{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:08:53Z","timestamp":1788952133404,"version":"build-2803163510"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Renal dysfunction is an important determinant of cardiovascular prognosis in patients with coronary artery disease. Estimated glomerular filtration rate (eGFR) is widely used to assess kidney function, but different equations may yield varying risk stratification results. This study aimed to compare the predictive performance of creatinine-based and cystatin C\u2013based eGFR equations for 1-year major adverse cardiovascular events (MACE) in patients undergoing percutaneous coronary intervention (PCI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included 560 patients who underwent PCI for acute coronary syndrome. Baseline clinical, laboratory, and echocardiographic variables were collected. Renal function was assessed using both creatinine-based and cystatin C\u2013based eGFR equations. Logistic regression with backward selection based on the Akaike Information Criterion (AIC) was performed to identify independent predictors of 1-year MACE. In addition, multiple machine learning models were developed to evaluate predictive performance, and Shapley Additive Explanations (SHAP) analysis was applied to assess feature importance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>During the 1-year follow-up, MACE occurred in 72 patients, comprising 71 repeat PCIs and one cardiac death. Equations relying on cystatin C detected a larger share of patients with impaired renal function compared with creatinine-based formulas. However, moderate-to-severe renal dysfunction defined by creatinine-based eGFR was significantly associated with an increased risk of 1-year MACE, although it was not retained as an independent predictor after AIC\u2013based multivariable adjustment. Machine learning models demonstrated modest predictive performance overall, with logistic regression achieving the highest discriminative ability. SHAP analysis indicated that cardiovascular functional parameters and metabolic markers were the major contributors to risk prediction, while renal function indicators provided complementary prognostic information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Creatinine-based eGFR showed a stronger observed association with 1-year MACE after PCI than cystatin C\u2013based eGFR in this single-center cohort; these findings do not establish clinical superiority of either equation and require external validation. Renal function assessment remains an important component of cardiovascular risk stratification, and traditional regression models performed comparably to machine learning approaches in moderately sized clinical datasets.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1863081","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:12:07Z","timestamp":1788948727000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative predictive performance of different eGFR equations for 1-year major adverse cardiovascular events after percutaneous coronary intervention in patients with acute coronary syndrome"],"prefix":"10.3389","volume":"13","author":[{"given":"Yicheng","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Cardiology and the Institute of Heart and Vascular Diseases, The Second Affiliated Hospital of Dalian Medical University","place":["Dalian, Liaoning, China"]},{"name":"College of Basic Medical Science, Dalian Medical University","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangming","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Cardiology and the Institute of Heart and Vascular Diseases, The Second Affiliated Hospital of Dalian Medical University","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruoxi","family":"Qi","sequence":"additional","affiliation":[{"name":"Department of Mental Health and Psychology, Dalian Medical University","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Sports Medicines and Rehabilitation, Shandong First Medical University","place":["Taian, Shandong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajie","family":"Mei","sequence":"additional","affiliation":[{"name":"Department of Cardiology and the Institute of Heart and Vascular Diseases, The Second Affiliated Hospital of Dalian Medical University","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenzhu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Cardiology and the Institute of Heart and Vascular Diseases, The Second Affiliated Hospital of Dalian Medical University","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Cardiology and the Institute of Heart and Vascular Diseases, The Second Affiliated Hospital of Dalian Medical University","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenli","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Cardiology and the Institute of Heart and Vascular Diseases, The Second Affiliated Hospital of Dalian Medical University","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Cardiology and the Institute of Heart and Vascular Diseases, The Second Affiliated Hospital of Dalian Medical University","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Cardiology and the Institute of Heart and Vascular Diseases, The Second Affiliated Hospital of Dalian Medical University","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Cardiology and the Institute of Heart and Vascular Diseases, The Second Affiliated Hospital of Dalian Medical University","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohong","family":"Geng","sequence":"additional","affiliation":[{"name":"Department of Cardiology and the Institute of Heart and Vascular Diseases, The Second Affiliated Hospital of Dalian Medical University","place":["Dalian, Liaoning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1093\/eurheartj\/ehy394","article-title":"2018 ESC\/EACTS guidelines on myocardial revascularization","volume":"40","author":"Neumann","year":"2019","journal-title":"Eur Heart J"},{"key":"B2","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1056\/NEJMoa0804626","article-title":"Percutaneous coronary intervention versus coronary-artery bypass grafting for severe coronary artery disease","volume":"360","author":"Serruys","year":"2009","journal-title":"N Engl J Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1820","DOI":"10.1056\/NEJMoa1909406","article-title":"Five-year outcomes after PCI or CABG for left main coronary disease","volume":"381","author":"Stone","year":"2019","journal-title":"N Engl J Med"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1296","DOI":"10.1056\/NEJMoa041031","article-title":"Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization","volume":"351","author":"Go","year":"2004","journal-title":"N Engl J Med"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.1161\/cir.0000000000001186","article-title":"A synopsis of the evidence for the science and clinical management of cardiovascular-kidney-metabolic (CKM) syndrome: a scientific statement from the American heart association","volume":"148","author":"Ndumele","year":"2023","journal-title":"Circulation"},{"key":"B6","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/s10741-014-9460-9","article-title":"Chronic kidney disease and cardiovascular complications","volume":"20","author":"Di Lullo","year":"2015","journal-title":"Heart Fail Rev"},{"key":"B7","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s00109-021-02037-7","article-title":"Vascular pathologies in chronic kidney disease: pathophysiological mechanisms and novel therapeutic approaches","volume":"99","author":"D\u00fcsing","year":"2021","journal-title":"J Mol Med"},{"key":"B8","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1080\/14779072.2018.1526082","article-title":"Assessing and minimizing the risk of percutaneous coronary intervention in patients with chronic kidney disease","volume":"16","author":"Guedeney","year":"2018","journal-title":"Expert Rev Cardiovasc Ther"},{"key":"B9","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.pcad.2024.12.004","article-title":"Coronary artery disease and percutaneous coronary intervention in patients with severe chronic kidney disease","volume":"88","author":"Shin","year":"2025","journal-title":"Prog Cardiovasc Dis"},{"key":"B10","doi-asserted-by":"publisher","first-page":"S117","DOI":"10.1016\/j.kint.2023.10.018","article-title":"KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease","volume":"105","author":"Stevens","year":"2024","journal-title":"Kidney Int"},{"key":"B11","doi-asserted-by":"publisher","first-page":"604","DOI":"10.7326\/0003-4819-150-9-200905050-00006","article-title":"A new equation to estimate glomerular filtration rate","volume":"150","author":"Levey","year":"2009","journal-title":"Ann Intern Med"},{"key":"B12","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1056\/NEJMoa1214234","article-title":"Cystatin C versus creatinine in determining risk based on kidney function","volume":"369","author":"Shlipak","year":"2013","journal-title":"N Engl J Med"},{"key":"B13","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1053\/j.ajkd.2007.11.018","article-title":"Estimating GFR using serum cystatin C alone and in combination with serum creatinine: a pooled analysis of 3,418 individuals with CKD","volume":"51","author":"Stevens","year":"2008","journal-title":"Am J Kid Dis"},{"key":"B14","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/s40620-014-0087-7","article-title":"Calibration and precision of serum creatinine and plasma cystatin C measurement: impact on the estimation of glomerular filtration rate","volume":"27","author":"Delanaye","year":"2014","journal-title":"J Nephrol"},{"key":"B15","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1093\/ndt\/gfw425","article-title":"Estimating glomerular filtration rate for the full age spectrum from serum creatinine and cystatin C","volume":"32","author":"Pottel","year":"2017","journal-title":"Nephrol Dial Transplant"},{"key":"B16","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1186\/s12933-024-02439-0","article-title":"Construction of machine learning diagnostic models for cardiovascular pan-disease based on blood routine and biochemical detection data","volume":"23","author":"Wang","year":"2024","journal-title":"Cardiovasc Diabetol"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1390729","DOI":"10.3389\/fendo.2024.1390729","article-title":"Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease","volume":"15","author":"Zhu","year":"2024","journal-title":"Front Endocrinol"},{"key":"B18","doi-asserted-by":"publisher","first-page":"2937","DOI":"10.1681\/asn.2006040368","article-title":"Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease","volume":"17","author":"Ma","year":"2006","journal-title":"J Am Soc Nephrol"},{"key":"B19","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1056\/NEJMoa1114248","article-title":"Estimating glomerular filtration rate from serum creatinine and cystatin C","volume":"367","author":"Inker","year":"2012","journal-title":"N Engl J Med"},{"key":"B20","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ejim.2023.10.013","article-title":"Residual cardiovascular risk: when should we treat it?","volume":"120","author":"Gomez-Delgado","year":"2024","journal-title":"Eur J Intern Med"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.3390\/cells10061391","article-title":"Inflammation during percutaneous coronary intervention-prognostic value, mechanisms and therapeutic targets","volume":"10","author":"Tucker","year":"2021","journal-title":"Cells"},{"key":"B22","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1161\/circulationaha.124.072009","article-title":"Derivation and validation of the PRECISE-HBR score to predict bleeding after percutaneous coronary intervention","volume":"151","author":"Gragnano","year":"2025","journal-title":"Circulation"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1497","DOI":"10.1016\/j.jcin.2019.04.015","article-title":"Incidence, predictors, and outcomes of acute ischemic stroke following percutaneous coronary intervention","volume":"12","author":"Alkhouli","year":"2019","journal-title":"JACC Cardiovasc Interv"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1016\/j.jacc.2024.01.012","article-title":"Incidence, predictors, and impact of hospital readmission after revascularization for left main coronary disease","volume":"83","author":"Kosmidou","year":"2024","journal-title":"J Am Coll Cardiol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1287434","DOI":"10.3389\/fcvm.2023.1287434","article-title":"Prediction models for major adverse cardiovascular events after percutaneous coronary intervention: a systematic review","volume":"10","author":"Deng","year":"2024","journal-title":"Front Cardiovasc Med"},{"key":"B26","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1016\/s0140-6736(18)30423-9","article-title":"Mortality after coronary artery bypass grafting versus percutaneous coronary intervention with stenting for coronary artery disease: a pooled analysis of individual patient data","volume":"391","author":"Head","year":"2018","journal-title":"Lancet"},{"key":"B27","doi-asserted-by":"publisher","first-page":"e85","DOI":"10.3346\/jkms.2026.41.e85","article-title":"Effectiveness and safety of biodegradable polymer-coated everolimus-eluting stent in multivessel coronary artery disease in routine clinical practice: a Korean multicenter, prospective, and observational study","volume":"41","author":"Park","year":"2026","journal-title":"J Korean Med Sci"},{"key":"B28","doi-asserted-by":"publisher","first-page":"1852","DOI":"10.1161\/circresaha.114.302721","article-title":"Mechanisms of plaque formation and rupture","volume":"114","author":"Bentzon","year":"2014","journal-title":"Circ Res"},{"key":"B29","doi-asserted-by":"publisher","first-page":"3461","DOI":"10.1002\/ehf2.14063","article-title":"Association of ejection fraction with mortality and cardiovascular events in patients with coronary artery disease","volume":"9","author":"Liu","year":"2022","journal-title":"ESC Heart Fail"},{"key":"B30","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1161\/01.HYP.0000102971.85504.7c","article-title":"Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American heart association councils on kidney in cardiovascular disease, high blood pressure research, clinical cardiology, and epidemiology and prevention","volume":"42","author":"Sarnak","year":"2003","journal-title":"Hypertension"},{"key":"B31","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1038\/s41581-022-00616-6","article-title":"Epidemiology and risk of cardiovascular disease in populations with chronic kidney disease","volume":"18","author":"Matsushita","year":"2022","journal-title":"Nat Rev Nephrol"},{"key":"B32","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1053\/ajkd.2002.34487","article-title":"Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis","volume":"40","author":"Dharnidharka","year":"2002","journal-title":"Am J Kidney Dis"},{"key":"B33","doi-asserted-by":"publisher","first-page":"2148","DOI":"10.1093\/eurheartj\/ehaf167","article-title":"Cardiovascular disease in chronic kidney disease","volume":"46","author":"Marx-Sch\u00fctt","year":"2025","journal-title":"Eur Heart J"},{"key":"B34","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1161\/circresaha.123.321752","article-title":"Endothelial cell dysfunction and increased cardiovascular risk in patients with chronic kidney disease","volume":"132","author":"Baaten","year":"2023","journal-title":"Circ Res"},{"key":"B35","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1001\/jama.2017.18391","article-title":"Big data and machine learning in health care","volume":"319","author":"Beam","year":"2018","journal-title":"J Am Med Assoc"},{"key":"B36","doi-asserted-by":"publisher","first-page":"103470","DOI":"10.1016\/j.redox.2024.103470","article-title":"Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants","volume":"79","author":"Qi","year":"2025","journal-title":"Redox Biol"},{"key":"B37","doi-asserted-by":"publisher","first-page":"137039","DOI":"10.1016\/j.chemosphere.2022.137039","article-title":"Development of an interpretable machine learning model associated with heavy metals\u2019 exposure to identify coronary heart disease among US adults via SHAP: findings of the US NHANES from 2003 to 2018","volume":"311","author":"Li","year":"2023","journal-title":"Chemosphere"}],"container-title":["Frontiers in Cardiovascular Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2026.1863081\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:12:08Z","timestamp":1788948728000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2026.1863081\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,9]]},"references-count":37,"alternative-id":["10.3389\/fcvm.2026.1863081"],"URL":"https:\/\/doi.org\/10.3389\/fcvm.2026.1863081","relation":{},"ISSN":["2297-055X"],"issn-type":[{"value":"2297-055X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,9]]},"article-number":"1863081"}}