• Lauren S Starnes, Joseph R Starnes, Tess Stopczynski, Justin Z Amarin, Cara Charnogursky, Haya Hayek, Rana Talj, David A Parra, Daniel E Clark, Anna E Patrick, Sophie E Katz, Leigh M Howard, Lauren Peetluk, Danielle Rankin, Andrew J Spieker, and Natasha B Halasa.
• Department of Pediatrics, Vanderbilt University Medical Center, Division of Pediatric Hospital Medicine, Nashville, Tennessee, USA.
• J Hosp Med. 2024 Mar 1; 19 (3): 175184175-184.

BackgroundMultisystem inflammatory syndrome in children (MIS-C) is a rare but serious complication of severe acute respiratory syndrome coronavirus 2 infection. Features of MIS-C overlap with those of Kawasaki disease (KD).ObjectiveThe study objective was to develop a prediction model to assist with this diagnostic dilemma.MethodsData from a retrospective cohort of children hospitalized with KD before the coronavirus disease 2019 pandemic were compared to a prospective cohort of children hospitalized with MIS-C. A bootstrapped backwards selection process was used to develop a logistic regression model predicting the probability of MIS-C diagnosis. A nomogram was created for application to individual patients.ResultsCompared to children with incomplete and complete KD (N = 602), children with MIS-C (N = 105) were older and had longer hospitalizations; more frequent intensive care unit admissions and vasopressor use; lower white blood cell count, lymphocyte count, erythrocyte sedimentation rate, platelet count, sodium, and alanine aminotransferase; and higher hemoglobin and C-reactive protein (CRP) at admission. Left ventricular dysfunction was more frequent in patients with MIS-C, whereas coronary abnormalities were more common in those with KD. The final prediction model included age, sodium, platelet count, alanine aminotransferase, reduction in left ventricular ejection fraction, and CRP. The model exhibited good discrimination with AUC 0.96 (95% confidence interval: [0.94-0.98]) and was well calibrated (optimism-corrected intercept of -0.020 and slope of 0.99).ConclusionsA diagnostic prediction model utilizing admission information provides excellent discrimination between MIS-C and KD. This model may be useful for diagnosis of MIS-C but requires external validation.© 2024 Society of Hospital Medicine.

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