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An acute kidney injury prediction nomogram based on neurosurgical intensive care unit profiles
BACKGROUND: Acute kidney injury (AKI) is a common and serious complication with high mortality within the neural-critical care unit, and can limit the treatment of osmotic diuresis and body fluid equilibrium. Given its seriousness, it is necessary to find a tool to predict the likelihood of AKI and...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7154440/ https://www.ncbi.nlm.nih.gov/pubmed/32309341 http://dx.doi.org/10.21037/atm.2020.01.60 |
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author | An, Shuo Luo, Hongliang Wang, Jiao Gong, Zhitao Tian, Ye Liu, Xuanhui Ma, Jun Jiang, Rongcai |
author_facet | An, Shuo Luo, Hongliang Wang, Jiao Gong, Zhitao Tian, Ye Liu, Xuanhui Ma, Jun Jiang, Rongcai |
author_sort | An, Shuo |
collection | PubMed |
description | BACKGROUND: Acute kidney injury (AKI) is a common and serious complication with high mortality within the neural-critical care unit, and can limit the treatment of osmotic diuresis and body fluid equilibrium. Given its seriousness, it is necessary to find a tool to predict the likelihood of AKI and to prevent its occurrence. METHODS: In this retrospective study, patients’ clinical profiles, laboratory test results, and doctors’ prescriptions were collected. Least absolute shrinkage and selection operator (LASSO) regression was used to select variables, and a logistic regression model was then applied to find independent risk factors for AKI. Based on the results of multivariate analysis, we established a nomogram to evaluate the probability of AKI, which was verified through the use of a receiver operating characteristic (ROC) curve and its calibration curves. RESULTS: Risk factors given by logistic regression were Glasgow Coma Scale (GCS) classification (1.593; 95% CI: 0.995–2.549; P=0.0523), coefficient of variation (CV) of GCS (1.017; 95% CI: 0.995–1.04; P=0.1367), hypertension (2.238; 95% CI: 1.124–4.456; P=0.0219), coronary heart disease (2.924; 95% CI: 1.2–7.126; P=0.0182), pneumonia within 7 days (3.032; 95% CI: 1.511–6.085; P=0.0018), heart failure within 7 days (6.589; 95% CI: 2.235–19.42; P=0.0006), furosemide (1.011; 95% CI: 1.005–1.016; P<0.0001), torasemide (1.028; 95% CI: 0.976–1.082; P=0.297), dopamine (1; 95% CI: 1–1.001, P=0.3297), and norepinephrine (1.007; 95% CI: 1–1.015; P=0.0474). The area under the curve (AUC) of the prediction model was 0.8786, and the calibration curves showed that the model had a good ability to predict AKI occurrence. CONCLUSIONS: This study presents an AKI prediction nomogram based on LASSO, logistic regression, and clinical risk factors. The clinical use of the nomogram may allow for the timely detection of AKI occurrence and thus improve the prognosis of patients. |
format | Online Article Text |
id | pubmed-7154440 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-71544402020-04-17 An acute kidney injury prediction nomogram based on neurosurgical intensive care unit profiles An, Shuo Luo, Hongliang Wang, Jiao Gong, Zhitao Tian, Ye Liu, Xuanhui Ma, Jun Jiang, Rongcai Ann Transl Med Original Article BACKGROUND: Acute kidney injury (AKI) is a common and serious complication with high mortality within the neural-critical care unit, and can limit the treatment of osmotic diuresis and body fluid equilibrium. Given its seriousness, it is necessary to find a tool to predict the likelihood of AKI and to prevent its occurrence. METHODS: In this retrospective study, patients’ clinical profiles, laboratory test results, and doctors’ prescriptions were collected. Least absolute shrinkage and selection operator (LASSO) regression was used to select variables, and a logistic regression model was then applied to find independent risk factors for AKI. Based on the results of multivariate analysis, we established a nomogram to evaluate the probability of AKI, which was verified through the use of a receiver operating characteristic (ROC) curve and its calibration curves. RESULTS: Risk factors given by logistic regression were Glasgow Coma Scale (GCS) classification (1.593; 95% CI: 0.995–2.549; P=0.0523), coefficient of variation (CV) of GCS (1.017; 95% CI: 0.995–1.04; P=0.1367), hypertension (2.238; 95% CI: 1.124–4.456; P=0.0219), coronary heart disease (2.924; 95% CI: 1.2–7.126; P=0.0182), pneumonia within 7 days (3.032; 95% CI: 1.511–6.085; P=0.0018), heart failure within 7 days (6.589; 95% CI: 2.235–19.42; P=0.0006), furosemide (1.011; 95% CI: 1.005–1.016; P<0.0001), torasemide (1.028; 95% CI: 0.976–1.082; P=0.297), dopamine (1; 95% CI: 1–1.001, P=0.3297), and norepinephrine (1.007; 95% CI: 1–1.015; P=0.0474). The area under the curve (AUC) of the prediction model was 0.8786, and the calibration curves showed that the model had a good ability to predict AKI occurrence. CONCLUSIONS: This study presents an AKI prediction nomogram based on LASSO, logistic regression, and clinical risk factors. The clinical use of the nomogram may allow for the timely detection of AKI occurrence and thus improve the prognosis of patients. AME Publishing Company 2020-03 /pmc/articles/PMC7154440/ /pubmed/32309341 http://dx.doi.org/10.21037/atm.2020.01.60 Text en 2020 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article An, Shuo Luo, Hongliang Wang, Jiao Gong, Zhitao Tian, Ye Liu, Xuanhui Ma, Jun Jiang, Rongcai An acute kidney injury prediction nomogram based on neurosurgical intensive care unit profiles |
title | An acute kidney injury prediction nomogram based on neurosurgical intensive care unit profiles |
title_full | An acute kidney injury prediction nomogram based on neurosurgical intensive care unit profiles |
title_fullStr | An acute kidney injury prediction nomogram based on neurosurgical intensive care unit profiles |
title_full_unstemmed | An acute kidney injury prediction nomogram based on neurosurgical intensive care unit profiles |
title_short | An acute kidney injury prediction nomogram based on neurosurgical intensive care unit profiles |
title_sort | acute kidney injury prediction nomogram based on neurosurgical intensive care unit profiles |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7154440/ https://www.ncbi.nlm.nih.gov/pubmed/32309341 http://dx.doi.org/10.21037/atm.2020.01.60 |
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