Predictive Model for Assessing the Prognosis of Rhabdomyolysis Patients in the Intensive Care Unit

Overview

Journal Front Med (Lausanne)

Date 2025 Jan 27

PMID 39867923

Authors

Yaxin Xiong

Hongyu Shi

Jianpeng Wang

Quankuan Gu

Yu Song

Weilan Kong

Jun Lyu

Mingyan Zhao

Xianglin Meng

Affiliations

Soon will be listed here.

Abstract

Background: Rhabdomyolysis (RM) frequently gives rise to diverse complications, ultimately leading to an unfavorable prognosis for patients. Consequently, there is a pressing need for early prediction of survival rates among RM patients, yet reliable and effective predictive models are currently scarce.

Methods: All data utilized in this study were sourced from the MIMIC-IV database. A multivariable Cox regression analysis was conducted on the data, and the performance of the new model was evaluated based on the Harrell's concordance index (C-index) and the area under the receiver operating characteristic curve (AUC). Furthermore, the clinical utility of the predictive model was assessed through decision curve analysis (DCA).

Results: A total of 725 RM patients admitted to the intensive care unit (ICU) were included in the analysis, comprising 507 patients in the training cohort and 218 patients in the testing cohort. For the development of the predictive model, 37 variables were carefully selected. Multivariable Cox regression revealed that age, phosphate max, RR mean, and SOFA score were independent predictors of survival outcomes in RM patients. In the training cohort, the AUCs of the new model for predicting 28-day, 60-day, and 90-day survival rates were 0.818 (95% CI: 0.766-0.871), 0.810 (95% CI: 0.761-0.855), and 0.819 (95% CI: 0.773-0.864), respectively. In the validation cohort, the AUCs of the new model for predicting 28-day, 60-day, and 90-day survival rates were 0.840 (95% CI: 0.772-0.900), 0.842 (95% CI: 0.780-0.899), and 0.842 (95% CI: 0.779-0.897), respectively.

Conclusion: This study identified crucial demographic factors, vital signs, and laboratory parameters associated with RM patient prognosis and utilized them to develop a more accurate and convenient prognostic prediction model for assessing 28-day, 60-day, and 90-day survival rates.

Implications For Clinical Practice: This study specifically targets patients with RM admitted to ICU and presents a novel clinical prediction model that surpasses the conventional SOFA score. By integrating specific prognostic indicators tailored to RM, the model significantly enhances prediction accuracy, thereby enabling a more targeted and effective approach to managing RM patients.

References

Buist M, Bernard S, Nguyen T, Moore G, Anderson J . Association between clinically abnormal observations and subsequent in-hospital mortality: a prospective study. Resuscitation. 2004; 62(2):137-41. DOI: 10.1016/j.resuscitation.2004.03.005. View

Baxter R, Moore J . Diagnosis and treatment of acute exertional rhabdomyolysis. J Orthop Sports Phys Ther. 2003; 33(3):104-8. DOI: 10.2519/jospt.2003.33.3.104. View

Zutt R, van der Kooi A, Linthorst G, Wanders R, de Visser M . Rhabdomyolysis: review of the literature. Neuromuscul Disord. 2014; 24(8):651-9. DOI: 10.1016/j.nmd.2014.05.005. View

Hedjoudje A, Farha J, Cheurfa C, Grabar S, Weiss E, Badurdeen D . Serum phosphate is associated with mortality among patients admitted to ICU for acute pancreatitis. United European Gastroenterol J. 2021; 9(5):534-542. PMC: 8259433. DOI: 10.1002/ueg2.12059. View

Liu C, Yuan Q, Mao Z, Hu P, Wu R, Liu X . Development and validation of a model for the early prediction of the RRT requirement in patients with rhabdomyolysis. Am J Emerg Med. 2021; 46:38-44. DOI: 10.1016/j.ajem.2021.03.006. View

Han D, Xu F, Li C, Zhang L, Yang R, Zheng S . A Novel Nomogram for Predicting Survival in Patients with Severe Acute Pancreatitis: An Analysis Based on the Large MIMIC-III Clinical Database. Emerg Med Int. 2021; 2021:9190908. PMC: 8526213. DOI: 10.1155/2021/9190908. View

Zimmerman J, Shen M . Rhabdomyolysis. Chest. 2013; 144(3):1058-1065. DOI: 10.1378/chest.12-2016. View

Chatzizisis Y, Misirli G, Hatzitolios A, Giannoglou G . The syndrome of rhabdomyolysis: complications and treatment. Eur J Intern Med. 2008; 19(8):568-74. DOI: 10.1016/j.ejim.2007.06.037. View

Eddington H, Hoefield R, Sinha S, Chrysochou C, Lane B, Foley R . Serum phosphate and mortality in patients with chronic kidney disease. Clin J Am Soc Nephrol. 2010; 5(12):2251-7. PMC: 2994087. DOI: 10.2215/CJN.00810110. View

10.

Klein J, Rizzo J, Zhang M, Keiding N . Statistical methods for the analysis and presentation of the results of bone marrow transplants. Part 2: Regression modeling. Bone Marrow Transplant. 2002; 28(11):1001-11. DOI: 10.1038/sj.bmt.1703271. View

11.

Iasonos A, Schrag D, Raj G, Panageas K . How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008; 26(8):1364-70. DOI: 10.1200/JCO.2007.12.9791. View

12.

Sinert R, Kohl L, Rainone T, Scalea T . Exercise-induced rhabdomyolysis. Ann Emerg Med. 1994; 23(6):1301-6. DOI: 10.1016/s0196-0644(94)70356-6. View

13.

McMahon G, Zeng X, Waikar S . A risk prediction score for kidney failure or mortality in rhabdomyolysis. JAMA Intern Med. 2013; 173(19):1821-8. PMC: 5152583. DOI: 10.1001/jamainternmed.2013.9774. View

14.

Ferreira F, Bota D, Bross A, Melot C, Vincent J . Serial evaluation of the SOFA score to predict outcome in critically ill patients. JAMA. 2001; 286(14):1754-8. DOI: 10.1001/jama.286.14.1754. View

15.

BELLVILLE J, HOWLAND W . Prognosis after severe hypoxia in man. Anesthesiology. 1957; 18(3):389-97. DOI: 10.1097/00000542-195705000-00004. View

16.

Johnson A, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S . MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023; 10(1):1. PMC: 9810617. DOI: 10.1038/s41597-022-01899-x. View

17.

Clarkson P, Kearns A, Rouzier P, Rubin R, Thompson P . Serum creatine kinase levels and renal function measures in exertional muscle damage. Med Sci Sports Exerc. 2006; 38(4):623-7. DOI: 10.1249/01.mss.0000210192.49210.fc. View

18.

McKenna M, Kelly M, Boran G, Lavin P . Spectrum of rhabdomyolysis in an acute hospital. Ir J Med Sci. 2019; 188(4):1423-1426. DOI: 10.1007/s11845-019-01968-y. View

19.

Melli G, Chaudhry V, Cornblath D . Rhabdomyolysis: an evaluation of 475 hospitalized patients. Medicine (Baltimore). 2005; 84(6):377-385. DOI: 10.1097/01.md.0000188565.48918.41. View

20.

Mastaglia F, Needham M . Update on toxic myopathies. Curr Neurol Neurosci Rep. 2011; 12(1):54-61. DOI: 10.1007/s11910-011-0232-9. View