Cargando…

Serum Anion Gap Predicts All-Cause Mortality in Critically Ill Patients with Acute Kidney Injury: Analysis of the MIMIC-III Database

BACKGROUND: No epidemiological study has investigated the effect of anion gap (AG) on the prognosis of critically ill patients with acute kidney injury (AKI). Therefore, we aimed to determine the association between serum AG and all-cause mortality in these patients. METHODS: From MIMIC III, we extr...

Descripción completa

Detalles Bibliográficos
Autores principales: Cheng, Bihuan, Li, Diwen, Gong, Yuqiang, Ying, Binyu, Wang, Benji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6995483/
https://www.ncbi.nlm.nih.gov/pubmed/32051697
http://dx.doi.org/10.1155/2020/6501272
_version_ 1783493379563192320
author Cheng, Bihuan
Li, Diwen
Gong, Yuqiang
Ying, Binyu
Wang, Benji
author_facet Cheng, Bihuan
Li, Diwen
Gong, Yuqiang
Ying, Binyu
Wang, Benji
author_sort Cheng, Bihuan
collection PubMed
description BACKGROUND: No epidemiological study has investigated the effect of anion gap (AG) on the prognosis of critically ill patients with acute kidney injury (AKI). Therefore, we aimed to determine the association between serum AG and all-cause mortality in these patients. METHODS: From MIMIC III, we extracted demographics, vital signs, laboratory tests, comorbidities, and scoring systems from the first 24 h after patient ICU admission. A generalized additive model was used to identify a nonlinear association between anion gap and 30-day all-cause mortality. We also used the Cox proportional hazards models to measure the association between AG levels and 30-day, 90-day, and 365-day mortality in patients with AKI. RESULTS: A total of 11,573 eligible subjects were extracted from the MIMIC-III. The relationship between AG levels and 30-day all-cause mortality in patients with AKI was nonlinear, with a U-shaped curve. In multivariate analysis, after adjusting for potential confounders, higher AG was a significant predictor of 30-day, 90-day, and 365-day all-cause mortality compared with lower AG (HR, 95% CI: 1.54, 1.33–1.75; 1.55, 1.38–1.73; 1.46, 1.31–1.60). CONCLUSIONS: The relationship between AG levels and 30-day all-cause mortality described a U-shaped curve. High-AG levels were associated with increased risk 30-day, 90-day, and 365-day all-cause mortality in critically ill patients with AKI.
format Online
Article
Text
id pubmed-6995483
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-69954832020-02-12 Serum Anion Gap Predicts All-Cause Mortality in Critically Ill Patients with Acute Kidney Injury: Analysis of the MIMIC-III Database Cheng, Bihuan Li, Diwen Gong, Yuqiang Ying, Binyu Wang, Benji Dis Markers Research Article BACKGROUND: No epidemiological study has investigated the effect of anion gap (AG) on the prognosis of critically ill patients with acute kidney injury (AKI). Therefore, we aimed to determine the association between serum AG and all-cause mortality in these patients. METHODS: From MIMIC III, we extracted demographics, vital signs, laboratory tests, comorbidities, and scoring systems from the first 24 h after patient ICU admission. A generalized additive model was used to identify a nonlinear association between anion gap and 30-day all-cause mortality. We also used the Cox proportional hazards models to measure the association between AG levels and 30-day, 90-day, and 365-day mortality in patients with AKI. RESULTS: A total of 11,573 eligible subjects were extracted from the MIMIC-III. The relationship between AG levels and 30-day all-cause mortality in patients with AKI was nonlinear, with a U-shaped curve. In multivariate analysis, after adjusting for potential confounders, higher AG was a significant predictor of 30-day, 90-day, and 365-day all-cause mortality compared with lower AG (HR, 95% CI: 1.54, 1.33–1.75; 1.55, 1.38–1.73; 1.46, 1.31–1.60). CONCLUSIONS: The relationship between AG levels and 30-day all-cause mortality described a U-shaped curve. High-AG levels were associated with increased risk 30-day, 90-day, and 365-day all-cause mortality in critically ill patients with AKI. Hindawi 2020-01-19 /pmc/articles/PMC6995483/ /pubmed/32051697 http://dx.doi.org/10.1155/2020/6501272 Text en Copyright © 2020 Bihuan Cheng et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Cheng, Bihuan
Li, Diwen
Gong, Yuqiang
Ying, Binyu
Wang, Benji
Serum Anion Gap Predicts All-Cause Mortality in Critically Ill Patients with Acute Kidney Injury: Analysis of the MIMIC-III Database
title Serum Anion Gap Predicts All-Cause Mortality in Critically Ill Patients with Acute Kidney Injury: Analysis of the MIMIC-III Database
title_full Serum Anion Gap Predicts All-Cause Mortality in Critically Ill Patients with Acute Kidney Injury: Analysis of the MIMIC-III Database
title_fullStr Serum Anion Gap Predicts All-Cause Mortality in Critically Ill Patients with Acute Kidney Injury: Analysis of the MIMIC-III Database
title_full_unstemmed Serum Anion Gap Predicts All-Cause Mortality in Critically Ill Patients with Acute Kidney Injury: Analysis of the MIMIC-III Database
title_short Serum Anion Gap Predicts All-Cause Mortality in Critically Ill Patients with Acute Kidney Injury: Analysis of the MIMIC-III Database
title_sort serum anion gap predicts all-cause mortality in critically ill patients with acute kidney injury: analysis of the mimic-iii database
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6995483/
https://www.ncbi.nlm.nih.gov/pubmed/32051697
http://dx.doi.org/10.1155/2020/6501272
work_keys_str_mv AT chengbihuan serumaniongappredictsallcausemortalityincriticallyillpatientswithacutekidneyinjuryanalysisofthemimiciiidatabase
AT lidiwen serumaniongappredictsallcausemortalityincriticallyillpatientswithacutekidneyinjuryanalysisofthemimiciiidatabase
AT gongyuqiang serumaniongappredictsallcausemortalityincriticallyillpatientswithacutekidneyinjuryanalysisofthemimiciiidatabase
AT yingbinyu serumaniongappredictsallcausemortalityincriticallyillpatientswithacutekidneyinjuryanalysisofthemimiciiidatabase
AT wangbenji serumaniongappredictsallcausemortalityincriticallyillpatientswithacutekidneyinjuryanalysisofthemimiciiidatabase