Prediction models for acute kidney injury following stanford type A aortic dissection surgery: a systematic review and meta-analysis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LI Wenqian ¹ , GU Yujie ² , YUE Xin ¹ , LIU Jiahui ¹ , WANG Ziwen ¹ , DONG Zhiyong ² ,  LU Hua ²

1. School of Nursing, Jinan University, Guangzhou, 510632, P. R. China;
2. The First Affiliated Hospital of Jinan University, Guangzhou, 510632, P. R. China;

Corresponding author：

LU Hua, Email: luhua2022@jnu.edu.cn

Keywords：

Stanford type A aortic dissection; acute kidney injury; prediction models; systematic review/meta-analysis

DOI：

10.7507/1007-4848.202504007

Video：

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Objective To conduct a comprehensive analysis of risk prediction models for acute kidney injury (AKI) following Stanford type A aortic dissection surgery through a systematic review. Methods A systematic search was performed in English and Chinese databases such as PubMed, EMbase, ProQuest, Web of Science, China National Knowledge Infrastructure (CNKI), VIP, Wanfang, and SinoMed to collect relevant literature published up to January 2025. Two researchers completed the literature screening and data extraction. The methodological quality of the prediction models was assessed using bias risk assessment tools, and a meta-analysis was performed using R version 4.3.1, with a focus on evaluating the predictive factors of the models. Results A total of 15 studies were included (13 retrospective cohort studies, 1 prospective cohort study, and 1 case-control study), involving 22 risk prediction models and a cumulative sample size of 4 498 patients. The overall applicability of the included studies was good, but all 15 studies exhibited a high risk of bias. The meta-analysis revealed that the area under the curve (AUC) for the predictive performance of the models was 0.834 [95%CI (0.798, 0.869)]. Further subgroup analysis indicated that the number of predictive factors was a source of heterogeneity. Additionally, hypertension [OR=2.35, 95%CI (1.55, 3.54)], serum creatinine [OR=1.01, 95%CI (1.00, 1.01)], age [OR=1.05, 95%CI (1.02, 1.09)], and white blood cell count [OR=1.14, 95%CI (1.06, 1.22)] were identified as predictors of AKI following type A aortic dissection surgery. Conclusion Currently, the predictive models for AKI after type A aortic dissection surgery demonstrate good performance. However, all included models carry a high risk of bias. It is recommended to strengthen multicenter prospective studies and external validation of the models to enhance their clinical applicability.

1.	张培华, 蒋米尔. 临床血管外科学. 2版. 北京: 科学出版社, 2007: 304-306.Zhang PH, Jiang ME. Clinical vascular surgery. 2nd ed. Beijing: Science Press, 2007: 304-306.
2.	Nienaber CA, Clough RE. Management of acute aortic dissection. Lancet, 2015, 385(9970): 800-811.
3.	Pape LA, Awais M, Woznicki EM, et al. Presentation, diagnosis, and outcomes of acute aortic dissection: 17-year trends from the International Registry of Acute Aortic Dissection. J Am Coll Cardiol, 2015, 66(4): 350-358.
4.	Turgut F, Awad AS, Abdel-Rahman EM. Acute kidney injury: medical causes and pathogenesis. J Clin Med, 2023, 12(1): 375.
5.	Hobson CE, Yavas S, Segal MS, et al. Acute kidney injury is associated with increased long-term mortality after cardiothoracic surgery. Circulation, 2009, 119(18): 2444-2453.
6.	Roh GU, Lee JW, Nam SB, et al. Incidence and risk factors of acute kidney injury after thoracic aortic surgery for acute dissection. Ann Thorac Surg, 2012, 94(3): 766-771.
7.	戴红英, 曲霖, 张扬. A型主动脉夹层术后急性肾损伤预测模型的构建与验证. 医学理论与实践, 2024, 37(7): 1097-1101.Dai HY, Qu L, Zhang Y. Development and validation of a prediction model for postoperative acute kidney injury in type A aortic dissection. J Med Pharm, 2024, 37(7): 1097-1101.
8.	潘越. Stanford A型主动脉夹层术后并发急性肾损伤的危险因素分析及风险预测模型. 华中科技大学, 2021.Pan Y. Analysis of risk factors and development of a risk prediction model for postoperative acute kidney injury in Stanford type A aortic dissection. Huazhong University of Science and Technology, 2021.
9.	Zhang C, Chen S, Yang J, et al. Postoperative nomogram and risk calculator of acute renal failure for Stanford type a aortic dissection surgery. Gen Thorac Cardiovasc Surg, 2023, 71(11): 639-647.
10.	Luo CC, Zhong YL, Qiao ZY, et al. Development and validation of a nomogram for postoperative severe acute kidney injury in acute type a aortic dissection. J Geriatr Cardiol, 2022, 19(10): 734-742.
11.	Liu X, Fang M, Wang K, et al. Machine learning-based model to predict severe acute kidney injury after total aortic arch replacement for acute type a aortic dissection. Heliyon, 2024, 10(13): e34171.
12.	Dong N, Piao H, Du Y, et al. Development of a practical prediction score for acute renal injury after surgery for Stanford type a aortic dissection. Interact Cardiovasc Thorac Surg, 2020, 30(5): 746-753.
13.	Xu F, Xie L, He J, et al. Prediction of postoperative acute kidney injury risk factors for acute type a aortic dissection patients after modified triple-branched stent graft implantation by a perioperative nomogram: a retrospective study. J Card Surg, 2023, 38(1): 3220929.
14.	Li XS, Wang ZY, Huang X, et al. Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning. Front Cardiovasc Med, 2022, 9: 984772.
15.	Fang M, Li J, Fang H, et al. Prediction of acute kidney injury after total aortic arch replacement with serum cystatin C and urine N-acetyl-β-d-glucosaminidase: a prospective observational study. Clin Chim Acta, 2023, 539: 105-113.
16.	余剑. 急性Stanford A型主动脉夹层术后急性肾损伤的危险因素研究. 南方医科大学, 2023.Yu J. Risk factors for postoperative acute kidney injury in patients with acute Stanford type A aortic dissection. Southern Medical University, 2023.
17.	李守明. 胱抑素C预测急性A型主动脉夹层患者的术后急性肾损伤. 山东大学, 2022.Li SM. Cystatin C in predicting postoperative acute kidney injury in patients with acute type A aortic dissection. Shandong University, 2022.
18.	刘光祖. 急性Stanford A型主动脉夹层术后急性肾损伤围术期危险因素分析及预测模型构建. 兰州大学, 2024.Liu GZ. Analysis of perioperative risk factors and development of a prediction model for postoperative acute kidney injury in acute Stanford type A aortic dissection. Lanzhou University, 2024.
19.	李培, 薛万腾, 赵鹏. 西安地区急性Stanford A型主动脉夹层患者的流行病学特征及术后发生急性肾损伤的影响因素. 中国医药, 2022, 17(7): 984-988.Li P, Xue WT, Zhao P. Epidemiological characteristics and influencing factors of postoperative acute kidney injury in patients with acute Stanford type A aortic dissection in Xi’an region. China Med, 2022, 17(7): 984-988.
20.	李素华, 陈思思, 黄萱, 等. 构建急性主动脉夹层患者发生急性肾损伤的临床预测模型. 现代生物医学进展, 2024, 24(14): 2613-2618,2633.Li SH, Chen SS, Huang X, et al. Development of a clinical prediction model for acute kidney injury in patients with acute aortic dissection. Prog Mod Biomed, 2024, 24(14): 2613-2618,2633.
21.	代尚太. A型主动脉夹层术后急性肾损伤的预测模型建立. 昆明理工大学, 2024.Dai ST. Establishment of a prediction model for postoperative acute kidney injury in type A aortic dissection[D]. Kunming University of Science and Technology, 2024.
22.	Moons KGM, Hooft L, Williams K, et al. Implementing systematic reviews of prognosis studies in Cochrane. Cochrane Database Syst Rev, 2018, 10(10): ED000129.
23.	Moons KGM, de Groot JA, 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.
24.	Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
25.	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 Chronic Kidney Diseases, Branch of Nephrologists of Chinese Medical Doctor Association, Expert Group of Clinical Practice Guidelines for Acute Kidney Injury in China. Clinical practice guidelines for acute kidney injury in China. Natl Med J China, 2023, 103(42): 3332-3366.
26.	Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med, 2019, 380(14): 1347-1358.
27.	Yue S, Li S, Huang X, et al. Machine learning for the prediction of acute kidney injury in patients with sepsis. J Transl Med, 2022, 20(1): 215.
28.	Lee HC, Yoon HK, Nam K, et al. Derivation and validation of machine learning approaches to predict acute kidney injury after cardiac surgery. J Clin Med, 2018, 7(10): 322.
29.	Parreco J, Soe-Lin H, Parks JJ, et al. Comparing machine learning algorithms for predicting acute kidney injury. Am Surg, 2019, 85(7): 725-729.
30.	Lei G, Wang G, Zhang C, et al. Using machine learning to predict acute kidney injury after aortic arch surgery. J Cardiothorac Vasc Anesth, 2020, 34(12): 3321-3328.
31.	Song X, Liu X, Liu F, et al. Comparison of machine learning and logistic regression models in predicting acute kidney injury: a systematic review and meta-analysis. Int J Med Inform, 2021, 151: 104484.
32.	Cutler DR, Edwards TC Jr, Beard KH, et al. Random forests for classification in ecology. Ecology, 2007, 88(11): 2783-2792.
33.	Salman HA, Kalakech A, Steiti A. Random forest algorithm overview. Deleted Journal, 2024, 1(1): 69-79.
34.	Richhariya B, Gupta D, Prasad S, et al. A review on support vector machines for classification problems. Artif Intell Syst Mach Learn, 2017, 9: 130-139.
35.	Messem AV. Support vector machines: a robust prediction method with applications in bioinformatics[M]. Handbook of Statistics, 2020.
36.	Taylor JM, Ankerst DP, Andridge RR. Validation of biomarker-based risk prediction models. Clin Cancer Res, 2008, 14(19): 5977-5983.
37.	Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart, 2012, 98(9): 683-690.
38.	MacCallum RC, Zhang SB, Preacher KJ, et al. On the practice of dichotomization of quantitative variables. Psychol Methods, 2002, 7(1): 19-40.
39.	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 studies on diagnostic or prognostic multivariable prediction models. Chin J Evid Based Med, 2020, 20(6): 737-744.
40.	Nadim MK, Forni LG, Bihorac A, et al. Cardiac and vascular surgery-associated acute kidney injury: the 20th International Consensus Conference of the ADQI (Acute Disease Quality Initiative) Group. J Am Heart Assoc, 2018, 7(11): e008834.
41.	Ostermann M, Cennamo A, Meersch M, et al. A narrative review of the impact of surgery and anaesthesia on acute kidney injury. Anaesthesia, 2020, 75(Suppl 1): e121-e133.
42.	Nah H, Lee S, Lee K, et al. Evaluation of bilirubin interference and accuracy of six creatinine assays compared with isotope dilution-liquid chromatography mass spectrometry. Clin Biochem, 2016, 49(3): 274-281.
43.	Goyal A, Maheshwari S, Abbasi HQ, et al. Development of acute kidney injury following repair of Stanford type a aortic dissection is associated with increased mortality and complications: a systematic review, meta-analysis, and meta-regression analysis. Cardiovasc Endocrinol Metab, 2024, 13(4): e00314.
44.	Zhou H, Wang G, Yang L, et al. Acute kidney injury after total arch replacement combined with frozen elephant trunk implantation: incidence, risk factors, and outcome. J Cardiothorac Vasc Anesth, 2018, 32(5): 2210-2217.
45.	Helgason D, Helgadottir S, Ahlsson A, et al. Acute kidney injury after acute repair of type a aortic dissection. Ann Thorac Surg, 2021, 111(4): 1292-1298.
46.	O’Sullivan ED, Hughes J, Ferenbach DA. Renal aging: causes and consequences. J Am Soc Nephrol, 2017, 28(2): 407-420.
47.	Muñoz-García AJ, Muñoz-García E, Jiménez-Navarro MF, et al. Clinical impact of acute kidney injury on short- and long-term outcomes after transcatheter aortic valve implantation with the CoreValve prosthesis. J Cardiol, 2015, 66(1): 46-49.
48.	余金甜, 陈俊杉, 张爱琴. 急性A型主动脉夹层术后急性肾损伤危险因素的系统评价与Meta分析. 中国胸心血管外科临床杂志, 2020, 27(1): 77-84.Yu JT, Chen JS, Zhang AQ. Systematic review and meta-analysis of risk factors for postoperative acute kidney injury in acute type A aortic dissection. Chin J Clin Thorac Cardiovasc Surg, 2020, 27(1): 77-84.
49.	Skotsimara G, Antonopoulos A, Oikonomou E, et al. Aortic wall inflammation in the pathogenesis, diagnosis and treatment of aortic aneurysms. Inflammation, 2022, 45(3): 965-976.
50.	Wu HB, Qin H, Ma WG, et al. Can renal resistive index predict acute kidney injury after acute type a aortic dissection repair?. Ann Thorac Surg, 2017, 104(5): 1583-1589.
51.	Devarajan P. Biomarkers for the early detection of acute kidney injury. Curr Opin Pediatr, 2011, 23(2): 194-200.
52.	Vacaroiu IA, Balcangiu-Stroescu AE, Șerban-Feier LF, et al. Biomarkers of acute kidney injury: a concise review of current literature. Rom J Lab Med, 2024, 32(4): 305-313.
53.	Sun H, Peng J, Cai S, et al. A translational study of Galectin-3 as an early biomarker and potential therapeutic target for ischemic-reperfusion induced acute kidney injury. J Crit Care, 2021, 65: 192-199.
54.	Wang L, Zhong GD, Lv XC, et al. Risk factors for acute kidney injury after Stanford type a aortic dissection repair surgery: a systematic review and meta-analysis. Ren Fail, 2022, 44(1): 1463-1477.

1. 张培华, 蒋米尔. 临床血管外科学. 2版. 北京: 科学出版社, 2007: 304-306.Zhang PH, Jiang ME. Clinical vascular surgery. 2nd ed. Beijing: Science Press, 2007: 304-306.
2. Nienaber CA, Clough RE. Management of acute aortic dissection. Lancet, 2015, 385(9970): 800-811.
3. Pape LA, Awais M, Woznicki EM, et al. Presentation, diagnosis, and outcomes of acute aortic dissection: 17-year trends from the International Registry of Acute Aortic Dissection. J Am Coll Cardiol, 2015, 66(4): 350-358.
4. Turgut F, Awad AS, Abdel-Rahman EM. Acute kidney injury: medical causes and pathogenesis. J Clin Med, 2023, 12(1): 375.
5. Hobson CE, Yavas S, Segal MS, et al. Acute kidney injury is associated with increased long-term mortality after cardiothoracic surgery. Circulation, 2009, 119(18): 2444-2453.
6. Roh GU, Lee JW, Nam SB, et al. Incidence and risk factors of acute kidney injury after thoracic aortic surgery for acute dissection. Ann Thorac Surg, 2012, 94(3): 766-771.
7. 戴红英, 曲霖, 张扬. A型主动脉夹层术后急性肾损伤预测模型的构建与验证. 医学理论与实践, 2024, 37(7): 1097-1101.Dai HY, Qu L, Zhang Y. Development and validation of a prediction model for postoperative acute kidney injury in type A aortic dissection. J Med Pharm, 2024, 37(7): 1097-1101.
8. 潘越. Stanford A型主动脉夹层术后并发急性肾损伤的危险因素分析及风险预测模型. 华中科技大学, 2021.Pan Y. Analysis of risk factors and development of a risk prediction model for postoperative acute kidney injury in Stanford type A aortic dissection. Huazhong University of Science and Technology, 2021.
9. Zhang C, Chen S, Yang J, et al. Postoperative nomogram and risk calculator of acute renal failure for Stanford type a aortic dissection surgery. Gen Thorac Cardiovasc Surg, 2023, 71(11): 639-647.
10. Luo CC, Zhong YL, Qiao ZY, et al. Development and validation of a nomogram for postoperative severe acute kidney injury in acute type a aortic dissection. J Geriatr Cardiol, 2022, 19(10): 734-742.
11. Liu X, Fang M, Wang K, et al. Machine learning-based model to predict severe acute kidney injury after total aortic arch replacement for acute type a aortic dissection. Heliyon, 2024, 10(13): e34171.
12. Dong N, Piao H, Du Y, et al. Development of a practical prediction score for acute renal injury after surgery for Stanford type a aortic dissection. Interact Cardiovasc Thorac Surg, 2020, 30(5): 746-753.
13. Xu F, Xie L, He J, et al. Prediction of postoperative acute kidney injury risk factors for acute type a aortic dissection patients after modified triple-branched stent graft implantation by a perioperative nomogram: a retrospective study. J Card Surg, 2023, 38(1): 3220929.
14. Li XS, Wang ZY, Huang X, et al. Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning. Front Cardiovasc Med, 2022, 9: 984772.
15. Fang M, Li J, Fang H, et al. Prediction of acute kidney injury after total aortic arch replacement with serum cystatin C and urine N-acetyl-β-d-glucosaminidase: a prospective observational study. Clin Chim Acta, 2023, 539: 105-113.
16. 余剑. 急性Stanford A型主动脉夹层术后急性肾损伤的危险因素研究. 南方医科大学, 2023.Yu J. Risk factors for postoperative acute kidney injury in patients with acute Stanford type A aortic dissection. Southern Medical University, 2023.
17. 李守明. 胱抑素C预测急性A型主动脉夹层患者的术后急性肾损伤. 山东大学, 2022.Li SM. Cystatin C in predicting postoperative acute kidney injury in patients with acute type A aortic dissection. Shandong University, 2022.
18. 刘光祖. 急性Stanford A型主动脉夹层术后急性肾损伤围术期危险因素分析及预测模型构建. 兰州大学, 2024.Liu GZ. Analysis of perioperative risk factors and development of a prediction model for postoperative acute kidney injury in acute Stanford type A aortic dissection. Lanzhou University, 2024.
19. 李培, 薛万腾, 赵鹏. 西安地区急性Stanford A型主动脉夹层患者的流行病学特征及术后发生急性肾损伤的影响因素. 中国医药, 2022, 17(7): 984-988.Li P, Xue WT, Zhao P. Epidemiological characteristics and influencing factors of postoperative acute kidney injury in patients with acute Stanford type A aortic dissection in Xi’an region. China Med, 2022, 17(7): 984-988.
20. 李素华, 陈思思, 黄萱, 等. 构建急性主动脉夹层患者发生急性肾损伤的临床预测模型. 现代生物医学进展, 2024, 24(14): 2613-2618,2633.Li SH, Chen SS, Huang X, et al. Development of a clinical prediction model for acute kidney injury in patients with acute aortic dissection. Prog Mod Biomed, 2024, 24(14): 2613-2618,2633.
21. 代尚太. A型主动脉夹层术后急性肾损伤的预测模型建立. 昆明理工大学, 2024.Dai ST. Establishment of a prediction model for postoperative acute kidney injury in type A aortic dissection[D]. Kunming University of Science and Technology, 2024.
22. Moons KGM, Hooft L, Williams K, et al. Implementing systematic reviews of prognosis studies in Cochrane. Cochrane Database Syst Rev, 2018, 10(10): ED000129.
23. Moons KGM, de Groot JA, 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.
24. Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
25. 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 Chronic Kidney Diseases, Branch of Nephrologists of Chinese Medical Doctor Association, Expert Group of Clinical Practice Guidelines for Acute Kidney Injury in China. Clinical practice guidelines for acute kidney injury in China. Natl Med J China, 2023, 103(42): 3332-3366.
26. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med, 2019, 380(14): 1347-1358.
27. Yue S, Li S, Huang X, et al. Machine learning for the prediction of acute kidney injury in patients with sepsis. J Transl Med, 2022, 20(1): 215.
28. Lee HC, Yoon HK, Nam K, et al. Derivation and validation of machine learning approaches to predict acute kidney injury after cardiac surgery. J Clin Med, 2018, 7(10): 322.
29. Parreco J, Soe-Lin H, Parks JJ, et al. Comparing machine learning algorithms for predicting acute kidney injury. Am Surg, 2019, 85(7): 725-729.
30. Lei G, Wang G, Zhang C, et al. Using machine learning to predict acute kidney injury after aortic arch surgery. J Cardiothorac Vasc Anesth, 2020, 34(12): 3321-3328.
31. Song X, Liu X, Liu F, et al. Comparison of machine learning and logistic regression models in predicting acute kidney injury: a systematic review and meta-analysis. Int J Med Inform, 2021, 151: 104484.
32. Cutler DR, Edwards TC Jr, Beard KH, et al. Random forests for classification in ecology. Ecology, 2007, 88(11): 2783-2792.
33. Salman HA, Kalakech A, Steiti A. Random forest algorithm overview. Deleted Journal, 2024, 1(1): 69-79.
34. Richhariya B, Gupta D, Prasad S, et al. A review on support vector machines for classification problems. Artif Intell Syst Mach Learn, 2017, 9: 130-139.
35. Messem AV. Support vector machines: a robust prediction method with applications in bioinformatics[M]. Handbook of Statistics, 2020.
36. Taylor JM, Ankerst DP, Andridge RR. Validation of biomarker-based risk prediction models. Clin Cancer Res, 2008, 14(19): 5977-5983.
37. Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart, 2012, 98(9): 683-690.
38. MacCallum RC, Zhang SB, Preacher KJ, et al. On the practice of dichotomization of quantitative variables. Psychol Methods, 2002, 7(1): 19-40.
39. 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 studies on diagnostic or prognostic multivariable prediction models. Chin J Evid Based Med, 2020, 20(6): 737-744.
40. Nadim MK, Forni LG, Bihorac A, et al. Cardiac and vascular surgery-associated acute kidney injury: the 20th International Consensus Conference of the ADQI (Acute Disease Quality Initiative) Group. J Am Heart Assoc, 2018, 7(11): e008834.
41. Ostermann M, Cennamo A, Meersch M, et al. A narrative review of the impact of surgery and anaesthesia on acute kidney injury. Anaesthesia, 2020, 75(Suppl 1): e121-e133.
42. Nah H, Lee S, Lee K, et al. Evaluation of bilirubin interference and accuracy of six creatinine assays compared with isotope dilution-liquid chromatography mass spectrometry. Clin Biochem, 2016, 49(3): 274-281.
43. Goyal A, Maheshwari S, Abbasi HQ, et al. Development of acute kidney injury following repair of Stanford type a aortic dissection is associated with increased mortality and complications: a systematic review, meta-analysis, and meta-regression analysis. Cardiovasc Endocrinol Metab, 2024, 13(4): e00314.
44. Zhou H, Wang G, Yang L, et al. Acute kidney injury after total arch replacement combined with frozen elephant trunk implantation: incidence, risk factors, and outcome. J Cardiothorac Vasc Anesth, 2018, 32(5): 2210-2217.
45. Helgason D, Helgadottir S, Ahlsson A, et al. Acute kidney injury after acute repair of type a aortic dissection. Ann Thorac Surg, 2021, 111(4): 1292-1298.
46. O’Sullivan ED, Hughes J, Ferenbach DA. Renal aging: causes and consequences. J Am Soc Nephrol, 2017, 28(2): 407-420.
47. Muñoz-García AJ, Muñoz-García E, Jiménez-Navarro MF, et al. Clinical impact of acute kidney injury on short- and long-term outcomes after transcatheter aortic valve implantation with the CoreValve prosthesis. J Cardiol, 2015, 66(1): 46-49.
48. 余金甜, 陈俊杉, 张爱琴. 急性A型主动脉夹层术后急性肾损伤危险因素的系统评价与Meta分析. 中国胸心血管外科临床杂志, 2020, 27(1): 77-84.Yu JT, Chen JS, Zhang AQ. Systematic review and meta-analysis of risk factors for postoperative acute kidney injury in acute type A aortic dissection. Chin J Clin Thorac Cardiovasc Surg, 2020, 27(1): 77-84.
49. Skotsimara G, Antonopoulos A, Oikonomou E, et al. Aortic wall inflammation in the pathogenesis, diagnosis and treatment of aortic aneurysms. Inflammation, 2022, 45(3): 965-976.
50. Wu HB, Qin H, Ma WG, et al. Can renal resistive index predict acute kidney injury after acute type a aortic dissection repair?. Ann Thorac Surg, 2017, 104(5): 1583-1589.
51. Devarajan P. Biomarkers for the early detection of acute kidney injury. Curr Opin Pediatr, 2011, 23(2): 194-200.
52. Vacaroiu IA, Balcangiu-Stroescu AE, Șerban-Feier LF, et al. Biomarkers of acute kidney injury: a concise review of current literature. Rom J Lab Med, 2024, 32(4): 305-313.
53. Sun H, Peng J, Cai S, et al. A translational study of Galectin-3 as an early biomarker and potential therapeutic target for ischemic-reperfusion induced acute kidney injury. J Crit Care, 2021, 65: 192-199.
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Latest ArticlesPrediction models for acute kidney injury following stanford type A aortic dissection surgery: a systematic review and meta-analysis

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