Kumar Ramesh, Kumar Abhishek, Kumar Sudhir
Department of Gastroenterology, All India Institute of Medical Sciences, Patna 801507, Bihar, India.
World J Crit Care Med. 2025 Jun 9;14(2):101587. doi: 10.5492/wjccm.v14.i2.101587.
Acute liver failure (ALF) and acute-on-chronic LF (ACLF) are prevalent hepatic emergencies characterized by an increased susceptibility to bacterial infections (BI), despite significant systemic inflammation. Literature indicates that 30%-80% of ALF patients and 55%-81% of ACLF patients develop BI, attributed to immunological dysregulation. Bacterial sepsis in these patients is associated with adverse clinical outcomes, including prolonged hospitalization and increased mortality. Early detection of bacterial sepsis is critical; however, distinguishing between sterile systemic inflammation and sepsis poses a significant challenge due to the overlapping clinical presentations of LF and sepsis. Conventional sepsis biomarkers, such as procalcitonin and C-reactive protein, have shown limited utility in LF patients due to inconsistent results. In contrast, novel biomarkers like presepsin and sTREM-1 have demonstrated promising discriminatory performance in this population, pending further validation. Moreover, emerging research highlights the potential of machine learning-based approaches to enhance sepsis detection and characterization. Although preliminary findings are encouraging, further studies are necessary to validate these results across diverse patient cohorts, including those with LF. This article provides a comprehensive review of the magnitude, impact, and diagnostic challenges associated with BI in LF patients, focusing on novel advancements in early sepsis detection and characterization.
急性肝衰竭(ALF)和慢加急性肝衰竭(ACLF)是常见的肝脏急症,其特征是尽管存在明显的全身炎症,但对细菌感染(BI)的易感性增加。文献表明,30%-80%的ALF患者和55%-81%的ACLF患者会发生BI,这归因于免疫失调。这些患者的细菌败血症与不良临床结局相关,包括住院时间延长和死亡率增加。早期检测细菌败血症至关重要;然而,由于肝衰竭和败血症的临床表现重叠,区分无菌性全身炎症和败血症构成了重大挑战。传统的败血症生物标志物,如降钙素原和C反应蛋白,由于结果不一致,在肝衰竭患者中的效用有限。相比之下,像可溶性髓系细胞触发受体-1(sTREM-1)和 presepsin这样的新型生物标志物在这一人群中已显示出有前景的鉴别性能,有待进一步验证。此外,新出现的研究突出了基于机器学习的方法在增强败血症检测和特征描述方面的潜力。尽管初步结果令人鼓舞,但仍需要进一步研究以在包括肝衰竭患者在内的不同患者队列中验证这些结果。本文全面综述了肝衰竭患者中与细菌感染相关的发生率、影响及诊断挑战,重点关注早期败血症检测和特征描述方面的新进展。