Frailty and Clinical Outcomes in Patients Treated With Hemodialysis: A Prospective Cohort Study
Bibliographic record
Abstract
Rationale and Objective: Frailty is common among people with kidney failure treated with hemodialysis (HD). The objective was to describe how frailty evolves over time in people treated by HD, how improvements in frailty and frailty markers are associate with clinical outcomes, and the characteristics that are associated with improvement in frailty. Study Design: Prospective cohort study. Setting and Participants: Adults initiating thrice weekly in-center HD in Canada. Exposure: We classified frailty using a 5-point score (3 or more indicates frailty) based on physical inactivity, slowness or weakness, poor endurance or exhaustion, and malnutrition. We categorized the frailty trajectory as never present, improving, deteriorating, and always present. Outcomes: All-cause death, hospitalizations, and placement into long-term care. Analytical Approach: We examined the association between time-varying frailty measures and these outcomes using Cox and negative binomial models, after adjustment for potential confounders. Results: 985 participants were included and followed up for a median of 33 months; 507 (51%) died, 761 (77%) experienced ≥1 hospitalization and 115 (12%) entered long-term care. Overall, 760 (77%) reported frailty during follow-up. Three-quarters (78%) of those with frailty at baseline remained frail throughout the follow-up, 46% without baseline frailty became frail, and 23% with baseline frailty became nonfrail. Higher frailty scores were associated with an increased risk of mortality (fully adjusted HR, 1.58 per unit; 95% CI, 1.39-1.80) and an increased rate of hospitalization (RR, 1.16 per unit; 95% CI, 1.09-1.23). Compared with those who were frail throughout the follow-up, participants with frailty at baseline but improving during follow-up showed a lower mortality (HR, 0.59; 95% CI, 0.42-0.81), and a lower rate of hospitalization (RR, 0.70; 95% CI, 0.56-0.87). Limitations: There was missing data on frailty at baseline and during follow-up. Conclusions: Frailty was associated with a higher risk of poor outcomes compared with those without frailty, and participants whose status improved from frail to nonfrail showed better clinical outcomes than those who remained frail. These findings emphasize the importance of identifying and implementing effective treatments for frailty in patients receiving maintenance HD.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".