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| ID | Type | Description | Link |
|---|---|---|---|
| 68060 | Other Grant/Funding Number | Gordon and Marilyn Macklin Foundation |
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| Name | Class |
|---|---|
| Gordon and Marilyn Macklin Foundation | UNKNOWN |
| Rady Children's Hospital, San Diego | OTHER |
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Evaluating the impact of a machine-learning clinical decision support tool on provider practice when evaluating febrile patients with Kawasaki Disease (KD) and non-KD illnesses.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Kawasaki MATCH | Experimental | Providers encouraged to access and utilize the Kawasaki MATCH decision support tool when evaluating and managing patients in the Emergency Department |
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| Routine Care | No Intervention | Providers prompted to manage patients as per usual/routine care without additional decision support. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Time to Kawasaki Disease treatment (KD patients only) | Time in days from initial ED evaluation to initial IVIG treatment in patients ultimately diagnosed with Kawasaki Disease | 90 days |
| Measure | Description | Time Frame |
|---|---|---|
| Kawasaki MATCH score | 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" | Day 1 (day of enrollment) |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Michael A Gardiner, MD | Contact | 949-310-4808 | magardiner@health.ucsd.edu |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Rady Children's Hospital, San Diego | Recruiting | San Diego | California | 92071 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40122277 | Background | 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. | |
| 36150781 |
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Not included in consent documents
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot | Yes | No | No | Study Protocol | Aug 19, 2025 | Dec 4, 2025 | Prot_000.pdf |
| SAP | No | Yes | No | Statistical Analysis Plan | Dec 4, 2025 | Dec 4, 2025 | SAP_001.pdf |
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| ID | Term |
|---|---|
| D009080 | Mucocutaneous Lymph Node Syndrome |
| ID | Term |
|---|---|
| D014657 | Vasculitis |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
| D008206 | Lymphatic Diseases |
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| Hospital Admission Rate | Proportion of patients admitted to the hospital from the Emergency Department (ED). Admitted patients = 1 Discharged patients = 0 | Day 1 (day of enrollment) |
| Kawasaki Disease consultation rate | Proportion of patients receiving Kawasaki service consultation | Day 1 (day of enrollment) |
| ED return visit | Return to Emergency Department | 7 days |
| Additional interventions | Additional ED orders for laboratory/imaging studies or additional procedures after the time of randomization | Day 1 (day of enrollment) |
| Provider assessment of KD likelihood - baseline | 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 | Day 1 (day of enrollment) |
| Provider assessment of KD likelihood - after algorithm | 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. | Day 1 (day of enrollment) |
| Algorithm helpfulness | 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. | Day 1 (day of enrollment) |
| 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. |
| D006425 |
| Hemic and Lymphatic Diseases |
| D017445 | Skin Diseases, Vascular |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |