced worsening symptom burden, mental QoL, and depression. Nonfrail recipients experienced early physical and mental QoL declines. Both groups reported improved treatment satisfaction and illness intrusiveness. Delayed graft function, 12-month graft function, and KT length-of-stay (LoS) were poorer in vulnerable/frail recipients. On the waitlist, nonfrail participants reported stable PROs, whereas vulnerable/frail participants experienced fluctuations, more infection events, and longer suspensions. Conclusion: The KTOP study provides a detailed, holistic, and longitudinal description of frailty's influence throughout the KT journey in older people. These findings are crucial to enabling more accurate discussions, better shared decision-making, and targeted interventions and clearer goals of KT to be determined.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".