Risk of Chronic Kidney Disease in Hospitalized Patients with Hidradenitis Suppurativa
Bibliographic record
Abstract
BACKGROUND: Hidradenitis suppurativa (HS) is associated with several comorbidities such as diabetes mellitus and cardiovascular diseases. These comorbidities are also risk factors for chronic kidney disease (CKD), yet little is known about the risk of CKD in HS patients. OBJECTIVES: The objective was to study the prevalence of CKD in HS patients. METHODS: Cross-sectional population-based study using the United States National Inpatient Sample database between January 1, 2002 and December 31, 2012 was performed. RESULTS: We identified 23,767 hospital admissions for HS patients and 95,068 admissions for age- and gender-matched controls. The prevalence of CKD in HS patients was 6.3% (1,497/23,767) compared to non-HS controls which was 4.3% (4,052/95,068). The association of CKD was strongest in HS patients, who were ≥60 years old, 16.9% (475/2,811), male 7.3% (695/9,556), obese 7.8% (407/5,209), diabetic 12.5% (890/7,105), hyperlipidemic 13.3% (416/3,126), and had cardiovascular diseases 12.5% (631/5,045). The crude odds ratio of CKD in HS patients was 1.5 (95% CI: 1.420-1.605) compared to non-HS patients. The association remained significant after adjusting for important covariates with adjusted odds ratio of CKD in HS patients of 1.1 (95% CI: 1.014-1.176) compared to non-HS patients. CONCLUSIONS: Our findings show that there is a possible association of HS with CKD. Any signs of CKD should be assessed by a nephrologist as early diagnosis can hopefully prevent further progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".