Comparison of IVIG resistance predictive models in Kawasaki disease

Abstract

Background: We aimed to compare the ten different scores (by Kobayashi, Egami, Harada, Formosa, Sano, Piram et al., Wu et al., Yang et al., Tan et al., and Kanai et al.) to assess their performance in predicting IVIG resistance in Turkish children.

Methods: Complete and incomplete KD patients diagnosed with KD at Hacettepe University between June 2007 and September 2019 were evaluated retrospectively.

Results: A total of 129 patients, 79 boys (61.2%), with a median age 36 (IQR 19.5-57.0) months were evaluated. Sixteen patients (12.4%) had IVIG resistance. Sensitivity was low for all the ten scores. Tan, Sano, and Egami predictive models had the highest specificity (97.3, 89.4, 86.7%, respectively). Almost all scoring systems distinguished the group of patients with low risk for IVIG resistance but could not differentiate IVIG-resistant patients. Multivariate analysis for the laboratory features showed that platelet count <300 × 10⁹/L and GGT serum levels were independent risk factors for IVIG resistance (OR: 3.896; 95% CI: 1.054-14.404; p = 0.042 and OR: 1.008; 95% CI: 1.001-1.015; p = 0.050).

Conclusions: The current scoring systems had a low sensitivity for predicting the risk for IVIG resistance in Turkish children. On the other hand, increased serum GGT levels and low platelet count were risk factors for predicting IVIG resistance.

Impact: Intravenous immunoglobulin (IVIG) resistance may be observed in 10-20% of patients diagnosed with Kawasaki disease. Coronary artery involvement is more frequent in IVIG-resistant patients. It is important to predict the patients who might develop IVIG resistance to improve prognosis. The performance of the IVIG resistance predictive models in Kawasaki disease in our population is limited due to the low sensitivity.