Multisystem inflammatory syndrome in children and SARS-CoV-2 variants: a two-year ambispective multicentric cohort study in Catalonia, Spain.

Multisystem inflammatory syndrome in children (MIS-C) is a rare but severe disease temporarily related to SARS-CoV-2. We aimed to describe the epidemiological, clinical, and laboratory findings of all MIS-C cases diagnosed in children < 18 years old in Catalonia (Spain) to study their trend throughout the pandemic. This was a multicenter ambispective observational cohort study (April 2020-April 2022). Data were obtained from the COVID-19 Catalan surveillance system and from all hospitals in Catalonia. We analyzed MIS-C cases regarding SARS-CoV-2 variants for demographics, symptoms, severity, monthly MIS-C incidence, ratio between MIS-C and accumulated COVID-19 cases, and associated rate ratios (RR). Among 555,848 SARS-CoV-2 infections, 152 children were diagnosed with MIS-C. The monthly MIS-C incidence was 4.1 (95% CI: 3.4-4.8) per 1,000,000 people, and 273 (95% CI: 230-316) per 1,000,000 SARS-CoV-2 infections (i.e., one case per 3,700 SARS-CoV-2 infections). During the Omicron period, the MIS-C RR was 8.2 (95% CI: 5.7-11.7) per 1,000,000 SARS-CoV-2 infections, which was significantly lower (p < 0.001) than that for previous variant periods in all age groups. The median [IQR] age of MIS-C was 8 [4-11] years, 62.5% male, and 80.2% without comorbidities. Common symptoms were gastrointestinal findings (88.2%) and fever > 39 °C (81.6%); nearly 40% had an abnormal echocardiography, and 7% had coronary aneurysm. Clinical manifestations and laboratory data were not different throughout the variant periods (p > 0.05).  Conclusion: The RR between MIS-C cases and SARS-CoV-2 infections was significantly lower in the Omicron period for all age groups, including those not vaccinated, suggesting that the variant could be the main factor for this shift in the MISC trend. Regardless of variant type, the patients had similar phenotypes and severity throughout the pandemic. What is Known: • Before our study, only two publications investigated the incidence of MIS-C regarding SARS-CoV-2 variants in Europe, one from Southeast England and another from Denmark. What is New: • To our knowledge, this is the first study investigating MIS-C incidence in Southern Europe, with the ability to recruit all MIS-C cases in a determined area and analyze the rate ratio for MIS-C among SARS-CoV-2 infections throughout variant periods. • We found a lower rate ratio of MISC/infections with SARS-CoV-2 in the Omicron period for all age groups, including those not eligible for vaccination, suggesting that the variant could be the main factor for this shift in the MISC trend.

Symptom-Based Predictive Model of COVID-19 Disease in Children.

BACKGROUND: Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is neither always accessible nor easy to perform in children. We aimed to propose a machine learning model to assess the need for a SARS-CoV-2 test in children (<16 years old), depending on their clinical symptoms. METHODS: Epidemiological and clinical data were obtained from the REDCap® registry. Overall, 4434 SARS-CoV-2 tests were performed in symptomatic children between 1 November 2020 and 31 March 2021, 784 were positive (17.68%). We pre-processed the data to be suitable for a machine learning (ML) algorithm, balancing the positive-negative rate and preparing subsets of data by age. We trained several models and chose those with the best performance for each subset. RESULTS: The use of ML demonstrated an AUROC of 0.65 to predict a COVID-19 diagnosis in children. The absence of high-grade fever was the major predictor of COVID-19 in younger children, whereas loss of taste or smell was the most determinant symptom in older children. CONCLUSIONS: Although the accuracy of the models was lower than expected, they can be used to provide a diagnosis when epidemiological data on the risk of exposure to COVID-19 is unknown.