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Exploring the diagnostic value of CLR and CPR in differentiating Kawasaki disease from other infectious diseases

Jin-Wen Liao¹, Xin Guo², Xu-Xia Li³

¹The Department of Pediatrics, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City (Longgang Maternity and Child Institute of Shantou University Medical College), Shenzhen, Guangdong Province, China.

Frontiers in Pediatrics

|March 4, 2024

Insights

This study developed a predictive model to help differentiate Kawasaki disease (KD) from infectious diseases in children. Early identification aids timely treatment with intravenous immunoglobulin (IVIG), reducing risks of coronary artery lesions (CAL).

Area of Science:

Pediatric Cardiology
Infectious Diseases
Diagnostic Model Development

Background:

Kawasaki disease (KD) is a leading cause of acquired heart disease in children.
Atypical presentations of KD can mimic infectious diseases, leading to misdiagnosis.
Delayed diagnosis of KD increases the risk of coronary artery lesions (CAL) and IVIG resistance.

Purpose of the Study:

To develop a predictive model for distinguishing KD from infectious diseases in pediatric patients.
To aid clinicians in making timely and accurate treatment decisions for children presenting with febrile illnesses.

Main Methods:

Retrospective analysis of 1,377 children (187 KD, 1,190 infectious diseases) from Shenzhen Longgang District Maternity & Child Healthcare Hospital.
Logistic regression and LASSO regression analyses were used to build a predictive model.
Model validation using calibration curves and C-index.

Main Results:

Fifteen independent risk factors for KD were identified using LASSO analysis.
A nomogram was constructed using 7 variables: WBC, Monocyte (MO), ESR, ALT, ALB, CPR, and CLR.
The predictive model demonstrated high accuracy with a C-index of 0.969.

Conclusions:

The developed predictive model effectively discriminates KD from infectious diseases in children.
This tool can support early decisions regarding intravenous immunoglobulin (IVIG) and antibiotic use.
Improved diagnostic accuracy can lead to prompt treatment and better patient outcomes.

Read full analysis

Abstract

Background:

Kawasaki disease (KD) is an important cause of acquired heart disease in children and adolescents worldwide. KD and infectious diseases can be easily confused when the clinical presentation is inadequate or atypical, leading to misdiagnosis or underdiagnosis of KD. In turn, misdiagnosis or underdiagnosis of KD can lead to delayed use of intravenous immunoglobulin (IVIG), increasing the risk of drug resistance and coronary artery lesions (CAL).

Objectives:

The purpose of this study was to develop a predictive model for identifying KD and infectious diseases in children in the hope of helping pediatricians develop timely and accurate treatment plans.

Methods:

The data Patients diagnosed with KD from January 2018 to July 2022 in Shenzhen Longgang District Maternity & Child Healthcare Hospital, and children diagnosed with infectious diseases in the same period will be included in this study as controls. We collected demographic information, clinical presentation, and laboratory data on KD before receiving IVIG treatment. All statistical analyses were performed using R-4.2.1 (https://www.rproject.org/). Logistic regression and Least Absolute Shrinkage with Selection Operator (LASSO) regression analyses were used to build predictive models. Calibration curves and C-index were used to validate the accuracy of the prediction models.

Results:

A total of 1,377 children were enrolled in this study, 187 patients with KD were included in the KD group and 1,190 children with infectious diseases were included in the infected group. We identified 15 variables as independent risk factors for KD by LASSO analysis. Then by logistic regression we identified 7 variables for the construction of nomogram including white blood cell (WBC), Monocyte (MO), erythrocyte sedimentation rate (ESR), alanine transaminase (ALT), albumin (ALB), C-reactive protein to procalcitonin ratio (CPR) and C-reactive protein to lymphocyte ratio (CLR). The calibration curve and C-index of 0.969 (95% confidence interval: 0.960-0.978) validated the model accuracy.

Conclusion:

Our predictive model can be used to discriminate KD from infectious diseases. Using this predictive model, it may be possible to provide an early determination of the use of IVIG and the application of antibiotics as soon as possible.

Keywords:

C-reactive protein to lymphocyte ratio C-reactive protein to procalcitonin ratio Kawasaki disease infectious diseases predictive model

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