Saylik, FaysalCinar, TufanSelcuk, MuratTanboga, Ibrahim Halil2025-05-102025-05-1020241752-03631752-037110.1080/17520363.2024.2422810https://doi.org/10.1080/17520363.2024.2422810https://hdl.handle.net/20.500.14720/11117Tanboga, Ibrahim Halil/0000-0003-4546-9227; Selcuk, Murat/0000-0003-2873-5159; Cinar, Tufan/0000-0001-8188-5020Aim: Inflammatory prognostic index (IPI), has been shown to be related with poor outcomes in cancer patients. We aimed to investigate the predictive role of IPI for contrast-induced nephropathy (CIN) development in non-ST segment elevation myocardial infarction patients using a nomogram and performing machine learning (ML) algorithms.Materials & methods: A total of 178 patients with CIN (+) and 1511 with CIN (-) were included.Results: CIN (+) patients had higher IPI levels, and IPI was independently associated with CIN. A risk prediction nomogram including IPI had a higher predictive ability and good calibration. Naive Bayes and k-nearest neighbors were the best ML algorithms for the prediction of CIN patients.Conclusion: IPI might be used as an easily obtainable marker for CIN prediction using ML algorithms.eninfo:eu-repo/semantics/closedAccessContrast-Induced NephropathyInflammatory Prognostic IndexMachine LearningNomogramNon-St Segment Elevation Myocardial InfarctionArticle1823Q4Q31007101539535134WOS:001355043100001