Urinary Colorimetric Sensor Array and Algorithm to Distinguish Kawasaki Disease from Other Febrile Illnesses

PLoS One. 2016 Feb 9;11(2):e0146733. doi: 10.1371/journal.pone.0146733. eCollection 2016.

Abstract

Objectives: Kawasaki disease (KD) is an acute pediatric vasculitis of infants and young children with unknown etiology and no specific laboratory-based test to identify. A specific molecular diagnostic test is urgently needed to support the clinical decision of proper medical intervention, preventing subsequent complications of coronary artery aneurysms. We used a simple and low-cost colorimetric sensor array to address the lack of a specific diagnostic test to differentiate KD from febrile control (FC) patients with similar rash/fever illnesses.

Study design: Demographic and clinical data were prospectively collected for subjects with KD and FCs under standard protocol. After screening using a genetic algorithm, eleven compounds including metalloporphyrins, pH indicators, redox indicators and solvatochromic dye categories, were selected from our chromatic compound library (n = 190) to construct a colorimetric sensor array for diagnosing KD. Quantitative color difference analysis led to a decision-tree-based KD diagnostic algorithm.

Results: This KD sensing array allowed the identification of 94% of KD subjects (receiver operating characteristic [ROC] area under the curve [AUC] 0.981) in the training set (33 KD, 33 FC) and 94% of KD subjects (ROC AUC: 0.873) in the testing set (16 KD, 17 FC). Color difference maps reconstructed from the digital images of the sensing compounds demonstrated distinctive patterns differentiating KD from FC patients.

Conclusions: The colorimetric sensor array, composed of common used chemical compounds, is an easily accessible, low-cost method to realize the discrimination of subjects with KD from other febrile illness.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Algorithms*
  • Colorimetry / instrumentation*
  • Data Mining
  • Decision Support Systems, Clinical*
  • Diagnosis, Differential
  • Female
  • Fever / diagnosis*
  • Humans
  • Male
  • Middle Aged
  • Mucocutaneous Lymph Node Syndrome / diagnosis*
  • Urinalysis / instrumentation*

Grants and funding

This work was supported by Stanford University Spark Program (2013–2014) (URL: http://med.stanford.edu/sparkmed.html) to XBL and HJC, and American Heart Association (14GRNT20510026) (URL: www.heart.org) to XBL and HJC. This work was supported by the Chinese Scholarship Council (CSC) (URL: http://en.csc.edu.cn/) to ZL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.