These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: A nomogram for predicting immunoglobulin-resistant Kawasaki disease in children.
    Author: Pan Y, Fan Q.
    Journal: J Int Med Res; 2023 Feb; 51(2):3000605221139704. PubMed ID: 36802838.
    Abstract:
    OBJECTIVE: This case-control study focused on the establishment and internal validation of a risk nomogram for intravenous immunoglobulin (IVIG)-resistant Kawasaki disease (KD) using the Kawasaki Disease Database. METHODS: The Kawasaki Disease Database is the first public database for KD researchers. A prediction nomogram for IVIG-resistant KD was constructed using multivariable logistic regression. Then, the C-index was used to assess the discriminating ability of the proposed prediction model, a calibration plot was drawn to evaluate its calibration, and a decision curve analysis was adopted to assess its clinical usefulness. Bootstrapping validation was performed for interval validation. RESULTS: The median ages of IVIG-resistant and -sensitive KD groups were 3.3 and 2.9 years, respectively. Predicting factors incorporated into the nomogram were coronary artery lesions, C-reactive protein, percentage of neutrophils, platelets, aspartate aminotransferase, and alanine transaminase. Our constructed nomogram exhibited favorable discriminating ability (C-index: 0.742; 95% confidence interval: 0.673-0.812) and excellent calibration. Moreover, interval validation achieved a high C-index of 0.722. CONCLUSIONS: The as-constructed new IVIG-resistant KD nomogram that incorporated C-reactive protein, coronary artery lesions, platelets, percentage of neutrophils, alanine transaminase, and aspartate aminotransferase may be adopted for predicting the risk of IVIG-resistant KD.
    [Abstract] [Full Text] [Related] [New Search]