Sleep apnea in hemodialysis patients: Risk factors and effect on survival
Bibliographic record
Abstract
UNLABELLED: Sleep disorders are common in patients with end-stage renal disease (ESRD). Using a simple questionnaire, we estimate the probability of sleep apnea in ESRD patients, determine the factors associated with a higher probability of sleep apnea, and determine the association between the probability of sleep apnea and cardiovascular and all-cause mortality. STUDY DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: prevalent hemodialysis patients (n=270) in 7 urban outpatient hemodialysis units. PREDICTOR: Probability of sleep apnea as quantified by the Flemons questionnaire. OUTCOMES AND MEASUREMENTS: Clinical, demographic, and dialysis-related characteristics were obtained at baseline. Total and cardiovascular mortality was ascertained after a median follow-up of 34 months. The probability of sleep apnea was low in 79 (29%) patients, moderate in 116 (43%) patients, and high in 75 (28%) patients. Male gender (odds ratio [OR] 5.13, p<0.001), obesity (BMI >30, OR 7.58, p<0.01), and interdialytic weight gain (OR 1.72/kg change, p<0.004) were independently associated with a high probability of sleep apnea. A high probability of sleep apnea at baseline did not predict total (hazard ratio [HR] 0.81, p=NS) or cardiovascular mortality (HR 0.9, p=NS). The Flemons questionnaire is validated in the general population, but has not been tested specifically in hemodialysis patients. The study may not be adequately powered to detect a difference in mortality. A high proportion of hemodialysis patients are likely to have sleep apnea; a simple bedside questionnaire can be used for screening to identify these patients. Excessive interdialytic weight gain is a potentially modifiable factor that increases the likelihood of sleep apnea. Despite the presence of a strong association between sleep apnea and mortality in the general population, a similar association could not be demonstrated in ESRD patients with a high prevalence of this condition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".