학술논문

Pediatric Prediction Model for Low Immunoglobulin G Level Based on Serum Globulin and Illness Status
Document Type
article
Source
Frontiers in Immunology, Vol 13 (2022)
Subject
globulin
hypogammaglobulinemia
prediction model
screening test
immunoglobulin G
Immunologic diseases. Allergy
RC581-607
Language
English
ISSN
1664-3224
Abstract
Hypogammaglobulinemia is a condition that requires prompt diagnosis and treatment. Unfortunately, serum immunoglobulin (Ig) measurements are not widely accessible in numerous developing countries. Serum globulin is potentially the best candidate for screening of low IgG level (IgGLo) due to its high availability, low cost, and rapid turnover time. However, multiple factors may influence the probability of prediction. Our study aimed to establish a simple prediction model using serum globulin to predict the likelihood of IgGLo in children. For retrospective data of patients who were suspected of having IgGLo, both serum IgG and globulin were simultaneously collected and measured. Potential factors interfering with serum globulin and IgG levels were investigated for their impact using bivariate binary logistic regression. A multivariate binary logistic regression was used to generate a formula and score to predict IgGLo. We obtained 953 samples from 143 pediatric patients. A strong positive correlation between serum globulin and IgG levels was observed (r=0.83, p < 0.001). A screening test model using serum globulin and illness status was constructed to predict IgGLo. The formula for predicting IgGLo was generated as follows; Predicted score = (2 x globulin (g/dl)) – illness condition score (well=0, sick=1). When the score was