ArticleEuropean journal of medical research2024
Predictive modeling for acute kidney injury after percutaneous coronary intervention in patients with acute coronary syndrome: a machine learning approach.
Article in European journal of medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in contrast-induced nephropathy after coronary interventions: A meta-analysis.Medicine · 2026Pooled it
- Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis.International urology and nephrology · 2025Pooled it
- Risk prediction models for contrast-induced acute kidney injury in patients with acute coronary syndromes: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- A fine-grained transformer combined with multimodal data for predicting hospital length of stay in acute coronary syndrome.Scientific reports · 2026Article
- Article
- An interpretable XGBoost model for contrast-associated acute kidney injury in patients with acute coronary syndrome: development and geographical external validation.Frontiers in digital health · 2026Article
- Applied machine learning to predict 1-year major adverse cardiovascular events in elderly patients after percutaneous coronary intervention.BMC medical informatics and decision making · 2025Article
- Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Predictors for Irreversibility of Contrast-Induced Acute Kidney Injury in Patients with Obesity After Contrast-Enhanced Computed Tomography Coronary Angiography.Advances in therapy · 2025Article
- Interpretable AI-driven multi-objective risk prediction in heart failure patients with thyroid dysfunction.Frontiers in digital health · 2025Article
- A Review of Electronic Early Warning Systems for Acute Kidney Injury.Advances in urology · 2024Review
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute kidney injury (AKI) is one of the preventable complications of percutaneous coronary intervention (PCI). This study aimed to develop machine learning (ML) models to predict AKI after PCI in patients with acute coronary syndrome (ACS).
methodsThis study was conducted at Tehran Heart Center from 2015 to 2020. Several variables were used to design five ML models: Naïve Bayes (NB), Logistic Regression (LR), CatBoost (CB), Multi-layer Perception (MLP), and Random Forest (RF). Feature importance was evaluated with the RF model, CB model, and LR coefficients while SHAP beeswarm plots based on the CB model were also used for deriving the importance of variables in the population using pre-procedural variables and all variables. Sensitivity, specificity, and the area under the receiver operating characteristics curve (ROC-AUC) were used as the evaluation measures.
resultsA total of 4592 patients were included, and 646 (14.1%) experienced AKI. The train data consisted of 3672 and the test data included 920 cases. The patient population had a mean age of 65.6 ± 11.2 years and 73.1% male predominance. Notably, left ventricular ejection fraction (LVEF) and fasting plasma glucose (FPG) had the highest feature importance when training the RF model on only pre-procedural features. SHAP plots for all features demonstrated LVEF and age as the top features. With pre-procedural variables only, CB had the highest AUC for the prediction of AKI (AUC 0.755, 95% CI 0.713 to 0.797), while RF had the highest sensitivity (75.9%) and MLP had the highest specificity (64.35%). However, when considering pre-procedural, procedural, and post-procedural features, RF outperformed other models (AUC: 0.775). In this analysis, CB achieved the highest sensitivity (82.95%) and NB had the highest specificity (82.93%).
conclusionOur analyses showed that ML models can predict AKI with acceptable performance. This has potential clinical utility for assessing the individualized risk of AKI in ACS patients undergoing PCI. Additionally, the identified features in the models may aid in mitigating these risk factors.
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