Pediatric Emergency Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, via Massarenti 9, 40138, Bologna, Italy.
Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy.
Eur J Pediatr. 2023 Nov;182(11):4889-4895. doi: 10.1007/s00431-023-05142-6. Epub 2023 Aug 19.
Children with Kawasaki disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), and Adenovirus infections (AI) of the upper respiratory tract show overlapping features. This study aims to develop a scoring system based on clinical or laboratory parameters to differentiate KD or MIS-C from AI patients. Ninety pediatric patients diagnosed with KD (n = 30), MIS-C (n = 26), and AI (n = 34) admitted to the Pediatric Emergency Unit of S.Orsola University Hospital in Bologna, Italy, from April 2018 to December 2021 were enrolled. Demographic, clinical, and laboratory data were recorded. A multivariable logistic regression analysis was performed, and a scoring system was subsequently developed. A simple model (clinical score), including five clinical parameters, and a complex model (clinic-lab score), resulting from the addition of one laboratory parameter, were developed and yielded 100% sensitivity and 80% specificity with a score ≥2 and 98.3% sensitivity and 83.3% specificity with a score ≥3, respectively, for MIS-C and KD diagnosis, as compared to AI.
This scoring system, intended for both outpatients and inpatients, might limit overtesting, contribute to a more effective use of resources, and help the clinician not underestimate the true risk of KD or MIS-C among patients with an incidental Adenovirus detection.
• Kawasaki Disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C) and adenoviral infections share overlapping clinical presentation in persistently febrile children, making differential diagnosis challenging. • Scoring systems have been developed to identify high-risk KD patients and discriminate KD from MIS-C patients.
• This is the first scoring model based on clinical criteria to distinguish adenoviral infection from KD and MIS-C. • The score might be used by general pediatricians before referring febrile children to the emergency department.
患有川崎病 (KD)、儿童多系统炎症综合征 (MIS-C) 和腺病毒感染 (AI) 的儿童表现出重叠特征。本研究旨在制定一种基于临床或实验室参数的评分系统,以区分 KD 或 MIS-C 与 AI 患者。2018 年 4 月至 2021 年 12 月,意大利博洛尼亚圣奥尔索拉大学医院儿科急诊室收治了 90 名诊断为 KD (n=30)、MIS-C (n=26) 和 AI (n=34) 的儿科患者。记录了人口统计学、临床和实验室数据。进行了多变量逻辑回归分析,并随后开发了评分系统。建立了一个简单的模型(临床评分),包括五个临床参数,和一个复杂的模型(临床-实验室评分),在一个实验室参数的基础上,分别得到了 100%的敏感性和 80%的特异性,评分≥2,98.3%的敏感性和 83.3%的特异性,评分≥3,用于 MIS-C 和 KD 诊断,与 AI 相比。
该评分系统,适用于门诊和住院患者,可能限制过度检测,有助于更有效地利用资源,并帮助临床医生在偶然发现腺病毒检测的情况下不会低估 KD 或 MIS-C 的真实风险。
•川崎病 (KD)、儿童多系统炎症综合征 (MIS-C) 和腺病毒感染在持续发热的儿童中具有重叠的临床表现,使鉴别诊断具有挑战性。•已经开发了评分系统来识别高危 KD 患者并区分 KD 与 MIS-C 患者。
•这是第一个基于临床标准的评分模型,用于区分腺病毒感染与 KD 和 MIS-C。•该评分可由普通儿科医生在将发热儿童转至急诊科之前使用。