Six-month multidisciplinary follow-up in multisystem inflammatory syndrome in children: An Italian single-center experience

Frontiers in Pediatrics(2023)

引用 4|浏览6
暂无评分
摘要
BackgroundA severe multisystem inflammatory syndrome in children (MIS-C) related to SARS-CoV-2 has been described after infection. A limited number of reports have analyzed the long-term complications related to pro-inflammatory status in MIS-C. We evaluated multiorgan impairment at the 6-month follow-up in MIS-C. MethodsWe enrolled 33 pediatric patients consecutively hospitalized for MIS-C and monitored for almost 6 months. The inter-relationship of patient's features and disease severity at admission with long term complications was studied by multivariate analysis. ResultsEndo-metabolic derangement, cardiac injury, respiratory, renal and gastrointestinal manifestations and neurological involvement are part of the initial presentation. The most abnormalities appear to resolve within the first few weeks, without significant long term dysfunction at the 6-months follow-up, except for endocrine (non-thyroidal illness syndrome in 12.1%, insulin resistance in 21.2%) and neurological system (27.3% cognitive or psychological, behavioral, adaptive difficulties). Endocrine and heart involvement at admission represent a significant factor on the long term sequelae; however no association between severity score and long-term outcome was noted. ConclusionsThe severity of initial clinical presentation may be associated to organ domain, however it is not related to long term sequelae. The prevalent organ restoration supports a predominant indirect immune-mediated injury triggered by a systemic inflammatory response; however a direct damage due to the viral entry could be not excluded. Eventhought our preliminary results seem to suggest that MIS-C is not a long-term risk condition for children health, a longer follow-up is mandatory to confirm this hypothesis.
更多
查看译文
关键词
multisystem inflammatory syndrome,children,SARS-CoV2,follow-up,COVID-19,complications
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要