{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T15:07:22Z","timestamp":1793113642315,"version":"4.5.0"},"reference-count":30,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Background and Objectives: Contrast-associated acute kidney injury (CA-AKI) is a frequent and clinically significant complication in patients with acute myocardial infarction (AMI) undergoing coronary angiography. Early and accurate risk stratification remains challenging with conventional models that rely on linear assumptions and limited variable integration. This study aimed to evaluate and compare the predictive performance of multiple machine learning (ML) algorithms with traditional logistic regression and the Mehran risk score for CA-AKI prediction and to explore key determinants of risk using explainable artificial intelligence methods. Materials and Methods: This retrospective, single-center study included 1741 patients with AMI who underwent coronary angiography. CA-AKI was defined according to KDIGO criteria. Multiple ML models, including gradient boosting machine (GBM), random forest (RF), XGBoost, support vector machine, elastic net, and standard logistic regression were developed using routinely available clinical and laboratory variables. A weighted ensemble model combining the best-performing algorithms was constructed. Model discrimination was assessed using area under the receiver operating characteristic curve (AUC), along with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Model interpretability was evaluated using feature importance and SHapley Additive exPlanations (SHAP). Results: CA-AKI occurred in 356 patients (20.4%). In multivariable logistic regression, lower left ventricular ejection fraction, higher contrast volume, lower sodium, lower hemoglobin, and higher neutrophil-to-lymphocyte ratio (NLR) were independently associated with CA-AKI. Among ML approaches, the weighted ensemble model demonstrated the highest discriminative performance (AUC 0.721), outperforming logistic regression and the Mehran risk score (AUC 0.608). Importantly, the ensemble model achieved a consistently high NPV (0.942), enabling reliable identification of low-risk patients. Explainability analyses revealed that inflammatory markers, particularly NLR, along with sodium, uric acid, baseline renal indices, and contrast burden, were the most influential predictors across models. Conclusions: In patients with AMI undergoing coronary angiography, interpretable ML models, especially ensemble and gradient boosting-based approaches, provide superior risk stratification for CA-AKI compared with conventional methods. The high negative predictive value highlights their clinical utility in safely identifying low-risk patients and supporting individualized, risk-adapted preventive strategies.<\/jats:p>","DOI":"10.3390\/medicina62010228","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T13:55:34Z","timestamp":1769176534000},"page":"228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning-Based Prediction and Feature Attribution Analysis of Contrast-Associated Acute Kidney Injury in Patients with Acute Myocardial Infarction"],"prefix":"10.3390","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4654-9002","authenticated-orcid":false,"given":"Neriman S\u0131la","family":"Ko\u00e7","sequence":"first","affiliation":[{"name":"Division of Nephrology, Department of Internal Medicine, Ankara Etlik City Hospital, 06170 Ankara, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7931-5172","authenticated-orcid":false,"given":"Can Ozan","family":"Ulusoy","sequence":"additional","affiliation":[{"name":"Perinatology Department, Bing\u00f6l State Hospital, 12000 Bing\u00f6l, T\u00fcrkiye"},{"name":"MSc Data Science Program, Hacettepe University, 06800 Ankara, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0834-900X","authenticated-orcid":false,"given":"Berrak It\u0131r","family":"Ayl\u0131","sequence":"additional","affiliation":[{"name":"Department of Health Sciences, Hacettepe University, 06100 Ankara, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3523-8783","authenticated-orcid":false,"given":"Yusuf Bozkurt","family":"\u015eahin","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Ankara Etlik City Hospital, 06170 Ankara, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7193-4324","authenticated-orcid":false,"given":"Veysel Ozan","family":"Tan\u0131k","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Ankara Etlik City Hospital, 06170 Ankara, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8119-3893","authenticated-orcid":false,"given":"Arzu","family":"Akg\u00fcl","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Internal Medicine, Ankara Etlik City Hospital, 06170 Ankara, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0881-7851","authenticated-orcid":false,"given":"Ekrem","family":"Kara","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Internal Medicine, Faculty of Medicine, Recep Tayyip Erdogan University, 53100 Rize, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lee, P.-H., Huang, S.M., Tsai, Y.-C., Wang, Y.-T., and Chew, F.Y. (2025). Biomarkers in Contrast-Induced Nephropathy: Advances in Early Detection, Risk Assessment, and Prevention Strategies. Int. J. Mol. Sci., 26.","DOI":"10.3390\/ijms26072869"},{"key":"ref_2","first-page":"299","article-title":"Contrast-Induced Acute Kidney Injury-Definitions, Epidemiology, and Implications","volume":"9","author":"Azzalini","year":"2020","journal-title":"Interv. Cardiol. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.bjae.2020.07.006","article-title":"Contrast-associated acute kidney injury","volume":"20","author":"Everson","year":"2020","journal-title":"BJA Educ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1097\/MOP.0b013e328343f4dd","article-title":"Biomarkers for the early detection of acute kidney injury","volume":"23","author":"Devarajan","year":"2011","journal-title":"Curr. Opin. Pediatr."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Annalisa, S., Marco, F., Bibiana, B., Bruno, B.M., Pierpaolo, C., Giuseppe, G., and Francesco, P. (2025). Acute kidney injury prediction and prognostication using machine learning. Int. Urol. Nephrol., Epub ahead of print.","DOI":"10.1007\/s11255-025-04959-4"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1186\/s40001-024-01782-y","article-title":"Evaluating biomarkers for contrast-induced nephropathy following coronary interventions: An umbrella review on meta-analyses","volume":"29","author":"Mahapatro","year":"2024","journal-title":"Eur. J. Med. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1080\/10408363.2025.2497843","article-title":"Machine learning models for acute kidney injury prediction and management: A scoping review of externally validated studies","volume":"62","author":"Rehman","year":"2025","journal-title":"Crit. Rev. Clin. Lab. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sun, L., Zhu, W., Chen, X., Jiang, J., Ji, Y., Liu, N., Xu, Y., Zhuang, Y., Sun, Z., and Wang, Q. (2020). Machine Learning to Predict Contrast-Induced Acute Kidney Injury in Patients With Acute Myocardial Infarction. Front. Med., 7.","DOI":"10.3389\/fmed.2020.592007"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7088","DOI":"10.1038\/s41598-025-86933-9","article-title":"Prediction of contrast-associated acute kidney injury with machine-learning in patients undergoing contrast-enhanced computed tomography in emergency department","volume":"15","author":"Lee","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.avsg.2025.01.043","article-title":"Contrast-Induced Acute Kidney Injury in Lower Limb Percutaneous Transluminal Angioplasty: A Machine Learning Approach for Preoperative Risk Prediction","volume":"115","author":"Lim","year":"2025","journal-title":"Ann. Vasc. Surg."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1007\/s11255-024-04257-5","article-title":"Predictive performance of machine learning models for kidney complications following coronary interventions: A systematic review and meta-analysis","volume":"57","author":"Najdaghi","year":"2025","journal-title":"Int. Urol. Nephrol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, L., Cao, X., Yang, Y., Fu, S., Jia, Y., Hu, W., and Xiang, F. (2025). Risk prediction models for contrast-induced acute kidney injury in patients with acute coronary syndromes: A systematic review and meta-analysis. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1629369"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s12325-024-03036-z","article-title":"Predictors for Irreversibility of Contrast-Induced Acute Kidney Injury in Patients with Obesity After Contrast-Enhanced Computed Tomography Coronary Angiography","volume":"42","author":"Berezina","year":"2025","journal-title":"Adv. Ther."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"c179","DOI":"10.1159\/000339789","article-title":"KDIGO clinical practice guidelines for acute kidney injury","volume":"120","author":"Khwaja","year":"2012","journal-title":"Nephron Clin. Pract."},{"key":"ref_15","first-page":"1393","article-title":"A simple risk score for prediction of contrast-induced nephropathy after percutaneous coronary intervention: Development and initial validation","volume":"44","author":"Mehran","year":"2004","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/0002-9149(67)90023-9","article-title":"Treatment of myocardial infarction in a coronary care unit. A two year experience with 250 patients","volume":"20","author":"Killip","year":"1967","journal-title":"Am. J. Cardiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1177\/0003319715578057","article-title":"Platelet-to-Lymphocyte Ratio Predicts Contrast-Induced Nephropathy in Patients With Non-ST-Segment Elevation Acute Coronary Syndrome","volume":"66","author":"Kocas","year":"2015","journal-title":"Angiology"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2146","DOI":"10.1056\/NEJMra1805256","article-title":"Contrast-Associated Acute Kidney Injury","volume":"380","author":"Mehran","year":"2019","journal-title":"N. Engl. J. Med."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Akg\u00fcl, A., Tunca, \u00c7., Ta\u015f, A., and Ayl\u0131, M.D. (2025). Association of Serum Uric Acid and CRP\/Albumin Ratio With Contrast-Induced Nephropathy in Acute Myocardial Infarction Patients. Angiology, Epub ahead of print.","DOI":"10.1177\/00033197251376082"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e16801","DOI":"10.1097\/MD.0000000000016801","article-title":"Could platelet-to-lymphocyte ratio be a predictor for contrast-induced nephropathy in patients with acute coronary syndrome?: A systematic review and meta-analysis","volume":"98","author":"Jiang","year":"2019","journal-title":"Medicine"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.hlc.2016.01.007","article-title":"Association of Neutrophil-to-lymphocyte Ratio with Contrast-induced Nephropathy in Patients with Non-ST-elevation Acute Coronary Syndrome Treated with Percutaneous Coronary Intervention","volume":"25","author":"Kurtul","year":"2016","journal-title":"Heart Lung Circ."},{"key":"ref_22","first-page":"59","article-title":"Neutrophil-to-Lymphocyte Ratio Predicts Contrast-Induced Acute Kidney Injury in Patients with ST-Elevation Myocardial Infarction Treated with Primary Percutaneous Coronary Intervention","volume":"14","author":"Velibey","year":"2019","journal-title":"J. Tehran Heart Cent."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1007\/s40620-021-01162-3","article-title":"Neutrophil-to-lymphocyte ratio is a marker for acute kidney injury progression and mortality in critically ill populations: A population-based, multi-institutional study","volume":"35","author":"Chen","year":"2022","journal-title":"J. Nephrol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s10238-025-01746-4","article-title":"Predictive and prognostic value of the neutrophil-to-lymphocyte ratio for acute kidney injury: A systematic review and meta-analysis","volume":"25","author":"Wei","year":"2025","journal-title":"Clin. Exp. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"969","DOI":"10.2169\/internalmedicine.50.4703","article-title":"Relationship between hyponatremia and in-hospital outcomes in Chinese patients with ST-elevation myocardial infarction","volume":"50","author":"Tang","year":"2011","journal-title":"Intern. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1111\/apha.12067","article-title":"Chronic activation of vasopressin V2 receptor signalling lowers renal medullary oxygen levels in rats","volume":"207","author":"Dietrich","year":"2013","journal-title":"Acta Physiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"325","DOI":"10.3892\/br.2017.966","article-title":"The role of hyperuricemia on vascular endothelium dysfunction","volume":"7","author":"Zhen","year":"2017","journal-title":"Biomed. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2474206","DOI":"10.1080\/0886022X.2025.2474206","article-title":"Deep learning for the prediction of acute kidney injury after coronary angiography and intervention in patients with chronic kidney disease: A model development and validation study","volume":"47","author":"Tang","year":"2025","journal-title":"Ren. Fail."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2258983","DOI":"10.1080\/0886022X.2023.2258983","article-title":"Correlation between neutrophil-to-lymphocyte ratio and contrast-induced acute kidney injury and the establishment of machine-learning-based predictive models","volume":"45","author":"Zhou","year":"2023","journal-title":"Ren. Fail."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Nicol\u00e1s, M., Gonz\u00e1lez-Guerrero, C., Goicoechea, M., Bosc\u00e1, L., Vali\u00f1o-Rivas, L., and L\u00e1zaro, A. (2024). Biomarkers in Contrast-Induced Acute Kidney Injury: Towards A New Perspective. Int. J. Mol. Sci., 25.","DOI":"10.20944\/preprints202402.0764.v1"}],"container-title":["Medicina"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1648-9144\/62\/1\/228\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T14:25:14Z","timestamp":1769178314000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1648-9144\/62\/1\/228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,22]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["medicina62010228"],"URL":"https:\/\/doi.org\/10.3390\/medicina62010228","relation":{},"ISSN":["1648-9144"],"issn-type":[{"value":"1648-9144","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,22]]}}}