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

Abstract

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 at a hospital. 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 succeeded in the discrimination between KD and non-KD groups. The prediction performance was not strongly correlated with the age and body temperature. Wave amplitudes and RR interval were suggested as the important features for the discrimination. These findings suggest that ECG-derived features may provide adjunctive information for distinguishing complete KD from other febrile illnesses.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study was supported by JSPS KAKENHI Grant Number JP26K00592, Suzuken Memorial Foundation, Research Foundation for the Electrotechnology of Chubu, and Aichi Health Promotion Foundation.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was approved by the institutional ethics committee of Fujita Health University (approval number: HM25-281).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Data Availability

The data supporting the findings of this study are not publicly available because they include potentially identifiable clinical and electrocardiographic data from pediatric patients. Data are available upon reasonable request and approval of institutional ethics committee. Analysis codes used in this study will be available.

https://github.com/nakano-lab

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