Symptom burden, fatigue, sleep quality and perceived social support in hemodialysis patients with musculoskeletal discomfort: a single center experience from Egypt
Bibliographic record
Abstract
BACKGROUND AND AIMS: Musculoskeletal disorders (MSDs) are commonly encountered in hemodialysis (HD) patients. However, the causes linked to these disorders are still partially defined. The aim of this study was to determine the frequency of MSDs and their relationship to a variety of clinico-social characteristics such as sleep quality, mood disorders, fatigue, and social support, in addition to the patients' clinical and therapeutic profile. METHOD: The study included 94 patients on maintenance HD. Clinical and Sociodemographic data was gathered. To investigate the prevalence and trends of MSDs, the Nordic Musculoskeletal Questionnaire (NMQ-E) was employed. Patients completed the modified Edmonton Symptom Assessment System, Pittsburgh Sleep Quality Index (PSQI), multidimensional Fatigue Inventory (MFI-20), and Perceived Social Support from Family Scales. Univariate and multivariate regression analysis were used to assess the determinants of MSDs. RESULTS: The patients' mean age was 49.73 and 59.6% were males. Seventy-two percent of patients were afflicted by MSDs. Knee pain (48.9%), low back pain (43.6%), shoulder pain (41.6%), hip/thigh pain (35.1%), and neck pains (35.1%) were the most reported MSD domains. Pain (p = 0.001), fatigue (p = 0.01), depression (p = 0.015), and anxiety (p = 0.003) scores were substantially higher in patients with MSDs. Furthermore, patients with MSDs engaged in less physical activity (p = 0.02) and perceived less social support (p = 0.029). Patients with MSDs had lower subjective sleep quality, daytime dysfunction domains, and global PSQI scores (p = 0.02, 0.031, 0.036, respectively). Female gender (p = 0.013), fatigue (p = 0.012), depression (p = 0.014), anxiety (p = 0.004), lower activity (p = 0.029), and PSQI score (0.027), use of erythropoiesis-stimulating agents (ESAs), antihypertensive drugs, calcium and Iron supplementation were all significantly associated with MSDs. At the multivariable regression model, administration of ESAs (p = 0.017) and pain score (p = 0.040) were the only independent variables associated with the outcome. CONCLUSION: MSDs are quite common among HD patients. Female gender, pain, fatigue, depression, anxiety, reduced activity, poor sleep quality, and use of ESAs are all significantly associated with MSDs in HD patients. Patients with MSD perceived less social support compared to the other group. Patients treated with antihypertensive drugs, calcium and iron supplements were more likely to suffer MSDs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".