BMC Pediatrics (Jul 2024)

Guideline for the diagnosis and treatment of incomplete Kawasaki disease in children in China

  • Fuyong Jiao,
  • Yan Pan,
  • Zhongdong Du,
  • Fangming Deng,
  • Xiaodong Yang,
  • Hong Wang,
  • Jie Shen,
  • Wei Xiang,
  • Zhilong Mu,
  • Chunyan Gao,
  • Jinmei Bai

DOI
https://doi.org/10.1186/s12887-024-04961-2
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 13

Abstract

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Abstract Background Kawasaki disease (KD) is a pyretic ailment predominantly observed in children aged below 5 years. There is currently a dearth of precise markers for timely identification of incomplete Kawasaki disease (IKD). It is imperative to develop updated, comprehensive, and evidence-based guidelines to effectively direct clinical practice. Methods The guideline development group comprised individuals with diverse expertise in both content and methodology and carried out an extensive exploration of the following digital repositories: CNKI, VIP, Wanfang Data, UpToDate, BMJ, Clinical Evidence, National Guideline Clearinghouse, Joanna Briggs Institute Library, Cochrane Library, and PubMed. The entire period from the establishment of these databases until January 1, 2024 was covered. To evaluate IKD, systematic reviews and randomised controlled trials were assessed using the risk of prejudice instrument specified in the Cochrane Handbook, along with the evidence robustness framework established by the GRADE group. The recommendations were formulated based on the findings, considering the evidence strength. After several iterations of expert consensus, the relevant professional committees in China endorsed the ultimate guideline. Results These guidelines address clinical questions regarding the classification and definition of KD, diagnosis of IKD, treatment during the acute phase of IKD, and follow-up of IKD. Conclusions To provide healthcare professionals with guidance and decision-making bases for the diagnosis and treatment of IKD in China, 13 recommendations were formulated based on expert consensus and evidence of best practices.

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