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The Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score was originally designed to predict mortality in heart failure patients, but its role in predicting CIN has not been fully explored. In the present retrospective study, 1403 STEMI patients treated with pPCI were analyzed. Those who developed CIN had higher mortality, longer hospital stays, and more comorbidities. The MAGGIC score and 21 clinical parameters were incorporated into deep learning (DL) models, including multilayer perceptrons, TabNet, TabTransformer, and Kolmogorov-Arnold Networks (KAN) and one machine learning algorithm such as logistic regression. The best-performing model, KAN, significantly improved CIN prediction with an area under curve (AUC) of 0.92. SHapley Additive exPlanations (SHAP) analysis revealed key predictors such as pain-to-balloon time, contrast volume, baseline creatinine, and MAGGIC score. Our findings demonstrate that combining MAGGIC risk scoring with DL substantially enhances CIN prediction in STEMI patients. This approach enables identification of at-risk individuals and supports implementation of nephroprotective strategies at an early stage. The web-based calculator may assist clinical decision making.<\/jats:p>","DOI":"10.1177\/00033197251399866","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T07:29:29Z","timestamp":1766561369000},"page":"1285-1294","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep Learning Improves the MAGGIC Risk Score in Predicting Contrast-Induced Nephropathy in ST Elevation Myocardial Infraction Patients"],"prefix":"10.1177","volume":"77","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4774-3103","authenticated-orcid":false,"given":"Remzi","family":"Sar\u0131kaya","sequence":"first","affiliation":[{"name":"Department of Cardiology, Van Education and Research Hospital, Van, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3165-6769","authenticated-orcid":false,"given":"Faysal","family":"\u015eayl\u0131k","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Van Education and Research Hospital, Van, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5369-5414","authenticated-orcid":false,"given":"\u00d6mer","family":"K\u00fcmet","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Van Education and Research Hospital, Van, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6682-5414","authenticated-orcid":false,"given":"G\u00f6rkem","family":"Ayhan","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Van Education and Research Hospital, Van, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0544-0657","authenticated-orcid":false,"given":"Ahmet Ferhat","family":"Kaya","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Van Education and Research Hospital, Van, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4731-4061","authenticated-orcid":false,"given":"Veysi","family":"Can","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Van Education and Research Hospital, Van, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8188-5020","authenticated-orcid":false,"given":"Tufan","family":"\u00c7\u0131nar","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Maryland Midtown Campus, Baltimore, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koray","family":"Kalendero\u011flu","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Health Sciences University, Dr Siyami Ersek Cardiovascular and Thoracic Surgery Center, Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,12,23]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/2750173"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.3892\/etm.2020.9572"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2018.06.196"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehs337"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.21470\/1678-9741-2022-0355"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjms.2024.07.029"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1177\/1179546820927404"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1080\/17520363.2024.2422810"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehy462"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcin.2015.09.026"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinbiochem.2016.06.001"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-150-9-200905050-00006"},{"key":"e_1_3_2_14_2","article-title":"TabTransformer: Tabular data modeling using contextual embeddings","author":"Huang X","unstructured":"Huang X, Khetan A, Cvitkovic M, Karnin Z. 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