Severe Pediatric COVID-19 and Multisystem Inflammatory Syndrome in Children from Wild-type to Population Immunity: A Prospective Multicenter Cohort Study with Real-time Reporting

Background: SARS-CoV-2 variant evolution and increasing immunity altered the impact of pediatric SARS-CoV-2 infection. Public health decision-making relies on accurate and timely reporting of clinical data. Methods: This international hospital-based multicenter, prospective cohort study with real-time reporting was active from March 2020 to December 2022. We evaluated longitudinal incident rates and risk factors for disease severity. Results: We included 564 hospitalized children with acute COVID-19 (n = 375) or multisystem inflammatory syndrome in children (n = 189) from the Netherlands, Curaçao and Surinam. In COVID-19, 134/375 patients (36%) needed supplemental oxygen therapy and 35 (9.3%) required intensive care treatment. Age above 12 years and preexisting pulmonary conditions were predictors for severe COVID-19. During omicron, hospitalized children had milder disease. During population immunity, the incidence rate of pediatric COVID-19 infection declined for older children but was stable for children below 1 year. The incidence rate of multisystem inflammatory syndrome in children was highest during the delta wave and has decreased rapidly since omicron emerged. Real-time reporting of our data impacted national pediatric SARS-CoV-2 vaccination- and booster-policies. Conclusions: Our data supports the notion that similar to adults, prior immunity protects against severe sequelae of SARS-CoV-2 infections in children. Real-time reporting of accurate and high-quality data is feasible and impacts clinical and public health decision-making. The reporting framework of our consortium is readily accessible for future SARS-CoV-2 waves and other emerging infections

Gene signature fingerprints stratify SLE patients in groups with similar biological disease profiles: a multicentre longitudinal study

OBJECTIVES: Clinical phenotyping and predicting treatment responses in SLE patients is challenging. Extensive blood transcriptional profiling has identified various gene modules that are promising for stratification of SLE patients. We aimed to translate existing transcriptomic data into simpler gene signatures suitable for daily clinical practice. METHODS: Real-time PCR of multiple genes from the IFN M1.2, IFN M5.12, neutrophil (NPh) and plasma cell (PLC) modules, followed by a principle component analysis, was used to identify indicator genes per gene signature. Gene signatures were measured in longitudinal samples from two childhood-onset SLE cohorts (n = 101 and n = 34, respectively), and associations with clinical features were assessed. Disease activity was measured using Safety of Estrogen in Lupus National Assessment (SELENA)-SLEDAI. Cluster analysis subdivided patients into three mutually exclusive fingerprint-groups termed (1) all-signatures-low, (2) only IFN high (M1.2 and/or M5.12) and (3) high NPh and/or PLC. RESULTS: All gene signatures were significantly associated with disease activity in cross-sectionally collected samples. The PLC-signature showed the highest association with disease activity. Interestingly, in longitudinally collected samples, the PLC-signature was associated with disease activity and showed a decrease over time. When patients were divided into fingerprints, the highest disease activity was observed in the high NPh and/or PLC group. The lowest disease activity was observed in the all-signatures-low group. The same distribution was reproduced in samples from an independent SLE cohort. CONCLUSIONS: The identified gene signatures were associated with disease activity and were indicated to be suitable tools for stratifying SLE patients into groups with similar activated immune pathways that may guide future treatment choices