A Diagnostic Prediction Model to Distinguish Multisystem Inflammatory Syndrome in Children

Abstract

Objective: Features of multisystem inflammatory syndrome in children (MIS-C) overlap with other syndromes, making the diagnosis difficult for clinicians. We aimed to compare clinical differences between patients with and without clinical MIS-C diagnosis and develop a diagnostic prediction model to assist clinicians in identification of patients with MIS-C within the first 24 hours of hospital presentation. Methods: A cohort of 127 patients (<21 years) were admitted to an academic children's hospital and evaluated for MIS-C. The primary outcome measure was MIS-C diagnosis at Vanderbilt University Medical Center. Clinical, laboratory, and cardiac features were extracted from the medical record, compared among groups, and selected a priori to identify candidate predictors. Final predictors were identified through a logistic regression model with bootstrapped backward selection in which only variables selected in more than 80% of 500 bootstraps were included in the final model. Results: Of 127 children admitted to our hospital with concern for MIS-C, 45 were clinically diagnosed with MIS-C and 82 were diagnosed with alternative diagnoses. We found a model with four variables—the presence of hypotension and/or fluid resuscitation, abdominal pain, new rash, and the value of serum sodium—showed excellent discrimination (concordance index 0.91; 95% confidence interval: 0.85-0.96) and good calibration in identifying patients with MIS-C. Conclusion: A diagnostic prediction model with early clinical and laboratory features shows excellent discrimination and may assist clinicians in distinguishing patients with MIS-C. This model will require external and prospective validation prior to widespread use.