α-1-Acid Glycoprotein Concentration as an Outcome Predictor in Adult Patients with Sepsis

Sheng Yuan Hsiao, Yun Ru Lai, Chia Te Kung, Nai Wen Tsai, Chih Min Su, Chih Cheng Huang, Hung Chen Wang, Ben Chung Cheng, Yu Jih Su, Wei Che Lin, Yi Fang Chiang, Jih Yang Ko, Cheng Hsien Lu*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

10 Scopus citations

Abstract

Background. α-1-Acid glycoprotein (AGP) is an acute-phase protein that plays a role in first-line defense against infection and is therefore elevated in sepsis. We tested the hypothesis that AGP levels increase initially in sepsis and decrease after antimicrobial therapy and that these levels may predict treatment outcomes. Methods. AGP, biomarkers widely used in clinical practice, and maximum 24-h acute physiology and chronic health evaluation (APACHE)-II scores upon emergency department (ED) admission were prospectively evaluated and compared. We further examined changes in AGP concentrations 1, 4, and 7 days after admission and determined the value of AGP that may be used to accurately and reliably predict the prognosis in patients with sepsis. Results. Mechanical ventilation, white blood cell (WBC) counts, C-reactive protein (CRP) and lactate levels, maximum 24-h APACHE-II scores, and AGP concentrations were significantly higher upon admission in patients with sepsis who died. AGP and lactate concentrations were also significantly higher in non-survivors than in survivors on days 1, 4, and 7. As indicated by the stepwise logistic regression model analysis and area under the curve analysis, AGP was the best prognostic indicator, and the cut-off value for predicting fatality was 1307 μg/mL, and any increase 1-ng/mL in AGP concentration would increase the fatality rate by 0.5%. Conclusion. Based on our observations, AGP may be a good prognostic predictor in patients with sepsis. In addition, serial AGP levels meet the requirements for predicting outcomes in patients with sepsis.

Original languageEnglish
Article number3174896
JournalBioMed Research International
Volume2019
DOIs
StatePublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 Sheng-Yuan Hsiao et al.

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