Development and validation of prediction models for death in patients with rhabdomyolysis-induced acute kidney injury treated with continuous renal replacement therapy_West China Medical Journal

Authors：

LI Xiayin ¹ , XING Yan ^1,2 , ZHAO Jin ¹ , BAI Ming ¹ , ZHAO Lijuan ¹ , YU Yan ¹ , ZHOU Meilan ¹ ,  SUN Shiren ¹

1. Department of Nephrology, the First Affiliated Hospital of Air Force Medical University, Xi’an, Shaanxi 710032, P. R. China;
2. The Outpatient Clinic, the 95026 Hospital of PLA, Foshan, Guangdong 528000, P. R. China;

Corresponding author：

SUN Shiren, Email: sunshiren@medmail.com.cn

Keywords：

Rhabdomyolysis; acute kidney injury; continuous renal replacement therapy; risk factors; prognosis

DOI：

10.7507/1002-0179.202406092

Video：

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Objective To identify risk factors for death in patients with rhabdomyolysis-induced acute kidney injury (RI-AKI) treated with continuous renal replacement therapy (CRRT), then to develop and validate the efficacy of prediction models based on these risk factors. Methods Clinical data and prognostic information of patients with RI-AKI requiring CRRT from 2008 to 2019 were extracted from the MIMIC-IV 2.2 database. The enrolled patients were divided into a training set and a test set at a ratio of 7∶3. LASSO regression, random forest (RF) and extreme gradient boosting (XGBoost) were used to identify the risk factors affecting patients’ 28-day survival in the training set, then to develop logistic model, RF model, support vector machine (SVM) model and XGBoost model. The accuracy of above prediction models and the area under the receiver operating characteristic curve (AUC) were calculated in the test set. Results A total of 175 patients were included. Lactic acid, age, Acute Physiology Score Ⅲ, hemoglobin, mean arterial pressure and body mass index measured at intensive care unit admission were identified as the six risk factors affecting 28-day survival of enrolled patients by LASSO regression, RF and XGBoost. The accuracy of the logistic model, RF model, SVM model and XGBoost model in the test set was 0.75, 0.79, 0.79 and 0.81, with the AUC of 0.82, 0.85, 0.87 and 0.87, respectively. Conclusion The XGBoost model, incorporating six risk factors including lactic acid, age, Acute Physiology Score Ⅲ, hemoglobin, mean arterial pressure, and body mass index assessed at the time of admission to the intensive care unit, demonstrates superior clinical predictive performance, thereby enhancing the clinical decision-making process for healthcare professionals.

Citation： LI Xiayin, XING Yan, ZHAO Jin, BAI Ming, ZHAO Lijuan, YU Yan, ZHOU Meilan, SUN Shiren. Development and validation of prediction models for death in patients with rhabdomyolysis-induced acute kidney injury treated with continuous renal replacement therapy. West China Medical Journal, 2024, 39(7): 1041-1047. doi: 10.7507/1002-0179.202406092 Copy

1.	Hebert JF, Burfeind KG, Malinoski D, et al. Molecular mechanisms of rhabdomyolysis-induced kidney injury: from bench to bedside. Kidney Int Rep, 2022, 8(1): 17-29.
2.	Simpson JP, Taylor A, Sudhan N, et al. Rhabdomyolysis and acute kidney injury: creatine kinase as a prognostic marker and validation of the McMahon Score in a 10-year cohort: a retrospective observational evaluation. Eur J Anaesthesiol, 2016, 33(12): 906-912.
3.	Candela N, Silva S, Georges B, et al. Short- and long-term renal outcomes following severe rhabdomyolysis: a French multicenter retrospective study of 387 patients. Ann Intensive Care, 2020, 10(1): 27.
4.	Michelsen J, Cordtz J, Liboriussen L, et al. Prevention of rhabdomyolysis-induced acute kidney injury. A DASAIM/DSIT clinical practice guideline. Acta Anaesthesiol Scand, 2019, 63(5): 576-586.
5.	Sawhney JS, Kasotakis G, Goldenberg A, et al. Management of rhabdomyolysis: a practice management guideline from the Eastern Association for the Surgery of Trauma. Am J Surg, 2022, 224(1 Pt A): 196-204.
6.	Kodadek L, Carmichael Ii SP, Seshadri A, et al. Rhabdomyolysis: an American Association for the Surgery of Trauma Critical Care Committee Clinical Consensus Document. Trauma Surg Acute Care Open, 2022, 7(1): e000836.
7.	Amyot SL, Leblanc M, Thibeault Y, et al. Myoglobin clearance and removal during continuous venovenous hemofiltration. Intensive Care Med, 1999, 25(10): 1169-1172.
8.	De Rosa S, Villa G, Inaba K, et al. Acute renal replacement therapy in patients with major extremity injuries. Minerva Anestesiol, 2018, 84(6): 747-755.
9.	Zeng X, Zhang L, Wu T, et al. Continuous renal replacement therapy (CRRT) for rhabdomyolysis. Cochrane Database Syst Rev, 2014, 2014(6): CD008566.
10.	Kellum JA, Lameire N, KDIGO AKI Guideline Work Group. Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1). Crit Care, 2013, 17(1): 204.
11.	Liu ZZ, Mathia S, Pahlitzsch T, et al. Myoglobin facilitates angiotensin II-induced constriction of renal afferent arterioles. Am J Physiol Renal Physiol, 2017, 312(5): F908-F916.
12.	Long B, Koyfman A, Gottlieb M. An evidence-based narrative review of the emergency department evaluation and management of rhabdomyolysis. Am J Emerg Med, 2019, 37(3): 518-523.
13.	Chatzizisis YS, Misirli G, Hatzitolios AI, et al. The syndrome of rhabdomyolysis: complications and treatment. Eur J Intern Med, 2008, 19(8): 568-574.
14.	Panizo N, Rubio-Navarro A, Amaro-Villalobos JM, et al. Molecular mechanisms and novel therapeutic approaches to rhabdomyolysis-induced acute kidney injury. Kidney Blood Press Res, 2015, 40(5): 520-532.
15.	Grivei A, Giuliani KTK, Wang X, et al. Oxidative stress and inflammasome activation in human rhabdomyolysis-induced acute kidney injury. Free Radic Biol Med, 2020, 160: 690-695.
16.	Pérez-Fernández X, Sabater-Riera J, Sileanu FE, et al. Clinical variables associated with poor outcome from sepsis-associated acute kidney injury and the relationship with timing of initiation of renal replacement therapy. J Crit Care, 2017, 40: 154-160.
17.	O’Sullivan ED, Hughes J, Ferenbach DA. Renal aging: causes and consequences. J Am Soc Nephrol, 2017, 28(2): 407-420.
18.	Lim SY, Choi WI, Jeon K, et al. Body mass index and mortality in Korean intensive care units: a prospective multicenter cohort study. PLoS One, 2014, 9(4): e90039.
19.	Schuurmans J, van Rossem BTB, Rellum SR, et al. Hypotension during intensive care stay and mortality and morbidity: a systematic review and meta-analysis. Intensive Care Med, 2024, 50(4): 516-525.
20.	Hou N, Li M, He L, et al. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med, 2020, 18(1): 462.
21.	Liu J, Wu J, Liu S, et al. Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model. PLoS One, 2021, 16(2): e0246306.
22.	Yu Y, Tran H. An XGBoost-based fitted Q iteration for finding the optimal STI strategies for HIV patients. IEEE Trans Neural Netw Learn Syst, 2022: PP.

1. Hebert JF, Burfeind KG, Malinoski D, et al. Molecular mechanisms of rhabdomyolysis-induced kidney injury: from bench to bedside. Kidney Int Rep, 2022, 8(1): 17-29.
2. Simpson JP, Taylor A, Sudhan N, et al. Rhabdomyolysis and acute kidney injury: creatine kinase as a prognostic marker and validation of the McMahon Score in a 10-year cohort: a retrospective observational evaluation. Eur J Anaesthesiol, 2016, 33(12): 906-912.
3. Candela N, Silva S, Georges B, et al. Short- and long-term renal outcomes following severe rhabdomyolysis: a French multicenter retrospective study of 387 patients. Ann Intensive Care, 2020, 10(1): 27.
4. Michelsen J, Cordtz J, Liboriussen L, et al. Prevention of rhabdomyolysis-induced acute kidney injury. A DASAIM/DSIT clinical practice guideline. Acta Anaesthesiol Scand, 2019, 63(5): 576-586.
5. Sawhney JS, Kasotakis G, Goldenberg A, et al. Management of rhabdomyolysis: a practice management guideline from the Eastern Association for the Surgery of Trauma. Am J Surg, 2022, 224(1 Pt A): 196-204.
6. Kodadek L, Carmichael Ii SP, Seshadri A, et al. Rhabdomyolysis: an American Association for the Surgery of Trauma Critical Care Committee Clinical Consensus Document. Trauma Surg Acute Care Open, 2022, 7(1): e000836.
7. Amyot SL, Leblanc M, Thibeault Y, et al. Myoglobin clearance and removal during continuous venovenous hemofiltration. Intensive Care Med, 1999, 25(10): 1169-1172.
8. De Rosa S, Villa G, Inaba K, et al. Acute renal replacement therapy in patients with major extremity injuries. Minerva Anestesiol, 2018, 84(6): 747-755.
9. Zeng X, Zhang L, Wu T, et al. Continuous renal replacement therapy (CRRT) for rhabdomyolysis. Cochrane Database Syst Rev, 2014, 2014(6): CD008566.
10. Kellum JA, Lameire N, KDIGO AKI Guideline Work Group. Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1). Crit Care, 2013, 17(1): 204.
11. Liu ZZ, Mathia S, Pahlitzsch T, et al. Myoglobin facilitates angiotensin II-induced constriction of renal afferent arterioles. Am J Physiol Renal Physiol, 2017, 312(5): F908-F916.
12. Long B, Koyfman A, Gottlieb M. An evidence-based narrative review of the emergency department evaluation and management of rhabdomyolysis. Am J Emerg Med, 2019, 37(3): 518-523.
13. Chatzizisis YS, Misirli G, Hatzitolios AI, et al. The syndrome of rhabdomyolysis: complications and treatment. Eur J Intern Med, 2008, 19(8): 568-574.
14. Panizo N, Rubio-Navarro A, Amaro-Villalobos JM, et al. Molecular mechanisms and novel therapeutic approaches to rhabdomyolysis-induced acute kidney injury. Kidney Blood Press Res, 2015, 40(5): 520-532.
15. Grivei A, Giuliani KTK, Wang X, et al. Oxidative stress and inflammasome activation in human rhabdomyolysis-induced acute kidney injury. Free Radic Biol Med, 2020, 160: 690-695.
16. Pérez-Fernández X, Sabater-Riera J, Sileanu FE, et al. Clinical variables associated with poor outcome from sepsis-associated acute kidney injury and the relationship with timing of initiation of renal replacement therapy. J Crit Care, 2017, 40: 154-160.
17. O’Sullivan ED, Hughes J, Ferenbach DA. Renal aging: causes and consequences. J Am Soc Nephrol, 2017, 28(2): 407-420.
18. Lim SY, Choi WI, Jeon K, et al. Body mass index and mortality in Korean intensive care units: a prospective multicenter cohort study. PLoS One, 2014, 9(4): e90039.
19. Schuurmans J, van Rossem BTB, Rellum SR, et al. Hypotension during intensive care stay and mortality and morbidity: a systematic review and meta-analysis. Intensive Care Med, 2024, 50(4): 516-525.
20. Hou N, Li M, He L, et al. Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med, 2020, 18(1): 462.
21. Liu J, Wu J, Liu S, et al. Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model. PLoS One, 2021, 16(2): e0246306.
22. Yu Y, Tran H. An XGBoost-based fitted Q iteration for finding the optimal STI strategies for HIV patients. IEEE Trans Neural Netw Learn Syst, 2022: PP.

West China Medical Journal

Development and validation of prediction models for death in patients with rhabdomyolysis-induced acute kidney injury treated with continuous renal replacement therapy

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