Scholarlink

Impact statement <p><UnorderedList Mark="Bullet"> <ItemContent> <p><b>Key message</b>: In a large multinational validation study, the Kawasaki MATCH machine-learning clinical decision support tool accurately identified patients with Kawasaki Disease (KD) using data from the REKAMLATINA network.</p> </ItemContent> <ItemContent> <p><b>What this study adds</b>: This is the first international validation of Kawasaki MATCH. Prospective and retrospective validation of the model across numerous diverse Latin American clinical settings demonstrates consistent performance despite differences in laboratory availability, data completeness, and practice patterns.</p> </ItemContent> <ItemContent> <p><b>Impact</b>: These findings support the use of AI-assisted decision support to improve recognition of KD, reduce diagnostic delay, and potentially prevent coronary artery complications in children across varied health systems.</p> </ItemContent> </UnorderedList></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Validation of Kawasaki MATCH on a Latin American cohort: application to the REKAMLATINA network

  • 期刊论文

  • 发表日期：

    2026年7月20日

查看全文
  • Adriana H. Tremoulet,
  • Jonathan Y. Lam,
  • Rolando Ulloa-Gutierrez,
  • Jane C. Burns,
  • Shamim Nemati,
  • Michael A. Gardiner,
  • Luis M. Garrido-García,
  • Dora Estripeaut,
  • Olguita del Aguila,
  • Virgen Gómez,
  • Enrique Faugier-Fuentes,
  • Greta Miño-León,
  • Sandra Beltrán,
  • Fernanda Cofré,
  • Enrique Chacon-Cruz,
  • Patricia Saltigeral-Simental,
  • Lucila Martínez-Medina,
  • Lourdes Dueñas,
  • Kathia Luciani,
  • Francisco J. Rodríguez-Quiroz,
  • German Camacho-Moreno,
  • Tamara Viviani,
  • Martha I. Alvarez-Olmos,
  • Heloisa Helena de Sousa Marques,
  • Eduardo López-Medina,
  • María C. Pirez,
  • Luisa B. Gámez-González,
  • Paola Pérez-Camacho,
  • Lorena Franco,
  • Adrián Collia,
  • Carlos F. Grazioso,
  • Giannina Izquierdo,
  • Mario Melgar,
  • Arturo Borzutzky,
  • Dolores Lovera,
  • Elizabeth Assandri,
  • Carlos Daza,
  • Marco A. Yamazaki-Nakashimada,
  • Diana López-Gallegos,
  • Adriana Díaz-Maldonado,
  • Pio López,
  • Mónica Pujadas,
  • Guillermo Soza,
  • Rafael Hernández-Magaña,
  • Saulo Duarte Passos,
  • Nadina Rubio-Pérez,
  • Rogelio Martínez-Ramírez,
  • Alejandro Díaz-Díaz,
  • Juan G. Mesa-Monsalve

摘要

Impact statement

Key message: In a large multinational validation study, the Kawasaki MATCH machine-learning clinical decision support tool accurately identified patients with Kawasaki Disease (KD) using data from the REKAMLATINA network.

What this study adds: This is the first international validation of Kawasaki MATCH. Prospective and retrospective validation of the model across numerous diverse Latin American clinical settings demonstrates consistent performance despite differences in laboratory availability, data completeness, and practice patterns.

Impact: These findings support the use of AI-assisted decision support to improve recognition of KD, reduce diagnostic delay, and potentially prevent coronary artery complications in children across varied health systems.