Prognostic analysis of sepsis-related liver injury and development of a prediction model based on machine learning method

ZHAO Yun; JIANG Wei; ZHENG Ruiqiang; YU Jiangquan

doi:10.7619/jcmp.20244867

Journal of Clinical Medicine in Practice > 2025 > 29(7): 32-37, 42. > DOI: 10.7619/jcmp.20244867

ZHAO Yun, JIANG Wei, ZHENG Ruiqiang, YU Jiangquan. Prognostic analysis of sepsis-related liver injury and development of a prediction model based on machine learning method[J]. Journal of Clinical Medicine in Practice, 2025, 29(7): 32-37, 42. DOI: 10.7619/jcmp.20244867

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Prognostic analysis of sepsis-related liver injury and development of a prediction model based on machine learning method

1.
School of Medicine of Yangzhou University, Yangzhou, Jiangsu, 225200
2.
Department of Critical Care Medicine, Taizhou Hospital of Traditional Chinese Medicine, Taizhou, Jiangsu, 225300
3.
Department of Critical Care Medicine, the Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, Jiangsu, 225200

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Received Date: October 15, 2024
Revised Date: December 23, 2024

Graphical Abstract

Abstract

Abstract

Objective
To analyze the prognosis of patients with sepsis-related liver injury (SRLI) and establish a prediction model for the occurrence of SRLI after ICU admission in sepsis patients using eight machine learning methods.
Methods
Patients who met the sepsis diagnostic criteria and had no underlying liver or biliary diseases were included from the MIMIC-IV database, and were classified into SRLI and non-SRLI groups based on liver enzymes ≥5 times the upper limit of normal (ULN) or bilirubin ≥2.0mg/dL. Chi-square test, multivariate Logistic regression analysis, and propensity score matching were used to analyze the mortality risk between the two groups. Eight machine learning algorithms[Logistic regression, classification and regression tree (CART), random forest (RF), support vector machine (SVM), K-nearest neighbors (K-NN), naive Bayes method (NBM), extreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT)]were employed to construct and validate the SRLI prediction model.
Results
The chi-square test (P < 0.001), multivariate Logistic regression analysis (P < 0.05), and log-rank test after propensity score matching (P < 0.05) all indicated that SRLI increased the mortality risk of patients. Among the SRLI prediction models, RF algorithm had the highest area under the curve (AUC), with its value of 0.866, followed by GBDT (AUC=0.862), Logistic regression (AUC=0.859), SVM (AUC=0.837), NBM (AUC=0.830), CART (AUC=0.771), XGBoost (AUC=0.764), and K-NN (AUC=0.722).
Conclusion
SRLI increases the mortality risk of patients. The prediction model constructed using the RF algorithm has high diagnostic value.

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References (18)

References

[1]	EVANS L, RHODES A, ALHAZZANI W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021[J]. Intensive Care Med, 2021, 47(11): 1181-1247. doi: 10.1007/s00134-021-06506-y
[2]	邢冬梅, 隋冰冰, 王磊. 老年脓毒症患者住院期间死亡风险预测模型的建立与验证[J]. 实用临床医药杂志, 2024, 28(8): 39-44. doi: 10.7619/jcmp.20233722
[3]	DHAINAUT J F, MARIN N, MIGNON A, et al. Hepatic response to sepsis: interaction between coagulation and inflammatory processes[J]. Crit Care Med, 2001, 29(7 Suppl): S42-S47.
[4]	PEREZ RUIZ DE GARIBAY A, KORTGEN A, LEONHARDT J, et al. Critical care hepatology: definitions, incidence, prognosis and role of liver failure in critically ill patients[J]. Crit Care, 2022, 26(1): 289. doi: 10.1186/s13054-022-04163-1
[5]	VAN DEN BROECKE A, VAN COILE L, DECRUYENAERE A, et al. Epidemiology, causes, evolution and outcome in a single-center cohort of 1116 critically ill patients with hypoxic hepatitis[J]. Ann Intensive Care, 2018, 8(1): 15. doi: 10.1186/s13613-018-0356-z
[6]	KOBASHI H, TOSHIMORI J, YAMAMOTO K. Sepsis-associated liver injury: incidence, classification and the clinical significance[J]. Hepatol Res, 2013, 43(3): 255-266. doi: 10.1111/j.1872-034X.2012.01069.x
[7]	LEVY M M, EVANS L E, RHODES A. The surviving sepsis campaign bundle: 2018 update[J]. Intensive Care Med, 2018, 44(6): 925-928. doi: 10.1007/s00134-018-5085-0
[8]	SEYMOUR C W, LIU V X, IWASHYNA T J, et al. Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (sepsis-3)[J]. JAMA, 2016, 315(8): 762-774. doi: 10.1001/jama.2016.0288
[9]	HU W H, CHEN H, MA C C, et al. Identification of indications for albumin administration in septic patients with liver cirrhosis[J]. Crit Care, 2023, 27(1): 300. doi: 10.1186/s13054-023-04587-3
[10]	WANG D W, YIN Y M, YAO Y M. Advances in sepsis-associated liver dysfunction[J]. Burns Trauma, 2014, 2(3): 97-105. doi: 10.4103/2321-3868.132689
[11]	HORVATITS T, DROLZ A, TRAUNER M, et al. Liver injury and failure in critical illness[J]. Hepatology, 2019, 70(6): 2204-2215. doi: 10.1002/hep.30824
[12]	JONSDOTTIR S, ARNARDOTTIR M B, ANDRESSON J A, et al. Prevalence, clinical characteristics and outcomes of hypoxic hepatitis in critically ill patients[J]. Scand J Gastroenterol, 2022, 57(3): 311-318. doi: 10.1080/00365521.2021.2005136
[13]	孙维维, 黄晓英, 王亚东. HELENICC评分预测脓毒症相关急性肾损伤行持续肾脏替代治疗患者早期病死率的价值[J]. 实用临床医药杂志, 2023, 27(15): 29-34. doi: 10.7619/jcmp.20231689
[14]	WOZNICA E A, INGLOT M, WOZNICA R K, et al. Liver dysfunction in sepsis[J]. Adv Clin Exp Med, 2018, 27(4): 547-551. doi: 10.17219/acem/68363
[15]	GINÈS P, SCHRIER R W. Renal failure in cirrhosis[J]. N Engl J Med, 2009, 361(13): 1279-1290. doi: 10.1056/NEJMra0809139
[16]	XIE T H, XIN Q, CAO X R, et al. Clinical characteristics and construction of a predictive model for patients with sepsis related liver injury[J]. Clin Chim Acta, 2022, 537: 80-86. doi: 10.1016/j.cca.2022.10.004
[17]	DAI J M, GUO W N, TAN Y Z, et al. Wogonin alleviates liver injury in sepsis through Nrf2-mediated NF-κB signalling suppression[J]. J Cell Mol Med, 2021, 25(12): 5782-5798. doi: 10.1111/jcmm.16604
[18]	HUANG H, TOHME S, AL-KHAFAJI A B, et al. Damage-associated molecular pattern-activated neutrophil extracellular trap exacerbates sterile inflammatory liver injury[J]. Hepatology, 2015, 62(2): 600-614. doi: 10.1002/hep.27841

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Prognostic analysis of sepsis-related liver injury and development of a prediction model based on machine learning method

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