Risk Factors for Fatigue in Adults Receiving Maintenance Hemodialysis Who Have Chronic Pain: A Secondary Analysis of the HOPE Consortium Trial
Bibliographic record
Abstract
Rationale & Objective: Fatigue is commonly experienced by adults with kidney failure receiving hemodialysis and those with chronic pain, but factors associated with fatigue are not fully understood. We determined the prevalence of fatigue in a clinical trial cohort of adults receiving maintenance hemodialysis who have chronic pain and identified factors associated with fatigue. Study Design: A cross-sectional study. Setting & Participants: The baseline data from the HOPE Consortium Trial to Reduce Pain and Opioid Use in Hemodialysis (HOPE Trial). Of the 643 participants randomized in the HOPE Trial, 636 had a baseline fatigue assessment and were included in this study. Exposures: Pain, sociodemographic, biological, dialysis-related, medical comorbid condition, psychological, and behavioral factors. Outcome: Fatigue was evaluated with the patient-reported outcomes measurement information system Fatigue SF 6a and defined as a T-score of ≥ 55. Analytical Approach: Logistic regression models. Results: Seventy-three percent of participants reported fatigue (n = 463), mean age was 60.4 (12.5), 289 (45.4%) were female, and 294 (46.2%) were Black/African American. In fully adjusted models, higher pain interference and opioid use in the last 14 days were each associated with higher odds of having fatigue (odds ratio ([OR) ] 1.37; 95% CI, 1.18-1.61; OR 1.80; 95% CI, 1.03-3.21, respectively), as were greater depressive symptoms and sleep disturbance (OR 1.21; 95% CI. 1.13-1.31; OR 1.08 95% CI 1.03-1.12, respectively). Higher physical function was associated with lower odds of having fatigue (OR 0.96 95% CI 0.93-0.99). Limitations: Fatigue assessed at one point in time. Conclusions: In adults receiving maintenance hemodialysis who have chronic pain, pain interference, opioid use, depression, and sleep disturbances are associated with increased odds of fatigue, and greater physical function is associated with lower odds of fatigue. Future work is needed to evaluate longitudinal associations, underlying mechanisms, and identify interventions.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".