An electrocardiogram-based machine learning model for distinguishing complete Kawasaki disease.

Kawasaki disease (KD) is a systemic vasculitis in young children, and early diagnosis remains challenging when clinical features are incomplete or overlap with those of other febrile illnesses. Because electrocardiography (ECG) is noninvasive and widely available, we investigated whether ECG-derived features could help distinguish complete KD from pediatric patients with fevers. We conducted a single-center retrospective study of hospitalized febrile children aged 1-8 years who underwent digital 12-lead ECG recording during the initial evaluation. Five amplitude features and six timing features extracted from the ECG were used to develop a logistic regression model to distinguish between complete KD and other febrile illnesses. The model discriminated between the KD and non-KD groups in the validation dataset. The prediction score was not significantly correlated with the age and body temperature. S-wave amplitude, the RR interval, and P-and Q-wave amplitudes were suggested to contribute to discrimination. These findings suggest that ECG-derived features may provide adjunctive information for distinguishing complete KD from other febrile illnesses. Author SummaryKawasaki disease is an inflammatory illness in young children that can lead to coronary artery complications if treatment is delayed. Early diagnosis is often difficult because its initial symptoms overlap with those of many common febrile illnesses. We investigated whether a routine 12-lead electrocardiogram (ECG), which is noninvasive, rapid, and widely available, contains information that can help distinguish complete Kawasaki disease from other febrile conditions. We retrospectively analyzed digital ECGs from hospitalized febrile children and extracted waveform amplitude and timing features. Using these features, we built a logistic regression model and evaluated it in a temporally separate validation cohort. The model distinguished patients with Kawasaki disease from patients with fever. P-, Q-, and S-wave amplitudes and the RR interval were repeatedly selected as important contributors, suggesting that both waveform morphology and heart-rate-related information may be relevant. These findings indicate that ECG-derived features may provide useful adjunctive information during the clinical assessment of complete Kawasaki disease.