Clinical implications of procalcitonin in Kawasaki disease: a useful candidate for differentiating from sepsis and evaluating IVIG responsiveness

A machine learning model for distinguishing Kawasaki disease from sepsis

KD is an acute systemic vasculitis that most commonly affects children under 5 years old. Sepsis is a systemic inflammatory response syndrome caused by infection. The main clinical manifestations of both are fever, and laboratory tests include elevated WBC count, C-reactive protein, and procalcitonin. However, the two treatments are very different. Therefore, it is necessary to establish a dynamic nomogram based on clinical data to help clinicians make timely diagnoses and decision-making. In this study, we analyzed 299 KD patients and 309 sepsis patients. We collected patients' age, sex, height, weight, BMI, and 33 biological parameters of a routine blood test. After dividing the patients into a training set and validation set, the least absolute shrinkage and selection operator method, support vector machine and receiver operating characteristic curve were used to select significant factors and construct the nomogram. The performance of the nomogram was evaluated by discrimination and calibration. The decision curve analysis was used to assess the clinical usefulness of the nomogram. This nomogram shows that height, WBC, monocyte, eosinophil, lymphocyte to monocyte count ratio (LMR), PA, GGT and platelet are independent predictors of the KD diagnostic model. The c-index of the nomogram in the training set and validation is 0.926 and 0.878, which describes good discrimination. The nomogram is well calibrated. The decision curve analysis showed that the nomogram has better clinical application value and decision-making assistance ability. The nomogram has good performance of distinguishing KD from sepsis and is helpful for clinical pediatricians to make early clinical decisions.

The Future of Vasculitis: A Manifesto

Predictions for a general path forward in vasculitis care and research are provided based on advances made in the past 20 years. Prospects for advances in translational research with potential to improve care are highlighted, including identification of hemato-inflammatory diseases, autoantigens, disease mechanisms in animal models, and biomarkers. A list of active randomized trials is provided, and areas of potential paradigm shifts in care are highlighted. The importance of patient involvement and international collaboration is noted, and a plea is made for innovative trial designs that would improve access of patients to trials and to clinical experts at referral centers.

Evaluation of subclinical left ventricular systolic dysfunction in patients with acute-phase Kawasaki disease by hematological indices, layer-specific left ventricular longitudinal strain and global myocardial work

PURPOSE

To evaluate subclinical LV systolic dysfunction in aKD patients by hematological indices, global layer-specific LV longitudinal strain and myocardial work (MW).

METHODS

Forty-three normal controls and 42 aKD patients were enrolled in the present study. The peak systolic epimyocardial (GLSEpi), middle layer (GLSMid) and endomyocardial (GLSEndo) longitudinal strain, global myocardial work index (GWI), global constructive work (GCW), global wasted work (GWW) and myocardial work efficiency (GWE) were measured by two-dimensional speckle-tracking echocardiography in apical three-chamber, four-chamber, and two-chamber views.

RESULTS

The absolute values of GLSEpi, GLSMid, and GLSEndo in aKD patients were significantly lower than those in normal controls (p < .01). The values of GCW and GWE were significantly lower than those of normal controls (p < .05). There were no significant differences among the AUCs of layer-specific LV GLS and global MW (p > .05). The correlation test showed that layer-specific LV GLS showed a good correlation with GCW. Multivariable analysis showed that Hb and LVEF were independent factors for GCW.

CONCLUSION

In this research, we found that subclinical LV systolic dysfunction was detected by layer-specific GLS and MW in aKD patients. GCW has the same diagnostic value as layer-specific LV GLS. Hb and LVEF are independent factors of LV myocardial function.

Clinical characteristics and outcomes of children with Kawasaki disease combined with sepsis in the pediatric intensive care unit

Background

Kawasaki disease (KD) is a vascular inflammatory disease with unknown pathogenesis. There are few studies on KD combined with sepsis worldwide.

Purpose

To provide valuable data regarding clinical characteristics and outcomes related to pediatric patients with KD combined with sepsis in pediatric intensive care unit (PICU).

Methods

We retrospectively analyzed the clinical data of 44 pediatric patients admitted in PICU at Hunan Children's Hospital with KD combined with sepsis between January 2018 and July 2021.

Results

Of the 44 pediatric patients (mean age, 28.18 ± 24.28 months), 29 were males and 15 were female. We further divided the 44 patients into two groups: KD combined with severe sepsis (n=19) and KD combined with non-severe sepsis (n=25). There were no significant between-group differences in leukocyte, C-reactive protein, and erythrocyte sedimentation rate. Interleukin-6, interleukin-2, interleukin-4 and procalcitonin in KD with severe sepsis group were significantly higher than those in KD with non-severe sepsis group. And the percentage of suppressor T lymphocyte and natural killer cell in severe sepsis group were significantly higher than those in non-severe group, while the CD4⁺/CD8⁺ T lymphocyte ratio was significantly lower in KD with severe sepsis group than in KD with non-severe sepsis group. All 44 children survived and were successfully treated after intravenous immune globulin (IVIG) combined with antibiotics.

Conclusion

Children who develop with KD combined with sepsis have different degrees of inflammatory response and cellular immunosuppression, and the degree of inflammatory response and cellular immunosuppression is significantly correlated with the severity of the disease.

Plasma interleukin-41 serves as a potential diagnostic biomarker for Kawasaki disease

INTRODUCTION

Kawasaki disease (KD) is a systemic vasculitis that causes abnormalities in the coronary arteries. Interleukin (IL)-41 is a novel immunoregulatory cytokine involved in the pathogenesis of some inflammatory and immune-related diseases. However, the role of IL-41 in KD is unclear. The purpose of this study was to detect the expression of IL-41 in the plasma of children with KD and its relationship with the disease.

METHODS

A total of 44 children with KD and 37 healthy controls (HC) were recruited for this study. Plasma concentrations of IL-41 were determined by ELISA. Correlations between plasma IL-41 levels and KD-related clinical parameters were analyzed by Pearson correlation and multivariate linear regression analysis. Receiver operating characteristic curve analysis was used to assess the clinical value of IL-41 in the diagnosis of KD.

RESULTS

Our results showed that plasma IL-41 levels were significantly elevated in children with KD compared with HC. Correlation analysis demonstrated that IL-41 levels were positively correlated with D-dimer and N-terminal pro-B-type natriuretic peptide, and negatively correlated with IgM, mean corpuscular hemoglobin concentration, total protein, albumin and pre-albumin. Multivariable linear regression analysis revealed that IgM and mean corpuscular hemoglobin concentrations were associated with IL-41. Receiver operating characteristic curve analysis showed that the area under the curve of IL-41 was 0.7101, with IL-41 providing 88.64 % sensitivity and 54.05 % specificity.

CONCLUSION

Our study indicated that plasma IL-41 levels in children with KD were significantly higher than those in HC, and may provide a potential diagnostic biomarker for KD.

A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population

Objective: We explored the risk factors for intravenous immunoglobulin (IVIG) resistance in children with Kawasaki disease (KD) and constructed a prediction model based on machine learning algorithms. Methods: A retrospective study including 1,398 KD patients hospitalized in 7 affiliated hospitals of Chongqing Medical University from January 2015 to August 2020 was conducted. All patients were divided into IVIG-responsive and IVIG-resistant groups, which were randomly divided into training and validation sets. The independent risk factors were determined using logistic regression analysis. Logistic regression nomograms, support vector machine (SVM), XGBoost and LightGBM prediction models were constructed and compared with the previous models. Results: In total, 1,240 out of 1,398 patients were IVIG responders, while 158 were resistant to IVIG. According to the results of logistic regression analysis of the training set, four independent risk factors were identified, including total bilirubin (TBIL) (OR = 1.115, 95% CI 1.067-1.165), procalcitonin (PCT) (OR = 1.511, 95% CI 1.270-1.798), alanine aminotransferase (ALT) (OR = 1.013, 95% CI 1.008-1.018) and platelet count (PLT) (OR = 0.998, 95% CI 0.996-1). Logistic regression nomogram, SVM, XGBoost, and LightGBM prediction models were constructed based on the above independent risk factors. The sensitivity was 0.617, 0.681, 0.638, and 0.702, the specificity was 0.712, 0.841, 0.967, and 0.903, and the area under curve (AUC) was 0.731, 0.814, 0.804, and 0.874, respectively. Among the prediction models, the LightGBM model displayed the best ability for comprehensive prediction, with an AUC of 0.874, which surpassed the previous classic models of Egami (AUC = 0.581), Kobayashi (AUC = 0.524), Sano (AUC = 0.519), Fu (AUC = 0.578), and Formosa (AUC = 0.575). Conclusion: The machine learning LightGBM prediction model for IVIG-resistant KD patients was superior to previous models. Our findings may help to accomplish early identification of the risk of IVIG resistance and improve their outcomes.