Development and validation of a prediction model for acute renal failure after lung transplantation_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

CHEN Sheng ¹ , PAN Chen ² , LI Shaoxiang ¹ , ZHANG Bingzheng ¹ ,  JIAO Wenjie ¹

1. Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao, 266071, Shandong, P. R. China;
2. Medical Department of Nantong University, Nantong, 226000, Jiangsu, P. R. China;

Corresponding author：

JIAO Wenjie, Email: jiaowj@qduhospital.cn

Keywords：

Lung transplantation; acute renal failure; risk factors; nomogram

DOI：

10.7507/1007-4848.202407017

Video：

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Objective To identify and analyze risk factors associated with acute renal failure (ARF) after lung transplantation (LTx) and develop a predictive model. Methods Patients for this study were obtained from the United Network for Organ Sharing (UNOS) database who underwent unilateral or bilateral lung transplantation between 2015 and 2022. Preoperative and postoperative clinical characteristics of the patients were analyzed. A combined approach using random forest and Lasso regression was employed to identify key preoperative factors associated with the incidence of ARF following lung transplantation. Random forest was used to assess the importance of each feature variable, while Lasso regression further filtered the variables contributing most significantly to the model. The predictive performance of the constructed model was evaluated in both training and validation sets, with ROC curves and AUC values used to verify and compare model effectiveness. Results A total of 15 110 LTx patients were included in the study, comprising 6 041 males and 9 069 females, with a median age of 64 years. Findings indicated that preoperative lung diagnosis, estimated glomerular filtration rate (eGFR), mechanical ventilation, ICU admission prior to transplantation, extracorporeal membrane oxygenation (ECMO) support, infection within two weeks before transplantation, Karnofsky Performance Status (KPS) score, donor age, waitlist duration, double-lung transplantation, and ischemia time showed statistically significant differences between groups (P<0.05). Model evaluation results demonstrated that the constructed predictive model achieved high accuracy in both the training and validation sets, with favorable AUC values, confirming its validity and reliability. Conclusion This study discusses common risk factors for ARF following lung transplantation and introduces an effective predictive model with potential clinical application.

1.	Mei J, Hu J, Krause EM, et al. The efficacy of pulmonary rehabilitation training program for patients after lung transplantation. J Thorac Dis, 2024, 16(1): 530-541.
2.	Miggins JJ, Reul RM, Barrett S, et al. Twenty-year survival following lung transplantation. J Thorac Dis, 2023, 15(6): 2997-3012.
3.	Hoang-Thi TN, Chassagnon G, Hua-Huy T, et al. Chronic lung allograft dysfunction post lung transplantation: A review of computed tomography quantitative methods for detection and follow-up. J Clin Med, 2021, 10(8): 1608.
4.	Bennett D, Fossi A, Marchetti L, et al. Postoperative acute kidney injury in lung transplant recipients. Interact Cardiovasc Thorac Surg, 2019, 28(6): 929-935.
5.	Jing L, Chen W, Guo L, et al. Acute kidney injury after lung transplantation: A narrative review. Ann Transl Med, 2021, 9(8): 717.
6.	Doricic J, Greite R, Vijayan V, et al. Kidney injury after lung transplantation: Long-term mortality predicted by post-operative day-7 serum creatinine and few clinical factors. PLoS One, 2022, 17(3): e0265002.
7.	Jiang GP, Liao YJ, Huang LL, et al. Effects and molecular mechanism of pachymic acid on ferroptosis in renal ischemia reperfusion injury. Mol Med Rep, 2021, 23(1): 63.
8.	Toyoda T, Thomae BL, Kandula V, et al. Primary graft dysfunction grade correlates with acute kidney injury stage after lung transplantation. J Thorac Dis, 2023, 15(7): 3751-3763.
9.	Fidalgo P, Ahmed M, Meyer SR, et al. Association between transient acute kidney injury and morbidity and mortality after lung transplantation: A retrospective cohort study. J Crit Care, 2014, 29(6): 1028-1034.
10.	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.
11.	Hamamoto R, Takasawa K, Machino H, et al. Application of non-negative matrix factorization in oncology: One approach for establishing precision medicine. Brief Bioinform, 2022, 23(4): bbac246.
12.	Li SM, Ren MY, Gan J, et al. Machine learning to determine risk factors for myopia progression in primary school children: The Anyang childhood eye study. Ophthalmol Ther, 2022, 11(2): 573-585.
13.	Shi Y, Cheng Z, Jian W, et al. Machine learning-based analysis of risk factors for chronic total occlusion in an Asian population. J Int Med Res, 2023, 51(10): 3000605231202141.
14.	Stafford IS, Gosink MM, Mossotto E, et al. A systematic review of artificial intelligence and machine learning applications to inflammatory bowel disease, with practical guidelines for interpretation. Inflamm Bowel Dis, 2022, 28(10): 1573-1583.
15.	Jiang L, Xia Z, Zhu R, et al. Diabetes risk prediction model based on community follow-up data using machine learning. Prev Med Rep, 2023, 35: 102358.
16.	Egan TM, Haithcock BE, Lobo J, et al. Donation after circulatory death donors in lung transplantation. J Thorac Dis, 2021, 13(11): 6536-6549.
17.	Li S, Chen S, Wang Z, et al. Risk factors and prognostic modeling in bridging extracorporeal membrane oxygenation patients before lung transplantation. J Thorac Dis, 2024, 16(8): 5238-5247.
18.	Yin ZY, Li BF, Zhou FN, et al. Risk factors of acute kidney injury after orthotopic liver transplantation in China. Sci Rep. 2017, 7: 41555.
19.	Ojo AO. Renal disease in recipients of nonrenal solid organ transplantation. Semin Nephrol, 2007, 27(4): 498-507.
20.	Luo N, Meng Q, Feng P, et al. China can be self-sufficient in maize production by 2030 with optimal crop management. Nat Commun, 2023, 14(1): 2637.
21.	Siddiqi N, McBride MA, Hariharan S. Similar risk profiles for post-transplant renal dysfunction and long-term graft failure: UNOS/OPTN database analysis. Kidney Int, 2004, 65(5): 1906-1913.
22.	Grimm JC, Lui C, Kilic A, et al. A risk score to predict acute renal failure in adult patients after lung transplantation. Ann Thorac Surg, 2015, 99(1): 251-257.
23.	Atchade E, Barour S, Tran-Dinh A, et al. Acute kidney injury after lung transplantation: Perioperative risk factors and outcome. Transplant Proc, 2020, 52(3): 967-976.
24.	Sang L, Chen S, Nong L, et al. The prevalence, risk factors, and prognosis of acute kidney injury after lung transplantation: A single-center cohort study in China. Transplant Proc, 2021, 53(2): 686-691.
25.	Lertjitbanjong P, Thongprayoon C, Cheungpasitporn W, et al. Acute kidney injury after lung transplantation: A systematic review and meta-analysis. J Clin Med, 2019, 8(10): 1713.
26.	Chaudhry R, Wanderer JP, Mubashir T, et al. Incidence and predictive factors of acute kidney injury after off-pump lung transplantation. J Cardiothorac Vasc Anesth, 2022, 36(1): 93-99.
27.	Savage N, Wayne S, Doi A, et al. Disclosing all complications of lung transplantation on ECMO. J Heart Lung Transplant, 2023, 42(7): 1002-1003.
28.	Liao X, Cheng Z, Wang L, et al. Analysis of the risk factors of acute kidney injury in patients receiving extracorporeal membrane oxygenation. Clin Nephrol, 2018, 90(4): 270-275.
29.	Sandrey MA. Special physical examination tests for superior labrum anterior-posterior shoulder tears: An examination of clinical usefulness. J Athl Train, 2013, 48(6): 856-858.
30.	Robinson PD, Shroff RC, Spencer H. Renal complications following lung and heart-lung transplantation. Pediatr Nephrol, 2013, 28(3): 375-386.
31.	Song R, Li Q, Hu J, et al. A mouse model of exertional heatstroke-related acute kidney injury. Ann Transl Med, 2022, 10(6): 276.
32.	Carillo C, Pecoraro Y, Anile M, et al. Evaluation of renal function in patients undergoing lung transplantation. Transplant Proc, 2017, 49(4): 699-701.
33.	Kallonen J, Corbascio M, Rådegran G, et al. Quality of life and functional status after pulmonary endarterectomy for chronic thromboembolic pulmonary hypertension: A Swedish single-center study. Pulm Circ, 2023, 13(2): e12219.
34.	Cheng JX, Liu BL, Zhang X, et al. Health-related quality of life in glioma patients in China. BMC Cancer, 2010, 10: 305.

1. Mei J, Hu J, Krause EM, et al. The efficacy of pulmonary rehabilitation training program for patients after lung transplantation. J Thorac Dis, 2024, 16(1): 530-541.
2. Miggins JJ, Reul RM, Barrett S, et al. Twenty-year survival following lung transplantation. J Thorac Dis, 2023, 15(6): 2997-3012.
3. Hoang-Thi TN, Chassagnon G, Hua-Huy T, et al. Chronic lung allograft dysfunction post lung transplantation: A review of computed tomography quantitative methods for detection and follow-up. J Clin Med, 2021, 10(8): 1608.
4. Bennett D, Fossi A, Marchetti L, et al. Postoperative acute kidney injury in lung transplant recipients. Interact Cardiovasc Thorac Surg, 2019, 28(6): 929-935.
5. Jing L, Chen W, Guo L, et al. Acute kidney injury after lung transplantation: A narrative review. Ann Transl Med, 2021, 9(8): 717.
6. Doricic J, Greite R, Vijayan V, et al. Kidney injury after lung transplantation: Long-term mortality predicted by post-operative day-7 serum creatinine and few clinical factors. PLoS One, 2022, 17(3): e0265002.
7. Jiang GP, Liao YJ, Huang LL, et al. Effects and molecular mechanism of pachymic acid on ferroptosis in renal ischemia reperfusion injury. Mol Med Rep, 2021, 23(1): 63.
8. Toyoda T, Thomae BL, Kandula V, et al. Primary graft dysfunction grade correlates with acute kidney injury stage after lung transplantation. J Thorac Dis, 2023, 15(7): 3751-3763.
9. Fidalgo P, Ahmed M, Meyer SR, et al. Association between transient acute kidney injury and morbidity and mortality after lung transplantation: A retrospective cohort study. J Crit Care, 2014, 29(6): 1028-1034.
10. 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.
11. Hamamoto R, Takasawa K, Machino H, et al. Application of non-negative matrix factorization in oncology: One approach for establishing precision medicine. Brief Bioinform, 2022, 23(4): bbac246.
12. Li SM, Ren MY, Gan J, et al. Machine learning to determine risk factors for myopia progression in primary school children: The Anyang childhood eye study. Ophthalmol Ther, 2022, 11(2): 573-585.
13. Shi Y, Cheng Z, Jian W, et al. Machine learning-based analysis of risk factors for chronic total occlusion in an Asian population. J Int Med Res, 2023, 51(10): 3000605231202141.
14. Stafford IS, Gosink MM, Mossotto E, et al. A systematic review of artificial intelligence and machine learning applications to inflammatory bowel disease, with practical guidelines for interpretation. Inflamm Bowel Dis, 2022, 28(10): 1573-1583.
15. Jiang L, Xia Z, Zhu R, et al. Diabetes risk prediction model based on community follow-up data using machine learning. Prev Med Rep, 2023, 35: 102358.
16. Egan TM, Haithcock BE, Lobo J, et al. Donation after circulatory death donors in lung transplantation. J Thorac Dis, 2021, 13(11): 6536-6549.
17. Li S, Chen S, Wang Z, et al. Risk factors and prognostic modeling in bridging extracorporeal membrane oxygenation patients before lung transplantation. J Thorac Dis, 2024, 16(8): 5238-5247.
18. Yin ZY, Li BF, Zhou FN, et al. Risk factors of acute kidney injury after orthotopic liver transplantation in China. Sci Rep. 2017, 7: 41555.
19. Ojo AO. Renal disease in recipients of nonrenal solid organ transplantation. Semin Nephrol, 2007, 27(4): 498-507.
20. Luo N, Meng Q, Feng P, et al. China can be self-sufficient in maize production by 2030 with optimal crop management. Nat Commun, 2023, 14(1): 2637.
21. Siddiqi N, McBride MA, Hariharan S. Similar risk profiles for post-transplant renal dysfunction and long-term graft failure: UNOS/OPTN database analysis. Kidney Int, 2004, 65(5): 1906-1913.
22. Grimm JC, Lui C, Kilic A, et al. A risk score to predict acute renal failure in adult patients after lung transplantation. Ann Thorac Surg, 2015, 99(1): 251-257.
23. Atchade E, Barour S, Tran-Dinh A, et al. Acute kidney injury after lung transplantation: Perioperative risk factors and outcome. Transplant Proc, 2020, 52(3): 967-976.
24. Sang L, Chen S, Nong L, et al. The prevalence, risk factors, and prognosis of acute kidney injury after lung transplantation: A single-center cohort study in China. Transplant Proc, 2021, 53(2): 686-691.
25. Lertjitbanjong P, Thongprayoon C, Cheungpasitporn W, et al. Acute kidney injury after lung transplantation: A systematic review and meta-analysis. J Clin Med, 2019, 8(10): 1713.
26. Chaudhry R, Wanderer JP, Mubashir T, et al. Incidence and predictive factors of acute kidney injury after off-pump lung transplantation. J Cardiothorac Vasc Anesth, 2022, 36(1): 93-99.
27. Savage N, Wayne S, Doi A, et al. Disclosing all complications of lung transplantation on ECMO. J Heart Lung Transplant, 2023, 42(7): 1002-1003.
28. Liao X, Cheng Z, Wang L, et al. Analysis of the risk factors of acute kidney injury in patients receiving extracorporeal membrane oxygenation. Clin Nephrol, 2018, 90(4): 270-275.
29. Sandrey MA. Special physical examination tests for superior labrum anterior-posterior shoulder tears: An examination of clinical usefulness. J Athl Train, 2013, 48(6): 856-858.
30. Robinson PD, Shroff RC, Spencer H. Renal complications following lung and heart-lung transplantation. Pediatr Nephrol, 2013, 28(3): 375-386.
31. Song R, Li Q, Hu J, et al. A mouse model of exertional heatstroke-related acute kidney injury. Ann Transl Med, 2022, 10(6): 276.
32. Carillo C, Pecoraro Y, Anile M, et al. Evaluation of renal function in patients undergoing lung transplantation. Transplant Proc, 2017, 49(4): 699-701.
33. Kallonen J, Corbascio M, Rådegran G, et al. Quality of life and functional status after pulmonary endarterectomy for chronic thromboembolic pulmonary hypertension: A Swedish single-center study. Pulm Circ, 2023, 13(2): e12219.
34. Cheng JX, Liu BL, Zhang X, et al. Health-related quality of life in glioma patients in China. BMC Cancer, 2010, 10: 305.

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