Point-of-care differentiation of Kawasaki disease from other febrile illnesses.


Journal Article

To test whether statistical learning on clinical and laboratory test patterns would lead to an algorithm for Kawasaki disease (KD) diagnosis that could aid clinicians.Demographic, clinical, and laboratory data were prospectively collected for subjects with KD and febrile controls (FCs) using a standardized data collection form.Our multivariate models were trained with a cohort of 276 patients with KD and 243 FCs (who shared some features of KD) and validated with a cohort of 136 patients with KD and 121 FCs using either clinical data, laboratory test results, or their combination. Our KD scoring method stratified the subjects into subgroups with low (FC diagnosis, negative predictive value >95%), intermediate, and high (KD diagnosis, positive predictive value >95%) scores. Combining both clinical and laboratory test results, the algorithm diagnosed 81.2% of all training and 74.3% of all testing of patients with KD in the high score group and 67.5% of all training and 62.8% of all testing FCs in the low score group.Our KD scoring metric and the associated data system with online (http://translationalmedicine.stanford.edu/cgi-bin/KD/kd.pl) and smartphone applications are easily accessible, inexpensive tools to improve the differentiation of most children with KD from FCs with other pediatric illnesses.

Full Text

Duke Authors

Cited Authors

  • Ling, XB; Kanegaye, JT; Ji, J; Peng, S; Sato, Y; Tremoulet, A; Burns, JC; Cohen, HJ

Published Date

  • January 2013

Published In

Volume / Issue

  • 162 / 1

Start / End Page

  • 183 - 188.e3

PubMed ID

  • 22819274

Pubmed Central ID

  • 22819274

Electronic International Standard Serial Number (EISSN)

  • 1097-6833

International Standard Serial Number (ISSN)

  • 0022-3476

Digital Object Identifier (DOI)

  • 10.1016/j.jpeds.2012.06.012


  • eng