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.

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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.	戴紅英, 曲霖, 張揚. A型主動脈夾層術后急性腎損傷預測模型的構建與驗證. 醫學理論與實踐, 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型主動脈夾層術后并發急性腎損傷的危險因素分析及風險預測模型. 華中科技大學, 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.	余劍. 急性Stanford A型主動脈夾層術后急性腎損傷的危險因素研究. 南方醫科大學, 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預測急性A型主動脈夾層患者的術后急性腎損傷. 山東大學, 2022.Li SM. Cystatin C in predicting postoperative acute kidney injury in patients with acute type A aortic dissection. Shandong University, 2022.
18.	劉光祖. 急性Stanford A型主動脈夾層術后急性腎損傷圍術期危險因素分析及預測模型構建. 蘭州大學, 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, 薛萬騰, 趙鵬. 西安地區急性Stanford A型主動脈夾層患者的流行病學特征及術后發生急性腎損傷的影響因素. 中國醫藥, 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.	李素華, 陳思思, 黃萱, 等. 構建急性主動脈夾層患者發生急性腎損傷的臨床預測模型. 現代生物醫學進展, 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型主動脈夾層術后急性腎損傷的預測模型建立. 昆明理工大學, 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.	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 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.	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 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.	Mu?oz-García AJ, Mu?oz-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, 陳俊杉, 張愛琴. 急性A型主動脈夾層術后急性腎損傷危險因素的系統評價與Meta分析. 中國胸心血管外科臨床雜志, 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, ?erban-Feier LF, et al. Biomarkers of acute kidney injury: a concise review of current literature. Rom J Lab Med, 2024, 32(4): 305-313.
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1. 張培華, 蔣米爾. 臨床血管外科學. 2版. 北京: 科學出版社, 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. 戴紅英, 曲霖, 張揚. A型主動脈夾層術后急性腎損傷預測模型的構建與驗證. 醫學理論與實踐, 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型主動脈夾層術后并發急性腎損傷的危險因素分析及風險預測模型. 華中科技大學, 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. 余劍. 急性Stanford A型主動脈夾層術后急性腎損傷的危險因素研究. 南方醫科大學, 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預測急性A型主動脈夾層患者的術后急性腎損傷. 山東大學, 2022.Li SM. Cystatin C in predicting postoperative acute kidney injury in patients with acute type A aortic dissection. Shandong University, 2022.
18. 劉光祖. 急性Stanford A型主動脈夾層術后急性腎損傷圍術期危險因素分析及預測模型構建. 蘭州大學, 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, 薛萬騰, 趙鵬. 西安地區急性Stanford A型主動脈夾層患者的流行病學特征及術后發生急性腎損傷的影響因素. 中國醫藥, 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. 李素華, 陳思思, 黃萱, 等. 構建急性主動脈夾層患者發生急性腎損傷的臨床預測模型. 現代生物醫學進展, 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型主動脈夾層術后急性腎損傷的預測模型建立. 昆明理工大學, 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. 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 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. 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 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. Mu?oz-García AJ, Mu?oz-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, 陳俊杉, 張愛琴. 急性A型主動脈夾層術后急性腎損傷危險因素的系統評價與Meta分析. 中國胸心血管外科臨床雜志, 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.
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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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