657 - Impact of pruritus on patient fatigue: a case-control study
Bibliographic record
Abstract
Abstract Background Pruritus, either as a chronic standalone disease or secondary to inflammatory skin disease, can have a very detrimental effect on the overall quality of life for patients suffering from intense itch, particularly on patients suffering from atopic dermatitis. Symptoms of itching have a reported association with worsened quality of life across various psychological and social domains, including fewer periods of rest and reduced sleep quality. While prior studies have been made to determine the relationship between both fatigue and pruritus combined on systematic disease severity, there is limited data on how the prevalence of pruritus directly correlates with the prevalence of fatigue. Objective We utilized the AoU database to perform a nested case-control study to determine the impact of pruritus on patient-reported fatigue. Methods Within this database, patients experiencing pruritus (chronic pruritus, chronic pruritus of unknown origin (CPUO), prurigo nodularis, psoriasis, and atopic dermatitis) were identified and matched to four controls using nearest neighbor propensity-score matching, with sex, age, and race/ethnicity. Comparative analyses between pruritus cases and controls were performed utilizing the Fisher's exact test for categorical variables and the unpaired t-test for continuous variables. Logistic regression models were developed to calculate the odds ratio (OR) of having pruritus (SNOMED: 279333002, ICD10CM-L29), and developing fatigue (SNOMED: 84229001, ICD10CM-R53.83), with covariates including age, race/ethnicity, sex, income, education, anxiety, and depression. A significance level was set at P<0.05, and 95% confidence intervals were developed using the Wald method. Results From the cohort with accessible electronic health records (EHR) in 91,212 controls and 22,803 cases of pruritus were identified (mean age 59.91 [standard deviation: 15.95], 63.57% female). Patients afflicted with pruritus demonstrated a noticeably elevated prevalence of fatigue (40.81%) compared to the control group (22.06%). After adjusting for demographics and other covariates in multivariable analysis, chronic pruritus remained significantly associated with fatigue, with a multivariable adjusted odds ratio (aOR) of 1.56 (95% CI 1.16-2.07. In-depth analysis revealed that specific pruritic conditions exhibited strong associations with fatigue, namely, CPOU (aOR 2.27, 95% CI 1.35-3.82), prurigo nodularis (aOR 2.21, 95% CI 1.87-2.61), and atopic dermatitis (Aor 2.05, 95% CI 1.97-2.13). Conclusion Our study provides a quantitative measure of pruritus’ impact on the prevalence of patient-reported fatigue across many pruritic conditions, including atopic dermatitis. This two-fold increase in fatigue in patients with itch further emphasizes the clinical importance of understanding the impact of pruritus on patient quality of life.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".