Evaluation of Baseline Characteristics and Prognostic Factors in Multisystemic Inflammatory Syndrome in Children: Is It Possible to Foresee the Prognosis in the First Step?

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ÇETİN B. Ş., Kısaarslan A. P., TEKİN S., Goksuluk M. B., BAYKAN A., Akyıldız B. N., ...More

Journal of Clinical Medicine, vol.11, no.15, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 15
  • Publication Date: 2022
  • Doi Number: 10.3390/jcm11154615
  • Journal Name: Journal of Clinical Medicine
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, EMBASE, Directory of Open Access Journals
  • Keywords: COVID-19, multisystem inflammatory syndrome in children (MIS-C), risk classification, prognosis
  • Kayseri University Affiliated: No


© 2022 by the authors.Background: Two years after the first cases, critical gaps remain in identifying prognostic factors in multisystem inflammatory syndrome in children (MIS-C). Methods: This retrospective study included 99 patients with MIS-C hospitalized between August 2020 and March 2022 in a pediatric tertiary center. The patients were divided into two groups according to clinical severity (low- and high-risk). Prognostic values of baseline clinical and laboratory characteristics were evaluated with advanced statistical analysis, including machine learning. Results: Sixty-three patients were male, and the median age was 83 (3–205) months. Fifty-nine patients (59.6%) were low-risk cases. Patients aged six years and over tended to be at higher risk. Involvement of aortic or tricuspid valve or >1 valve was more frequent in the high-risk group. Mortality in previously healthy children was 3.2%. Intensive care unit admission and mortality rate in the high-risk group were 37.5% and 7.5%, respectively. At admission, high-risk patients were more likely to have reduced lymphocyte count and total protein level and increased brain natriuretic peptide (BNP), ferritin, D-dimer, and troponin concentrations. The multiple logistic regression model showed that BNP, total protein, and troponin were associated with higher risk. When the laboratory parameters were used together, BNP, total protein, ferritin, and D-dimer provided the highest contribution to the discrimination of the risk groups (100%, 89.6%, 85.6%, and 55.8%, respectively). Conclusions: Our study widely evaluates and points to some clinical and laboratory parameters that, at admission, may indicate a more severe course. Modeling studies with larger sample groups are strongly needed.