Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Atopic dermatitis (AD) has the highest burden of any skin disease; however, the severity-associated factors remain unclear. To evaluate potential severity-associated factors of AD and to design and validate a severity prediction model to inform the management of AD patients. A cross-sectional study of 900 AD patients was conducted from December 2021 to October 2022 at our hospital. The primary outcome was disease severity, categorized as mild, moderate, or severe using the scoring atopic dermatitis index. Ordinal logistic regression and bootstrapped validation were used to derive and internally validate the model. Increasing age, elevated eosinophil level, higher economic status, and urban residence were associated with severe AD. Breastfeeding, disinfectants and topical emollients use, and short duration of bathing were associated with mild AD. In the prediction model, predictors included age, eosinophil and economic status, residence, feeding, disinfectants and emollients use, and duration of bathing. Prediction models demonstrated good discrimination (bias-corrected concordance index [c-index] = 0.72) and good calibration. Risk factors for the severity of AD were identified that could aid the early prediction of AD progression. The predictive model included variables that are easily evaluated and could inform personalized prevention and therapy.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1089/derm.2023.0037 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!