Complement 3 and the Prognostic Nutritional Index Distinguish Kawasaki Disease from Other Fever Illness with a Nomogram.

OBJECTIVE: This study aimed to establish a model to distinguish Kawasaki disease (KD) from other fever illness using the prognostic nutritional index (PNI) and immunological factors. METHOD: We enrolled a total of 692 patients (including 198 with KD and 494 children with febrile diseases). Of those, 415 patients were selected to be the training group and 277 patients to be the validation group. Laboratory data, including the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR), the prognostic nutritional index (PNI), and immunological factors, were retrospectively collected for an analysis after admission. We used univariate and multivariate logistic regressions and nomograms for the analysis. RESULT: Patients with KD showed significantly higher C3 and a lower PNI. After a multivariate logistic regression, the total leukocyte count, PNI, C3, and NLR showed a significance (p < 0.05) and then performed well with the nomogram model. The areas under the ROC in the training group and the validation group were 0.858 and 0.825, respectively. The calibration curves of the two groups for the probability of KD showed a near agreement to the actual probability. CONCLUSIONS:  Compared with children with febrile diseases, patients with KD showed increased C3 and a decreased nutritional index of the PNI. The nomogram established with these factors could effectively identify KD from febrile illness in children.

A Nomogram Model Identifies Eosinophilic Frequencies to Powerfully Discriminate Kawasaki Disease From Febrile Infections.

Background: Kawasaki disease (KD) is a form of systemic vasculitis that occurs primarily in children under the age of 5 years old. No single laboratory data can currently distinguish KD from other febrile infection diseases. The purpose of this study was to establish a laboratory data model that can differentiate between KD and other febrile diseases caused by an infection in order to prevent coronary artery complications in KD. Methods: This study consisted of a total of 800 children (249 KD and 551 age- and gender-matched non-KD febrile infection illness) as a case-control study. Laboratory findings were analyzed using univariable, multivariable logistic regression, and nomogram models. Results: We selected 562 children at random as the model group and 238 as the validation group. The predictive nomogram included high eosinophil percentage (100 points), high C-reactive protein (93 points), high alanine transaminase (84 points), low albumin (79 points), and high white blood cell (64 points), which generated an area under the curve of 0.873 for the model group and 0.905 for the validation group. Eosinophilia showed the highest OR: 5.015 (95% CI:-3.068-8.197) during multiple logistic regression. The sensitivity and specificity in the validation group were 84.1 and 86%, respectively. The calibration curves of the validation group for the probability of KD showed near an agreement to the actual probability. Conclusion: Eosinophilia is a major factor in this nomogram model and had high precision for predicting KD. This report is the first among the existing literature to demonstrate the important role of eosinophil in KD by nomogram.

A novel nomogram model for differentiating Kawasaki disease from sepsis.

Kawasaki disease (KD) is a form of systemic vasculitis that occurs in children under the age of 5 years old. Due to prolonged fever and elevated inflammatory markers that are found in both KD and sepsis, the treatment approach differs for each. We enrolled a total of 420 children (227 KD and 193 sepsis) in this study. Logistic regression and a nomogram model were used to analyze the laboratory markers. We randomly selected 247 children as the training modeling group and 173 as the validation group. After completing a logistic regression analysis, white blood cell (WBC), anemia, procalcitonin (PCT), C-reactive protein (CRP), albumin, and alanine transaminase (ALT) demonstrated a significant difference in differentiating KD from sepsis. The patients were scored according to the nomogram, and patients with scores greater than 175 were placed in the high-risk KD group. The area under the curve of the receiver operating characteristic curve (ROC curve) of the modeling group was 0.873, sensitivity was 0.893, and specificity was 0.746, and the ROC curve in the validation group was 0.831, sensitivity was 0.709, and specificity was 0.795. A novel nomogram prediction model may help clinicians differentiate KD from sepsis with high accuracy.