Machine learning algorithms using the inflammatory prognostic index for contrast-induced nephropathy in NSTEMI patients

<b>Aim:</b> 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.<b>Materials & methods:</b> A total of 178 patients with CIN (+) and 1511 with CIN (-) were included.<b>Results:</b> 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.<b>Conclusion:</b> IPI might be used as an easily obtainable marker for CIN prediction using ML algorithms.