EXPRESS:血清乳酸水平在脓毒症患者死亡率预测中的准确性:一项系统综述和荟萃分析。

IF 2
Keyi Li, Hangqi Zhu, Houying Liu, Wei Du, Jing Wu, Chenjin Li, Guolin Song
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引用次数: 0

摘要

目的:探讨血清乳酸(SL)在脓毒症患者死亡率预测中的准确性。方法:检索PubMed、Embase、Cochrane Library和Web of Science,检索截止日期为2025年3月27日。队列研究和病例对照研究的质量通过纽卡斯尔-渥太华量表(NOS)进行评定,横断面研究的质量根据卫生保健研究和质量机构(AHRQ)标准进行评定。我们的meta分析使用Stata 15完成。结果:34项研究共纳入46,300例败血症患者。合并分析表明,SL升高与脓毒症患者死亡率升高显著相关(当效应大小(ES)报告为危险比(HR)时:HR=1.85, 95% CI: 1.29-2.66)。结论:SL升高与脓毒症患者死亡率密切相关,特别是当阈值≥4 mmol/L时。然而,其诊断价值仍然有限。未来的研究应解决乳酸测量程序的标准化和多模态预测模型的发展,以实现从风险分层到精确干预的转变和突破。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The accuracy of serum lactate in mortality prediction in sepsis patients: A systematic review and meta-analysis.

Objective: Our study aims to assess the accuracy of serum lactate (SL) in mortality prediction among sepsis patients.

Methods: PubMed, Embase, Cochrane Library, and Web of Science were searched until March 27, 2025. The quality of cohort and case-control studies was rated via the Newcastle-Ottawa Scale (NOS), and that of cross-sectional ones was assessed per the Agency for Healthcare Research and Quality (AHRQ) criteria. Our meta-analysis was completed using Stata 15.

Results: 34 studies involving 46,300 sepsis patients were included. Pooled analyses demonstrated that elevated SL was significantly associated with rising mortality in sepsis patients (when effect size (ES) was reported as hazard ratio (HR): HR = 1.85, 95% CI: 1.29-2.66, p < 0.05; when ES was reported as odds ratio (OR): OR = 1.52, 95% CI: 1.39-1.67, p < 0.001). 17 studies evaluated the performance of SL for mortality prediction in sepsis patients, revealing the pooled sensitivity, specificity, and an area under the curve (AUC) of 0.80 (95% CI: 0.75-0.85), 0.37 (95% CI: 0.25-0.51), and 0.73 (95% CI: 0.68-0.76).

Conclusions: Elevated SL is closely associated with mortality in sepsis patients, particularly when the threshold is ≥4 mmol/L. Nevertheless, its diagnostic value remains limited. Future research should address the standardization of lactate measurement procedures and the development of multimodal prediction models for a transformation and breakthrough from risk stratification to precise intervention.

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