{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T06:52:49Z","timestamp":1791355969016,"version":"4.3.1"},"reference-count":39,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"19","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2026,10,6]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Early identification of intravenous immunoglobulin\u2010resistant Kawasaki disease is important for reducing the risk of coronary artery lesions. We aimed to develop, externally validate, and deploy an interpretable machine\u2010learning model for early prediction of intravenous immunoglobulin\u2010resistant Kawasaki disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">The derivation cohort included 3023 patients with Kawasaki disease admitted to Children's Hospital of Soochow University from January 2020 to December 2024 and was used for model training and internal validation. External validation included 1632 patients from 3 independent hospitals during the same period. Thirty\u2010three clinical variables available within 24 hours of admission were used to develop 12 machine\u2010learning models. Model discrimination was assessed using the area under the curve, and Shapley Additive Explanations were used for model interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">Among the 12 algorithms, the Extra Trees model showed the best discriminative performance. After feature reduction, an interpretable Extra Trees model incorporating 8 variables was selected. The model achieved area under the curve of 0.865 in internal validation, 0.890 in the Anhui cohort (n=654), 0.805 in the Xuzhou cohort (n=574), and 0.853 in the Suqian cohort (n=404). The final model was implemented as a web\u2010based application for individualized risk estimation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">We developed and externally validated an interpretable machine\u2010learning model for early prediction of intravenous immunoglobulin\u2010resistant Kawasaki disease. Shapley Additive Explanations\u2010based interpretation and web deployment may facilitate individualized risk assessment and clinical decision\u2010making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.126.049660","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T13:32:56Z","timestamp":1789738376000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Development and Validation of an Interpretable Prediction Model for IVIG\u2010Resistant Kawasaki Disease: A\u00a0Multicenter Prospective Cohort Study"],"prefix":"10.1161","volume":"15","author":[{"given":"Shuhui","family":"Wang","sequence":"first","affiliation":[{"name":"Children's Hospital of Soochow University","place":["Suzhou, China"]},{"name":"Institute of Pediatric Research, Children's Hospital of Soochow University","place":["Suzhou, 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