Prediction Models for Intravenous Immunoglobulin Resistance in Kawasaki Disease: A Meta-analysis

Abstract

CONTEXT Approximately 10% to 20% of patients with Kawasaki disease (KD) are refractory to initial intravenous immunoglobulin (IVIG) therapy. KD is mainly associated with coronary artery abnormalities. OBJECTIVES To identify and evaluate all developed prediction models for IVIG resistance in patients with KD and synthesize evidence from external validation studies that evaluated their predictive performances. DATA SOURCES PubMed Medline, Dialog Embase, the Cochrane Central Register of Controlled Trials, the World Health Organization International Clinical Trials Registry Platform, and ClinicalTrials.gov were searched from inception until October 5, 2021. STUDY SELECTION All cohort studies that reported patients diagnosed with KD who underwent an initial IVIG of 2 g/kg were selected. DATA EXTRACTION Study and patient characteristics and model performance measures. Two authors independently extracted data from the studies. RESULTS The Kobayashi, Egami, Sano, Formosa, and Harada scores were the only prediction models with 3 or more external validation of the161 model analyses in 48 studies. The summary C–statistics were 0.65 (95% confidence interval [CI]: 0.57–0.73), 0.63 (95% CI: 0.55–0.71), 0.58 (95% CI: 0.55–0.60), 0.50 (95% CI: 0.36–0.63), and 0.63 (95% CI: 0.44–0.78) for the Kobayashi, Egami, Sano, Formosa, and Harada models, respectively. All 5 models showed low positive predictive values (0.14–0.39) and high negative predictive values (0.85–0.92). LIMITATIONS Potential differences in the characteristics of the target population among studies and lack of assessment of calibrations. CONCLUSIONS None of the 5 prediction models with external validation accurately distinguished between patients with and without IVIG resistance.