Prediction Model for the Differential Diagnosis of Kawasaki Disease and Acute Cervical Lymphadenitis in Patients Initially Presenting with Fever and Cervical Lymphadenitis

Abstract

Objectives: To distinguish early-stage lymph node first presentation of Kawasaki disease from acute cervical lymphadenitis, we developed an algorithm using sequential laboratory marker levels and radiologic findings.

Study design: Data were obtained from pediatric inpatients initially presenting with fever and cervical lymphadenopathy. Discriminative factors for the differential diagnosis of acute cervical lymphadenitis and lymph node first presentation of Kawasaki disease were identified from intergroup comparison or univariate logistic regression analysis. A model for differentiating between lymph node first presentation of Kawasaki disease and acute cervical lymphadenitis was constructed using decision-tree analysis.

Results: Patients were divided into 2 cohorts: training (206 patients) and validation (103 patients) cohorts. A decision-tree model developed from the data of the training cohort included 3 determinants: neck computed tomography- or ultrasonography-defined abscess, percentage change in C-reactive protein level, and percentage change in neutrophil count. The prediction power of our decision-tree model for the validation cohort was superior to that of previously known laboratory markers (sensitivity of 89.5%, specificity of 88.9%, positive predictive value of 95.8%, negative predictive value of 75.0%, overall accuracy of 89.3%, and a Youden index of 0.784).

Conclusions: A decision-tree model could differentiate lymph node first presentation of Kawasaki disease from acute cervical lymphadenitis with an increased accuracy. External validation based on multicenter data is needed before clinical application.

Keywords: Kawasaki disease; acute cervical lymphadenitis; cervical lymphadenopathy; decision-tree analysis.