Exploring the diagnostic value of CLR and CPR in differentiating Kawasaki disease from other infectious diseases based on clinical predictive modeling

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.

Pilot study of DNA methylation in the pathogenesis of chronic rhinosinusitis with nasal polyps

BACKGROUND

DNA methylation has been implicated in the pathogenesis of allergy and atopy. This study aimed to identify whether DNA methylation also plays an important role in the pathogenesis of nasal polyps (NP).

METHODOLOGY

NP tissues were obtained from 32 patients with chronic rhinosinusitis with bilateral NP. Biopsies of inferior turbinate mucosa (ITM) were taken from 18 patients who underwent rhinoseptoplasty (control group). The methylated genes, which were detected by DNA methylation microarray, were validated by methylation-specific polymerase chain reaction, bisulphite sequencing, real-time polymerase chain reaction and immunohistochemistry.

RESULTS

DNA methylation microarray identified 8,008 CpG islands in 2,848 genes. One hundred and ninety-eight genes were found to have a methylated signal in the promoter region in NP samples compared with ITM samples. The four top genes that changed, COL18A1, EP300, GNAS and SMURF1, were selected for further study. The methylation frequency of COL18A1 was significantly higher in NP samples than in ITM samples.

CONCLUSIONS

DNA methylation might play an important role in the pathogenesis of NP. Promoter methylation of COL18A1 was found to be significantly increased in NP tissues, further studies are necessary to confirm the significance of these epigenetic factors in the mechanisms underlying the development or persistence of NP.