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

Aim: 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.
Contrast-induced nephropathy (CIN) is a significant complication that can arise from the use of contrast media during diagnostic or invasive procedures.
Inflammatory markers such as the neutrophil-to-lymphocyte ratio and the C-reactive protein-to-albumin ratio were previously identified as good predictors of CIN, leading to the idea that the combination of these markers might be more useful for predicting CIN development than each parameter individually.
A novel inflammatory marker (inflammatory prognostic index), which is the product of neutrophil/lymphocyte ratio (NLR) and C-reactive protein/albumin ratio (CAR) was an independent predictor of CIN in non-ST segment elevation myocardial infarction patients (NSTEMI).
A nomogram including inflammatory-prognostic index (IPI) had high predictive and calibration abilities for estimating the risk of CIN development.
IPI had higher discriminative ability than both NLR and CAR for discrimination of CIN (+) patients from CIN (-) ones.
Naive-Bayes and k-nearest neighbors were the best algorithms for prediction of CIN development in NSTEMI.
As a prediction tool machine learning (ML) algorithms including IPI, especially k-nearest neighbors might have net clinical benefit for detecting CIN development risk.
In recent years, ML algorithms have been shown to better predict worse cardiovascular outcomes by analyzing the available healthcare data.
The present study revealed that the ML algorithms might accurately predict the CIN developments when combined with well-known inflammatory marker in NSTEMI patients.
A nomogram including IPI might be useful as a prediction tool for CIN development after coronary interventions in NSTEMI patients.