A Diagnostic Prediction Model to Distinguish Multisystem Inflammatory Syndrome in Children. Issue 12 (1st November 2022)

• Record Type: Journal Article
• Authors: Clark, Matthew T.; Rankin, Danielle A.; Peetluk, Lauren S.; Gotte, Alisa; Herndon, Alison; McEachern, William; Smith, Andrew; Clark, Daniel E.; Hardison, Edward; Esbenshade, Adam J.; Patrick, Anna; Halasa, Natasha B.; Connelly, James A.; Katz, Sophie E.
• Is Part Of: ACR open rheumatology. Volume 4:Issue 12(2022)
• Page Start: 1050
• Page End: 1059
• Publication Date: 2022-11-01
• Subjects: Rheumatology -- Periodicals; 616.723005
• DOI: 10.1002/acr2.11509
• Languages: English
• ISSNs: 2578-5745

Abstract

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.