Subgroups of children with Kawasaki disease: a data-driven cluster analysis

Area of Science:

• Pediatric Rheumatology
• Immunology
• Data Science in Medicine

Background:

• Kawasaki disease (KD) is typically viewed as a singular condition.
• However, significant variability exists in clinical presentations and patient outcomes.
• Identifying distinct KD subgroups is crucial for tailored management.

Purpose of the Study:

• To employ a data-driven methodology for identifying distinct clinical subgroups within Kawasaki disease.
• To analyze the clinical, seasonal, and proteomic characteristics of these identified subgroups.

Main Methods:

• Hierarchical clustering and k-means parcellation were used on 14 clinical variables from 1016 KD patients.
• Proteomic data from 32 KD patients and 24 healthy children were analyzed for differential protein abundance.
• Seasonal and incidence trends were examined for each subgroup.

Main Results:

• Four distinct Kawasaki disease subgroups were identified based on clinical features, laboratory results, and treatment responses.
• Subgroup 1: Hepatobiliary involvement, low coronary artery aneurysm, high IVIG resistance.
• Subgroup 2: High neutrophil count, high KD shock rate.
• Subgroup 3: Cervical lymphadenopathy, high inflammation markers, low hemoglobin.
• Subgroup 4: Young onset, high coronary artery aneurysm, low IVIG resistance.
• Distinct seasonal patterns and proteomic profiles were observed across subgroups.

Conclusions:

• Kawasaki disease exhibits significant heterogeneity, supporting the existence of distinct clinical subgroups.
• These findings have critical implications for refining clinical management strategies for KD.
• The identified subgroups necessitate tailored research designs and interpretation of results.