ArticleRenal failure2025
Machine learning prediction of contrast-induced AKI after PCI using the Systemic Immune-inflammation Index: insights from MIMIC-IV.
Article in Renal failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- From black box to glass box: explainable artificial intelligence for acute kidney injury prediction-a scoping review and the GLASS-AKI translational framework proposal.International urology and nephrology · 2026Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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,
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Registered trials
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