Frailty Prevalence in Younger End-Stage Kidney Disease Patients Undergoing Dialysis and Transplantation
Bibliographic record
Abstract
BACKGROUND: Frailty, originally characterized in community-dwelling older adults, is increasingly being studied and implemented for adult patients with end-stage kidney disease (ESKD) of all ages (>18 years). Frailty prevalence and manifestation are unclear in younger adults (18-64 years) with ESKD; differences likely exist based on whether the patients are treated with hemodialysis (HD) or kidney transplantation (KT). METHODS: We leveraged 3 cohorts: 378 adults initiating HD (2008-2012), 4,304 adult KT candidates (2009-2019), and 1,396 KT recipients (2008-2019). The frailty phenotype was measured within 6 months of dialysis initiation, at KT evaluation, and KT admission. Prevalence of frailty and its components was estimated by age (≥65 vs. <65 years). A Wald test for interactions was used to test whether risk factors for frailty differed by age. RESULTS: In all 3 cohorts, frailty prevalence was higher among older than younger adults (HD: 71.4 vs. 47.3%; candidates: 25.4 vs. 18.8%; recipients: 20.8 vs. 14.3%). In all cohorts, older patients were more likely to have slowness and weakness but less likely to report exhaustion. Among candidates, older age (odds ratio [OR] = 1.79, 95% CI: 1.47-2.17), non-Hispanic black race (OR = 1.30, 95% CI: 1.08-1.57), and dialysis type (HD vs. no dialysis: OR = 2.06, 95% CI: 1.61-2.64; peritoneal dialysis vs. no dialysis: OR = 1.78, 95% CI: 1.28-2.48) were associated with frailty prevalence, but sex and Hispanic ethnicity were not. These associations did not differ by age (pinteractions > 0.1). Similar results were observed for recipients and HD patients. CONCLUSIONS: Although frailty prevalence increases with age, younger patients have a high burden. Clinicians caring for this vulnerable population should recognize that younger patients may experience frailty and screen all age groups.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".