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A trauma‐related survival predictive model of acute respiratory distress syndrome

PURPOSE: The purpose of this study was to construct and validate a simple model for the prediction of survival in patients with trauma‐related ARDS. METHODS: This is a single‐center, retrospective cohort study using MIMIC‐III Clinical Database. RESULTS: 842 patients were included in this study. 175...

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Autores principales: Tang, Rui, Wang, Hanghang, Peng, Junnan, Wang, Daoxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8605170/
https://www.ncbi.nlm.nih.gov/pubmed/34545630
http://dx.doi.org/10.1002/jcla.24006
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author Tang, Rui
Wang, Hanghang
Peng, Junnan
Wang, Daoxin
author_facet Tang, Rui
Wang, Hanghang
Peng, Junnan
Wang, Daoxin
author_sort Tang, Rui
collection PubMed
description PURPOSE: The purpose of this study was to construct and validate a simple model for the prediction of survival in patients with trauma‐related ARDS. METHODS: This is a single‐center, retrospective cohort study using MIMIC‐III Clinical Database. RESULTS: 842 patients were included in this study. 175 (20.8%) died in‐hospital, whereas 215 (25.5%) died within 90 days. The deceased group had higher Acute Physiology Score (APS III), Sequential Organ Failure Assessment (SOFA), and Simplified Acute Physiology Score II (SAPS II). In multivariate logistic regression model, independent risk factors for mortality in ARDS patients included age ([odds ratio] OR, 1.035; 95% confidence interval [CI], 1.020–1.049), body mass index (OR, 0.957; 95% CI, 0.926–0.989), red blood cell distribution width (OR, 1.283; 95% CI, 1.141–1.443), hematocrit (OR, 1.055; 95% CI, 1.017–1.095), lactate (OR, 1.226; 95% CI, 1.127–1.334), blood urea nitrogen (OR, 1.025; 95% CI, 1.007–1.044), acute kidney failure (OR, 1.875; 95% CI, 1.188–2.959), sepsis (OR, 1.917; 95% CI, 1.165–3.153), type of admission (emergency vs. elective [OR, 2.822; 95% CI, 1.647–4.837], and urgent vs. elective [OR, 5.156; 95% CI, 1.896–14.027]). The area under the curve (AUC) of the model was 0.826, which was superior than the SAPS II (0.776), APS III (0.718), and SOFA (0.692). In the cross‐validation model, the accuracy of the test set was 0.823, the precision was 0.643, and the AUC was 0.813. CONCLUSIONS: We established a prediction model using data commonly used in the clinic, which has high accuracy and precision and is worthy of use in clinical practice.
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spelling pubmed-86051702021-11-26 A trauma‐related survival predictive model of acute respiratory distress syndrome Tang, Rui Wang, Hanghang Peng, Junnan Wang, Daoxin J Clin Lab Anal Research Articles PURPOSE: The purpose of this study was to construct and validate a simple model for the prediction of survival in patients with trauma‐related ARDS. METHODS: This is a single‐center, retrospective cohort study using MIMIC‐III Clinical Database. RESULTS: 842 patients were included in this study. 175 (20.8%) died in‐hospital, whereas 215 (25.5%) died within 90 days. The deceased group had higher Acute Physiology Score (APS III), Sequential Organ Failure Assessment (SOFA), and Simplified Acute Physiology Score II (SAPS II). In multivariate logistic regression model, independent risk factors for mortality in ARDS patients included age ([odds ratio] OR, 1.035; 95% confidence interval [CI], 1.020–1.049), body mass index (OR, 0.957; 95% CI, 0.926–0.989), red blood cell distribution width (OR, 1.283; 95% CI, 1.141–1.443), hematocrit (OR, 1.055; 95% CI, 1.017–1.095), lactate (OR, 1.226; 95% CI, 1.127–1.334), blood urea nitrogen (OR, 1.025; 95% CI, 1.007–1.044), acute kidney failure (OR, 1.875; 95% CI, 1.188–2.959), sepsis (OR, 1.917; 95% CI, 1.165–3.153), type of admission (emergency vs. elective [OR, 2.822; 95% CI, 1.647–4.837], and urgent vs. elective [OR, 5.156; 95% CI, 1.896–14.027]). The area under the curve (AUC) of the model was 0.826, which was superior than the SAPS II (0.776), APS III (0.718), and SOFA (0.692). In the cross‐validation model, the accuracy of the test set was 0.823, the precision was 0.643, and the AUC was 0.813. CONCLUSIONS: We established a prediction model using data commonly used in the clinic, which has high accuracy and precision and is worthy of use in clinical practice. John Wiley and Sons Inc. 2021-09-20 /pmc/articles/PMC8605170/ /pubmed/34545630 http://dx.doi.org/10.1002/jcla.24006 Text en © 2021 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Research Articles
Tang, Rui
Wang, Hanghang
Peng, Junnan
Wang, Daoxin
A trauma‐related survival predictive model of acute respiratory distress syndrome
title A trauma‐related survival predictive model of acute respiratory distress syndrome
title_full A trauma‐related survival predictive model of acute respiratory distress syndrome
title_fullStr A trauma‐related survival predictive model of acute respiratory distress syndrome
title_full_unstemmed A trauma‐related survival predictive model of acute respiratory distress syndrome
title_short A trauma‐related survival predictive model of acute respiratory distress syndrome
title_sort trauma‐related survival predictive model of acute respiratory distress syndrome
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8605170/
https://www.ncbi.nlm.nih.gov/pubmed/34545630
http://dx.doi.org/10.1002/jcla.24006
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