Cardiovascular comorbidities of atopic dermatitis: using National Health Insurance data in Korea
Bibliographic record
Abstract
BACKGROUND: It is well known that atopic dermatitis (AD) is associated with other allergic diseases. Recentely, links to diseases other than allergic disease have also been actively studied. Among them, the results of studies regarding AD comorbidities, especially cardiovascular disease (CVD), have varied from country to country. OBJECTIVE: To analyze whether the risk of CVD is different between AD patients and healthy controls using Korean National Health Insurance Data. METHODS: We obtained data from 2005 to 2016 from the Korean National Health Insurance Research Database. Patients with one AD code and two AD-related tests codes were selected as AD patients, and age-and sex-matched controls to the AD patients were selected from among those without AD (1:5). Each group was investigated for accompanying metabolic syndrome (which contains hypertension, type 2 diabetes, and hyperlipidemia) and CVD (angina, myocardial infarction, peripheral vascular disease, and stroke) using ICD 10 codes. RESULTS: The incidence of metabolic diseases and CVD were significantly different between the AD and control groups. Using multivariable Cox regression, differences were adjusted for sex, age, and other CVD and metabolic diseases. As a result, not only metabolic disease, but also the CVD risk of AD patients was significantly higher than that of the control group. Patients with AD had as significantly higher risk of hyperlipidemia (hazard ratio [HR] = 33.02, p < 0.001), hypertension (HR = 4.86, p < 0.001), and type 2 diabetes (HR = 2.96, p < 0.001). AD patients also had a higher risk of stroke (HR = 10.61, p < 0.001), myocardial infarction (HR = 9.43, p < 0.001), angina (HR = 5.99, p < 0.001), and peripheral vascular disease (HR = 2.46, p < 0.001). Besides hyperlipidemia, there was no difference in risk according to AD severity. CONCLUSION: Patients with AD have a greater risk of CVD than those without AD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".