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.	中國醫師協會急診醫師分會, 中華醫學會心血管病學分會, 中華醫學會檢驗醫學分會. 急性冠脈綜合征急診快速診療指南. 中華急診醫學雜志, 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.	王潤澤. 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.	國家慢性腎病臨床醫學研究中心, 中國醫師協會腎臟內科醫師分會, 中國急性腎損傷臨床實踐指南專家組. 中國急性腎損傷臨床實踐指南. 中華醫學雜志, 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.	陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 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.	藍潞杭, 蔣炫東, 王茂峰, 等. 隨機森林模型預測急性心肌梗死后急性腎損傷. 中華急診醫學雜志, 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.	李龍, 劉真義, 李浩然, 等. 基于隨機森林模型法的AMI患者并發AKI預測模型的建立. 重慶醫學, 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.	黃萱, 木胡牙提·烏拉斯汗, 陸晨, 等. 基于機器學習的急性冠脈綜合征患者急性腎損傷的預測模型. 臨床腎臟病雜志, 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.	葉楠, 祝闖, 徐豐博, 等. 機器學習算法構建急性心肌梗死患者發生急性腎損傷風險預測模型并與傳統模型比較. 中華腎臟病雜志, 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.	呂華勝, 拉再依·巴合提, 袁騰, 等. STEMI患者PCI術后急性腎損傷的機器學習預測模型構建與驗證. 西安交通大學學報(醫學版), 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.	李澤茂, 丁靜, 馬汝航, 等. 基于人工智能的冠狀動脈周圍脂肪影像組學及衰減指數構建可解釋性機器學習模型預測冠心病患者主要心血管不良事件發生. 臨床放射學雜志, 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. 中國醫師協會急診醫師分會, 中華醫學會心血管病學分會, 中華醫學會檢驗醫學分會. 急性冠脈綜合征急診快速診療指南. 中華急診醫學雜志, 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. 王潤澤. 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. 國家慢性腎病臨床醫學研究中心, 中國醫師協會腎臟內科醫師分會, 中國急性腎損傷臨床實踐指南專家組. 中國急性腎損傷臨床實踐指南. 中華醫學雜志, 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. 陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 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. 藍潞杭, 蔣炫東, 王茂峰, 等. 隨機森林模型預測急性心肌梗死后急性腎損傷. 中華急診醫學雜志, 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. 李龍, 劉真義, 李浩然, 等. 基于隨機森林模型法的AMI患者并發AKI預測模型的建立. 重慶醫學, 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. 黃萱, 木胡牙提·烏拉斯汗, 陸晨, 等. 基于機器學習的急性冠脈綜合征患者急性腎損傷的預測模型. 臨床腎臟病雜志, 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. 葉楠, 祝闖, 徐豐博, 等. 機器學習算法構建急性心肌梗死患者發生急性腎損傷風險預測模型并與傳統模型比較. 中華腎臟病雜志, 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. 呂華勝, 拉再依·巴合提, 袁騰, 等. STEMI患者PCI術后急性腎損傷的機器學習預測模型構建與驗證. 西安交通大學學報(醫學版), 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. 李澤茂, 丁靜, 馬汝航, 等. 基于人工智能的冠狀動脈周圍脂肪影像組學及衰減指數構建可解釋性機器學習模型預測冠心病患者主要心血管不良事件發生. 臨床放射學雜志, 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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