Machine learning prediction of contrast-induced AKI after PCI using the Systemic Immune-inflammation Index: insights from MIMIC-IV
Year: 2025
Type: article
Abstract: This study aimed to evaluate the systemic immune-inflammation index (SII) for predicting contrast-induced acute kidney injury (CI-AKI) and to develop a machine learning model integrating SII with key risk factors. Data were derived from the MIMIC-IV database (2008–2019) for acute myocardial infarction patients undergoing percutaneous coronary intervention in the intensive care unit. Logistic regression and restricted cubic splines were used to assess the association between SII and CI-AKI. Six machine learning models were developed on 70% of the training set and validated on the remaining 30%. Among 1,334 included patients, multivariable logistic regression identified a higher SII as a significant independent predictor for CI-AKI (Q4 vs. Q1: OR = 2.90, 95% CI: 2.01–4.19, p < 0.001). The Random Forest model demonstrated the best performance, achieving an area under the curve (AUC) of 0.84 (95% CI: 0.78–0.91) in the validation set, significantly outperforming the traditional modified Mehran score (AUC = 0.84 vs. 0.72). Calibration and decision curve analyses confirmed the model’s robustness and clinical utility. In conclusion, SII is strongly associated with CI-AKI risk, and the developed Random Forest model integrating SII offers a superior and interpretable tool for pre-procedural risk stratification, potentially guiding targeted prevention strategies.
Source: Renal Failure
Authors: Jiwen Zhang, Haizhu Chen, Lin Yu, Wei Han
Institution: Guangzhou University of Chinese Medicine
Language: English
Topic: Acute Kidney Injury Research
Subfield: Nephrology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,306
OpenAlex ID: W7124884220