Machine learning-based risk prediction model for acute kidney injury in patients with acute coronary syndrome: A systematic review_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHANG Qi ^1,2 , LI Chenming ² , YAN Guyue ² ,  WU Qing ¹

1. The First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, P. R. China;
2. School of Nursing, Suzhou Medical College, Soochow University, Suzhou, 215006, Jiangsu, P. R. China;

Corresponding author：

WU Qing, Email: qwu@suda.edu.cn

Keywords：

Machine learning; acute coronary syndrome; acute kidney injury; systematic review

DOI：

10.7507/1007-4848.202508033

Video：

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Objective To systematically evaluate the risk prediction models of acute kidney injury in patients with acute coronary syndrome (ACS) based on machine learning, providing a reference for clinical selection of appropriate risk assessment tools. Methods Clinical studies using machine learning methods for predicting the risk of acute kidney injury in ACS patients were retrieved from PubMed, Cochrane Library, EMbase, Web of Science core database, CNKI, Wanfang Database, Chinese Biomedical Literature Database, and VIP Journal Database. The retrieval time was from the establishment of the database to May 24, 2025, and the quality of the model was evaluated using the prediction model bias risk assessment tool. Results Nine articles were included, using 20 machine learning methods to construct 58 prediction models. The area under the receiver operating characteristic curve ranged from 0.740 to 0.894. The most commonly used predictors were age and creatinine. The overall bias risk of the included studies was relatively high, but the applicability was good. Conclusion: Machine learning models can identify the risk of acute kidney injury in ACS patients. All models have good predictive potential, but they are still in the development stage. It is recommended that future studies adopt prospective research and external validation to improve the stability and predictive accuracy of the model.

1.	中国医师协会急诊医师分会, 中华医学会心血管病学分会, 中华医学会检验医学分会. 急性冠脉综合征急诊快速诊疗指南. 中华急诊医学杂志, 2016, 25(4): 397-404.Chinese College of Emergency Physicians, Chinese Society of Cardiology, Chinese Society of Laboratory Medicine. Guidelines for rapid diagnosis and treatment of acute coronary syndrome in emergency department. Chin J Emerg Med, 2016, 25(4): 397-404.
2.	Rangaswami J, Bhalla V, Blair JEA, et al. Cardiorenal syndrome: classification, pathophysiology, diagnosis, and treatment strategies: a scientific statement from the American Heart Association. Circulation, 2019, 139(16): e840-e878.
3.	Ismail Y, Kasmikha Z, Green HL, et al. Cardio-renal syndrome type 1: epidemiology, pathophysiology, and treatment. Semin Nephrol, 2012, 32(1): 18-25.
4.	Lameire NH, Bagga A, Cruz D, et al. Acute kidney injury: an increasing global concern. Lancet, 2013, 382(9887): 170-179.
5.	王润泽. Advances in clinical research on acute kidney injury associated with acute coronary syndromes. Adv Clin Med, 2024, 14(2): 2994-3003.Wang RZ. Advances in clinical research on acute kidney injury associated with acute coronary syndromes. Adv Clin Med, 2024, 14(2): 2994-3003.
6.	国家慢性肾病临床医学研究中心, 中国医师协会肾脏内科医师分会, 中国急性肾损伤临床实践指南专家组. 中国急性肾损伤临床实践指南. 中华医学杂志, 2023, 103(42): 3332-3366.National Clinical Research Center for Kidney Disease, Chinese Medical Doctor Association Nephrology Physician Branch, Chinese Acute Kidney Injury Clinical Practice Guideline Expert Group. Chinese clinical practice guideline for acute kidney injury. Natl Med J China, 2023, 103(42): 3332-3366.
7.	Chatterjee S, Kundu A, Mukherjee D, et al. Risk of contrast-induced acute kidney injury in ST-elevation myocardial infarction patients undergoing multi-vessel intervention-meta-analysis of randomized trials and risk prediction modeling study using observational data. Catheter Cardiovasc Interv, 2017, 90(2): 205-212.
8.	Becker JU, Mayerich D, Padmanabhan M, et al. Artificial intelligence and machine learning in nephropathology. Kidney Int, 2020, 98(1): 65-75.
9.	Moons KG, Hooft L, Williams K, et al. Implementing systematic reviews of prognosis studies in Cochrane. Cochrane Database Syst Rev, 2018, 2018(10): ED000129.
10.	Moons KGM, de Groot JAH, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
11.	陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.Chen XP, Zhang Y, Zhuang YY, et al. PROBAST: a tool for assessing the risk of bias in diagnostic or prognostic prediction model studies. Chin J Evid Based Med, 2020, 20(6): 737-744.
12.	蓝潞杭, 蒋炫东, 王茂峰, 等. 随机森林模型预测急性心肌梗死后急性肾损伤. 中华急诊医学杂志, 2021, 30(4): 491-495.Lan LH, Jiang XD, Wang MF, et al. Random forest model for predicting acute kidney injury after acute myocardial infarction. Chin J Emerg Med, 2021, 30(4): 491-495.
13.	李龙, 刘真义, 李浩然, 等. 基于随机森林模型法的AMI患者并发AKI预测模型的建立. 重庆医学, 2022, 51(24): 4304-4307,4312.Li L, Liu ZY, Li HR, et al. Establishment of a prediction model for AKI in AMI patients based on random forest model method. Chongqing Med, 2022, 51(24): 4304-4307,4312.
14.	Cai D, Xiao T, Zou A, et al. Predicting acute kidney injury risk in acute myocardial infarction patients: an artificial intelligence model using medical information mart for intensive care databases. Front Cardiovasc Med, 2022, 9: 964894.
15.	黄萱, 木胡牙提·乌拉斯汗, 陆晨, 等. 基于机器学习的急性冠脉综合征患者急性肾损伤的预测模型. 临床肾脏病杂志, 2023, 23(8): 653-662.Huang X, Muhuyati W, Lu C, et al. Prediction model of acute kidney injury in patients with acute coronary syndrome based on machine learning. J Clin Nephrol, 2023, 23(8): 653-662.
16.	Song L, Li Y, Nie S, et al. Using machine learning to predict adverse events in acute coronary syndrome: a retrospective study. Clin Cardiol, 2023, 46(12): 1594-1602.
17.	Behnoush AH, Shariatnia MM, Khalaji A, et al. Predictive modeling for acute kidney injury after percutaneous coronary intervention in patients with acute coronary syndrome: a machine learning approach. Eur J Med Res, 2024, 29(1): 91.
18.	叶楠, 祝闯, 徐丰博, 等. 机器学习算法构建急性心肌梗死患者发生急性肾损伤风险预测模型并与传统模型比较. 中华肾脏病杂志, 2024, 40(3): 175-182.Ye N, Zhu C, Xu FB, et al. Machine learning algorithms to construct a risk prediction model for acute kidney injury in patients with acute myocardial infarction and compare with traditional models. Chin J Nephrol, 2024, 40(3): 175-182.
19.	Wei J, Cai D, Xiao T, et al. Artificial intelligence algorithms permits rapid acute kidney injury risk classification of patients with acute myocardial infarction. Heliyon, 2024, 10(16): e36051.
20.	吕华胜, 拉再依·巴合提, 袁腾, 等. STEMI患者PCI术后急性肾损伤的机器学习预测模型构建与验证. 西安交通大学学报(医学版), 2025, 46(2): 1-12.Lyu HS, Lazayi B, Yuan T, et al. Construction and validation of a machine learning prediction model for acute kidney injury after PCI in STEMI patients. J Xi'an Jiaotong Univ (Med Sci), 2025, 46(2): 1-12.
21.	Harrell J. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. 2nd ed. Cham: Springer, 2015.
22.	李泽茂, 丁静, 马汝航, 等. 基于人工智能的冠状动脉周围脂肪影像组学及衰减指数构建可解释性机器学习模型预测冠心病患者主要心血管不良事件发生. 临床放射学杂志, 2025, 44(3): 456-464.Li ZM, Ding J, Ma RH, et al. Interpretable machine learning model based on artificial intelligence for pericoronary adipose tissue radiomics and attenuation index to predict major adverse cardiovascular events in patients with coronary artery disease. J Clin Radiol, 2025, 44(3): 456-464.
23.	D'Ascenzo F, De Filippo O, Gallone G, et al. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. Lancet, 2021, 397(10270): 199-207.
24.	Lullo LD, Bellasi A, Barbera V, et al. Cardionephrology and cardiorenal disease in Italy: state of the art. Rev Cardiovasc Med, 2021, 22(3): 963-971.
25.	Formica M, Politano P, Marazzi F, et al. Acute kidney injury and chronic kidney disease in the elderly and polypharmacy. Blood Purif, 2018, 46(4): 332-336.
26.	Pocock SJ, Huo Y, Van de Werf F, et al. Predicting two-year mortality from discharge after acute coronary syndrome: an internationally-based risk score. Eur Heart J Acute Cardiovasc Care, 2019, 8(8): 727-737.
27.	Ranucci M, Castelvecchio S, Menicanti L, et al. Risk of assessing mortality risk in elective cardiac operations: age, creatinine, ejection fraction, and the law of parsimony. Circulation, 2009, 119(24): 3053-3061.
28.	Araujo GN, Pivatto Junior F, Fuhr B, et al. Simplifying contrast-induced acute kidney injury prediction after primary percutaneous coronary intervention: the age, creatinine and ejection fraction score. Cardiovasc Interv Ther, 2018, 33(3): 224-231.

1. 中国医师协会急诊医师分会, 中华医学会心血管病学分会, 中华医学会检验医学分会. 急性冠脉综合征急诊快速诊疗指南. 中华急诊医学杂志, 2016, 25(4): 397-404.Chinese College of Emergency Physicians, Chinese Society of Cardiology, Chinese Society of Laboratory Medicine. Guidelines for rapid diagnosis and treatment of acute coronary syndrome in emergency department. Chin J Emerg Med, 2016, 25(4): 397-404.
2. Rangaswami J, Bhalla V, Blair JEA, et al. Cardiorenal syndrome: classification, pathophysiology, diagnosis, and treatment strategies: a scientific statement from the American Heart Association. Circulation, 2019, 139(16): e840-e878.
3. Ismail Y, Kasmikha Z, Green HL, et al. Cardio-renal syndrome type 1: epidemiology, pathophysiology, and treatment. Semin Nephrol, 2012, 32(1): 18-25.
4. Lameire NH, Bagga A, Cruz D, et al. Acute kidney injury: an increasing global concern. Lancet, 2013, 382(9887): 170-179.
5. 王润泽. Advances in clinical research on acute kidney injury associated with acute coronary syndromes. Adv Clin Med, 2024, 14(2): 2994-3003.Wang RZ. Advances in clinical research on acute kidney injury associated with acute coronary syndromes. Adv Clin Med, 2024, 14(2): 2994-3003.
6. 国家慢性肾病临床医学研究中心, 中国医师协会肾脏内科医师分会, 中国急性肾损伤临床实践指南专家组. 中国急性肾损伤临床实践指南. 中华医学杂志, 2023, 103(42): 3332-3366.National Clinical Research Center for Kidney Disease, Chinese Medical Doctor Association Nephrology Physician Branch, Chinese Acute Kidney Injury Clinical Practice Guideline Expert Group. Chinese clinical practice guideline for acute kidney injury. Natl Med J China, 2023, 103(42): 3332-3366.
7. Chatterjee S, Kundu A, Mukherjee D, et al. Risk of contrast-induced acute kidney injury in ST-elevation myocardial infarction patients undergoing multi-vessel intervention-meta-analysis of randomized trials and risk prediction modeling study using observational data. Catheter Cardiovasc Interv, 2017, 90(2): 205-212.
8. Becker JU, Mayerich D, Padmanabhan M, et al. Artificial intelligence and machine learning in nephropathology. Kidney Int, 2020, 98(1): 65-75.
9. Moons KG, Hooft L, Williams K, et al. Implementing systematic reviews of prognosis studies in Cochrane. Cochrane Database Syst Rev, 2018, 2018(10): ED000129.
10. Moons KGM, de Groot JAH, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
11. 陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.Chen XP, Zhang Y, Zhuang YY, et al. PROBAST: a tool for assessing the risk of bias in diagnostic or prognostic prediction model studies. Chin J Evid Based Med, 2020, 20(6): 737-744.
12. 蓝潞杭, 蒋炫东, 王茂峰, 等. 随机森林模型预测急性心肌梗死后急性肾损伤. 中华急诊医学杂志, 2021, 30(4): 491-495.Lan LH, Jiang XD, Wang MF, et al. Random forest model for predicting acute kidney injury after acute myocardial infarction. Chin J Emerg Med, 2021, 30(4): 491-495.
13. 李龙, 刘真义, 李浩然, 等. 基于随机森林模型法的AMI患者并发AKI预测模型的建立. 重庆医学, 2022, 51(24): 4304-4307,4312.Li L, Liu ZY, Li HR, et al. Establishment of a prediction model for AKI in AMI patients based on random forest model method. Chongqing Med, 2022, 51(24): 4304-4307,4312.
14. Cai D, Xiao T, Zou A, et al. Predicting acute kidney injury risk in acute myocardial infarction patients: an artificial intelligence model using medical information mart for intensive care databases. Front Cardiovasc Med, 2022, 9: 964894.
15. 黄萱, 木胡牙提·乌拉斯汗, 陆晨, 等. 基于机器学习的急性冠脉综合征患者急性肾损伤的预测模型. 临床肾脏病杂志, 2023, 23(8): 653-662.Huang X, Muhuyati W, Lu C, et al. Prediction model of acute kidney injury in patients with acute coronary syndrome based on machine learning. J Clin Nephrol, 2023, 23(8): 653-662.
16. Song L, Li Y, Nie S, et al. Using machine learning to predict adverse events in acute coronary syndrome: a retrospective study. Clin Cardiol, 2023, 46(12): 1594-1602.
17. Behnoush AH, Shariatnia MM, Khalaji A, et al. Predictive modeling for acute kidney injury after percutaneous coronary intervention in patients with acute coronary syndrome: a machine learning approach. Eur J Med Res, 2024, 29(1): 91.
18. 叶楠, 祝闯, 徐丰博, 等. 机器学习算法构建急性心肌梗死患者发生急性肾损伤风险预测模型并与传统模型比较. 中华肾脏病杂志, 2024, 40(3): 175-182.Ye N, Zhu C, Xu FB, et al. Machine learning algorithms to construct a risk prediction model for acute kidney injury in patients with acute myocardial infarction and compare with traditional models. Chin J Nephrol, 2024, 40(3): 175-182.
19. Wei J, Cai D, Xiao T, et al. Artificial intelligence algorithms permits rapid acute kidney injury risk classification of patients with acute myocardial infarction. Heliyon, 2024, 10(16): e36051.
20. 吕华胜, 拉再依·巴合提, 袁腾, 等. STEMI患者PCI术后急性肾损伤的机器学习预测模型构建与验证. 西安交通大学学报(医学版), 2025, 46(2): 1-12.Lyu HS, Lazayi B, Yuan T, et al. Construction and validation of a machine learning prediction model for acute kidney injury after PCI in STEMI patients. J Xi'an Jiaotong Univ (Med Sci), 2025, 46(2): 1-12.
21. Harrell J. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. 2nd ed. Cham: Springer, 2015.
22. 李泽茂, 丁静, 马汝航, 等. 基于人工智能的冠状动脉周围脂肪影像组学及衰减指数构建可解释性机器学习模型预测冠心病患者主要心血管不良事件发生. 临床放射学杂志, 2025, 44(3): 456-464.Li ZM, Ding J, Ma RH, et al. Interpretable machine learning model based on artificial intelligence for pericoronary adipose tissue radiomics and attenuation index to predict major adverse cardiovascular events in patients with coronary artery disease. J Clin Radiol, 2025, 44(3): 456-464.
23. D'Ascenzo F, De Filippo O, Gallone G, et al. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. Lancet, 2021, 397(10270): 199-207.
24. Lullo LD, Bellasi A, Barbera V, et al. Cardionephrology and cardiorenal disease in Italy: state of the art. Rev Cardiovasc Med, 2021, 22(3): 963-971.
25. Formica M, Politano P, Marazzi F, et al. Acute kidney injury and chronic kidney disease in the elderly and polypharmacy. Blood Purif, 2018, 46(4): 332-336.
26. Pocock SJ, Huo Y, Van de Werf F, et al. Predicting two-year mortality from discharge after acute coronary syndrome: an internationally-based risk score. Eur Heart J Acute Cardiovasc Care, 2019, 8(8): 727-737.
27. Ranucci M, Castelvecchio S, Menicanti L, et al. Risk of assessing mortality risk in elective cardiac operations: age, creatinine, ejection fraction, and the law of parsimony. Circulation, 2009, 119(24): 3053-3061.
28. Araujo GN, Pivatto Junior F, Fuhr B, et al. Simplifying contrast-induced acute kidney injury prediction after primary percutaneous coronary intervention: the age, creatinine and ejection fraction score. Cardiovasc Interv Ther, 2018, 33(3): 224-231.

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Latest ArticlesMachine learning-based risk prediction model for acute kidney injury in patients with acute coronary syndrome: A systematic review

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