Kawasaki MATCH Trial

Following laboratory evaluation, providers will be randomized to treat patients according to usual practice/standard of care vs. receiving clinical decision support from the Kawasaki MATCH tool - a previously validated machine-learning clinical decision support tool to identify Kawasaki Disease. The study aim is to evaluate the accuracy of Kawasaki MATCH prospectively when used at the point of care, as well as how this tool impacts clinical decisions including additional evaluation and hospital admission.

• Kawasaki MATCH (Other): Providers access the Kawasaki MATCH decision support tool. Patient information is entered into the tool and a risk score is indicated to the provider. Kawasaki MATCH is a previously validated machine-learning decision support tool for the diagnosis of Kawasaki Disease. This tool utilizes patient age, 18 laboratory features, and 5 clinical features to formulate a risk score.

Primary outcomes

• Time to Kawasaki Disease treatment (KD patients only) [90 days]: Time in days from initial ED evaluation to initial IVIG treatment in patients ultimately diagnosed with Kawasaki Disease

Secondary outcomes

• Kawasaki MATCH score [Day 1 (day of enrollment)]: MATCH score determined by entering patient clinical and laboratory information into the Kawasaki MATCH algorithm. Algorithm output is a decimal between 0 and 1. Scores less than 0.4 will be deemed a prediction of "not Kawasaki Disease" while scores of 0.4 or higher will be deemed "Kawasaki Disease"
• Hospital Admission Rate [Day 1 (day of enrollment)]: Proportion of patients admitted to the hospital from the Emergency Department (ED). Admitted patients = 1 Discharged patients = 0
• Kawasaki Disease consultation rate [Day 1 (day of enrollment)]: Proportion of patients receiving Kawasaki service consultation
• ED return visit [7 days]: Return to Emergency Department
• Additional interventions [Day 1 (day of enrollment)]: Additional ED orders for laboratory/imaging studies or additional procedures after the time of randomization
• Provider assessment of KD likelihood - baseline [Day 1 (day of enrollment)]: Visual analog scale of suspected likelihood of Kawasaki Disease. Scale between 0 (Definitely not Kawasaki Disease) and 100 (Definitely Kawasaki Disease) with higher values indicating higher provider suspicion for Kawasaki Disease. This outcome will be assessed on providers randomized to both study arms
• Provider assessment of KD likelihood - after algorithm [Day 1 (day of enrollment)]: Visual analog scale of suspected likelihood of Kawasaki Disease. Scale between 0 (Definitely not Kawasaki Disease) and 100 (Definitely Kawasaki Disease) with higher values indicating higher provider suspicion for Kawasaki Disease. This outcome only assessed for providers in the experimental arm.
• Algorithm helpfulness [Day 1 (day of enrollment)]: Visual analog scale of the perceived helpfulness of the Kawasaki MATCH algorithm. Scale between 0 (Not helpful) and 100 (Extremely helpful) with higher values indicating increased helpfulness. This outcome only assessed for providers in the experimental arm.

Sex: All, ages: 30 Days to 17 Years

Inclusion Criteria:

* Measured or subjective fever for >= 3 calendar days
* One measured fever >= 38.0 C (home or in ED)
* One or more clinical feature of Kawasaki Disease including:
* Rash
* Conjunctival injection
* Oropharyngeal changes
* Extremity changes (erythema, edema, desquamation)
* Cervical adenopathy (>=1.5cm)
* Infants < 6 months of age with >= 7 days of fever eligible even if none of the above clinical features
* Requires IV/phlebotomy for clinical evaluation

Exclusion Criteria:

* Congenital or Acquired Immune function
* Genetic disorders
* Current systemic steroid, immunosuppression, or chemotherapy treatment (not including inhaled steroids)

• Lam JY, Shimizu C, Gardiner MA, Giorgio T, Wright V, Baker A, Anderson MS, Heizer H, Mohandas S, Kazarians A, Kaneta K, Jone PN, Dominguez SR, Szmuszkovicz JR, Newburger JW, Tremoulet AH, Burns JC. External Validation of a Machine Learning Model to Diagnose Kawasaki Disease. J Pediatr. 2025 Jul;282:114543. doi: 10.1016/j.jpeds.2025.114543. Epub 2025 Mar 21. PMID 40122277
• Lam JY, Shimizu C, Tremoulet AH, Bainto E, Roberts SC, Sivilay N, Gardiner MA, Kanegaye JT, Hogan AH, Salazar JC, Mohandas S, Szmuszkovicz JR, Mahanta S, Dionne A, Newburger JW, Ansusinha E, DeBiasi RL, Hao S, Ling XB, Cohen HJ, Nemati S, Burns JC; Pediatric Emergency Medicine Kawasaki Disease Research Group; CHARMS Study Group. A machine-learning algorithm for diagnosis of multisystem inflammatory syndrome in children and Kawasaki disease in the USA: a retrospective model development and validation study. Lancet Digit Health. 2022 Oct;4(10):e717-e726. doi: 10.1016/S2589-7500(22)00149-2. PMID 36150781

• Study Protocol (PDF, 2025-08-19)
• Statistical Analysis Plan (PDF, 2025-12-04)