Evidence map›Paper›PMID 41652564›Full record

ArticleBMC infectious diseases2026

Evaluating the predictive power of lactate-to-albumin ratio on 30-day mortality among sepsis patients: a retrospective cohort study.

Xiaona An, Yuzhu Zhang, Lu Zhang, Hui Li, Nianhai Feng, Xin Wang, Xiquan Zhang

Abstract read
In one paragraph

Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xiaona An *Department of Anesthesiology, Zibo Central Hospital, Zibo, Shandong, 255000, PR China.
Yuzhu Zhang *Department of Anesthesiology, Zibo Central Hospital, Zibo, Shandong, 255000, PR China.
Lu ZhangDepartment of Anesthesiology, Zibo Central Hospital, Zibo, Shandong, 255000, PR China.
Hui LiDepartment of Anesthesiology, Zibo Central Hospital, Zibo, Shandong, 255000, PR China.
Nianhai FengDepartment of Anesthesiology, Zibo Central Hospital, Zibo, Shandong, 255000, PR China.
Xin WangDepartment of Interventional Vascular, Zibo 148 Hospital, China RongTong Medical Healthcare Group Co. Ltd., Zibo, Shandong, 255000, PR China. xinwang89110@163.com.
Xiquan ZhangDepartment of Interventional Vascular, Zibo 148 Hospital, China RongTong Medical Healthcare Group Co. Ltd., Zibo, Shandong, 255000, PR China. zyfbb19901024@sina.com.

Funding

the Medical and Health Science and Technology Development Project of Shandong Provincial of China 202104110087the Natural Science Foundation of Shandong Province of China ZR2022QH202
6 · The paper itself

Abstract

backgroundThe lactate-to-albumin ratio (LAR) is a predictor in sepsis that has been shown to outperform lactate and albumin. However, the role of LAR and mortality in Chinese adult patients with sepsis has not been evaluated.

methodsThis retrospective study aims to probe into the predictive value of the LAR for 30-day mortality in Chinese adult patients with sepsis, comparing its effectiveness to traditional markers such as lactate and albumin. This retrospective cohort study utilized data from Dr. Wang’s study published in PLOS ONE. This data was Dongyang People’s Hospital admitting to the emergency department between June 2013 and September 2021.

resultsAmong the 1,205 sepsis patients, 1,014 survived, and 191 did not, yielding a 30-day mortality rate of 15.9%. Uni- and multivariate logistics regression model were used. Meanwhile, Receiver Operating Characteristic (ROC) analysis was used to assess the predictive ability of lactate, albumin, and LAR for 30-day mortality. Multivariable logistic regression analysis confirmed that each 1-unit increase in LAR corresponded to a 97% increase in the risk of 30-day mortality, with an odds ratio (OR) of 1.97 (95% confidence interval (CI) 1.67–2.33, P < 0.001). Meanwhile, the area under the ROC curve (AUC) of LAR 77.68% was obviously superior to albumin 63.72% and lactate 76.32%.

conclusionsThe findings uncover a notable correlation between higher LAR levels and increased 30-day mortality risk in Chinese adult patients with sepsis. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Lactic AcidSepsisSerum AlbuminAdultAgedAged, 80 and overBiomarkersChinaFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesROC CurveBiomarkersLactic AcidSerum Albumin30-day mortalityCohortLactate-to-albumin ratio (LAR)Sepsis

Identifiers

PMID41652564
PMCPMC12977411

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.