ArticleClinical cardiology2019
A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography.
Article in Clinical cardiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 4 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 4 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
- Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy.BMC nephrology · 2022Pooled it
- A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease.Cardiovascular diabetology · 2025Trial
- Utility of artificial intelligence in predicting clinical outcomes of angiographic procedures: a comprehensive scoping review.Journal of cardiovascular imaging · 2026Review
- Large language model driven multicenter prediction and explainable risk attribution of acute kidney injury.Nature communications · 2026Article
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.Journal of translational medicine · 2025Review
- Statistical models versus machine learning approach for competing risks in proctological surgery.Updates in surgery · 2025Article
- Biomarkers in Contrast-Induced Nephropathy: Advances in Early Detection, Risk Assessment, and Prevention Strategies.International journal of molecular sciences · 2025Review
- Construction of a machine learning-based interpretable prediction model for acute kidney injury in hospitalized patients.Scientific reports · 2025Observational
- Artificial Intelligence in Coronary Artery Interventions: Preprocedural Planning and Procedural Assistance.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Prediction of contrast-associated acute kidney injury with machine-learning in patients undergoing contrast-enhanced computed tomography in emergency department.Scientific reports · 2025Article
- Anesthetic Management Recommendations Using a Machine Learning Algorithm to Reduce the Risk of Acute Kidney Injury After Cardiac Surgeries.Anesthesiology and pain medicine · 2024Article
- Biomarkers in Contrast-Induced Acute Kidney Injury: Towards A New Perspective.International journal of molecular sciences · 2024Review
- Recent Advances of Proteomics in Management of Acute Kidney Injury.Diagnostics (Basel, Switzerland) · 2023Review
- Role of C-Reactive Protein in Kidney Diseases.Kidney diseases (Basel, Switzerland) · 2023Review
- Global biomarkers trends in acute kidney injury: a bibliometric analysis.Renal failure · 2023Article
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
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Authors and funding
8 authors.
Funding
Abstract
backgroundStandard measures of kidney function are only modestly useful for accurate prediction of risk for acute kidney injury (AKI). HYPOTHESIS: Clinical and biomarker data can predict AKI more accurately.
methodsUsing Luminex xMAP technology, we measured 109 biomarkers in blood from 889 patients prior to undergoing coronary angiography. Procedural AKI was defined as an absolute increase in serum creatinine of ≥0.3 mg/dL, a percentage increase in serum creatinine of ≥50%, or a reduction in urine output (documented oliguria of <0.5 mL/kg per hour for >6 hours) within 7 days after contrast exposure. Clinical and biomarker predictors of AKI were identified using machine learning and a final prognostic model was developed with least absolute shrinkage and selection operator (LASSO).
resultsForty-three (4.8%) patients developed procedural AKI. Six predictors were present in the final model: four (history of diabetes, blood urea nitrogen to creatinine ratio, C-reactive protein, and osteopontin) had a positive association with AKI risk, while two (CD5 antigen-like and Factor VII) had a negative association with AKI risk. The final model had a cross-validated area under the receiver operating characteristic curve (AUC) of 0.79 for predicting procedural AKI, and an in-sample AUC of 0.82 (P < 0.001). The optimal score cutoff had 77% sensitivity, 75% specificity, and a negative predictive value of 98% for procedural AKI. An elevated score was predictive of procedural AKI in all subjects (odds ratio = 9.87; P < 0.001).
conclusionsWe describe a clinical and proteomics-supported biomarker model with high accuracy for predicting procedural AKI in patients undergoing coronary angiography.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.