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Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome

OBJECTIVE: As a common yet intractable complication of severe sepsis, acute respiratory distress syndrome (ARDS) is closely associated with poor clinical outcomes and elevated medical expenses. The aim of the current study is to generate a model combining transcriptional biomarkers and clinical para...

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Autores principales: Yao, Ren-Qi, Shen, Zong, Ma, Qi-Min, Ling, Ping, Wei, Chen-Ru, Zheng, Li-Yu, Duan, Yu, Li, Wei, Zhu, Feng, Sun, Yu, Wu, Guo-Sheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9846102/
https://www.ncbi.nlm.nih.gov/pubmed/36685531
http://dx.doi.org/10.3389/fimmu.2022.1084568
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author Yao, Ren-Qi
Shen, Zong
Ma, Qi-Min
Ling, Ping
Wei, Chen-Ru
Zheng, Li-Yu
Duan, Yu
Li, Wei
Zhu, Feng
Sun, Yu
Wu, Guo-Sheng
author_facet Yao, Ren-Qi
Shen, Zong
Ma, Qi-Min
Ling, Ping
Wei, Chen-Ru
Zheng, Li-Yu
Duan, Yu
Li, Wei
Zhu, Feng
Sun, Yu
Wu, Guo-Sheng
author_sort Yao, Ren-Qi
collection PubMed
description OBJECTIVE: As a common yet intractable complication of severe sepsis, acute respiratory distress syndrome (ARDS) is closely associated with poor clinical outcomes and elevated medical expenses. The aim of the current study is to generate a model combining transcriptional biomarkers and clinical parameters to alarm the development of ARDS in septic patients. METHODS: Gene expression profile (GSE66890) was downloaded from the Gene Expression Omnibus database and clinical data were extracted. Differentially expressed genes (DEGs) from whole blood leukocytes were identified between patients with sepsis alone and septic patients who develop ARDS. ARDS prediction model was constructed using backward stepwise regression and Akaike Information Criterion (AIC). Meanwhile, a nomogram based on this model was established, with subsequent internal validation. RESULTS: A total of 57 severe septic patients were enrolled in this study, and 28 (49.1%) developed ARDS. Based on the differential expression analysis, six DEGs (BPI, OLFM4, LCN2, CD24, MMP8 and MME) were screened. According to the outcome prediction model, six valuable risk factors (direct lung injury, shock, tumor, BPI, MME and MMP8) were incorporated into a nomogram, which was used to predict the onset of ARDS in septic patients. The calibration curves of the nomogram showed good consistency between the probabilities and observed values. The decision curve analysis also revealed the potential clinical usefulness of the nomogram. The area under the receiver operating characteristic (AUROC) for the prediction of ARDS occurrence in septic patients by the nomogram was 0.86 (95% CI = 0.767-0.952). A sensitivity analysis showed that the AUROC for the prediction of ARDS development in septic patients without direct lung injury was 0.967 (95% CI = 0.896-1.0). CONCLUSIONS: The nomogram based on transcriptional biomarkers and clinical parameters showed a good performance for the prediction of ARDS occurrence in septic patients.
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spelling pubmed-98461022023-01-19 Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome Yao, Ren-Qi Shen, Zong Ma, Qi-Min Ling, Ping Wei, Chen-Ru Zheng, Li-Yu Duan, Yu Li, Wei Zhu, Feng Sun, Yu Wu, Guo-Sheng Front Immunol Immunology OBJECTIVE: As a common yet intractable complication of severe sepsis, acute respiratory distress syndrome (ARDS) is closely associated with poor clinical outcomes and elevated medical expenses. The aim of the current study is to generate a model combining transcriptional biomarkers and clinical parameters to alarm the development of ARDS in septic patients. METHODS: Gene expression profile (GSE66890) was downloaded from the Gene Expression Omnibus database and clinical data were extracted. Differentially expressed genes (DEGs) from whole blood leukocytes were identified between patients with sepsis alone and septic patients who develop ARDS. ARDS prediction model was constructed using backward stepwise regression and Akaike Information Criterion (AIC). Meanwhile, a nomogram based on this model was established, with subsequent internal validation. RESULTS: A total of 57 severe septic patients were enrolled in this study, and 28 (49.1%) developed ARDS. Based on the differential expression analysis, six DEGs (BPI, OLFM4, LCN2, CD24, MMP8 and MME) were screened. According to the outcome prediction model, six valuable risk factors (direct lung injury, shock, tumor, BPI, MME and MMP8) were incorporated into a nomogram, which was used to predict the onset of ARDS in septic patients. The calibration curves of the nomogram showed good consistency between the probabilities and observed values. The decision curve analysis also revealed the potential clinical usefulness of the nomogram. The area under the receiver operating characteristic (AUROC) for the prediction of ARDS occurrence in septic patients by the nomogram was 0.86 (95% CI = 0.767-0.952). A sensitivity analysis showed that the AUROC for the prediction of ARDS development in septic patients without direct lung injury was 0.967 (95% CI = 0.896-1.0). CONCLUSIONS: The nomogram based on transcriptional biomarkers and clinical parameters showed a good performance for the prediction of ARDS occurrence in septic patients. Frontiers Media S.A. 2023-01-04 /pmc/articles/PMC9846102/ /pubmed/36685531 http://dx.doi.org/10.3389/fimmu.2022.1084568 Text en Copyright © 2023 Yao, Shen, Ma, Ling, Wei, Zheng, Duan, Li, Zhu, Sun and Wu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Yao, Ren-Qi
Shen, Zong
Ma, Qi-Min
Ling, Ping
Wei, Chen-Ru
Zheng, Li-Yu
Duan, Yu
Li, Wei
Zhu, Feng
Sun, Yu
Wu, Guo-Sheng
Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome
title Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome
title_full Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome
title_fullStr Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome
title_full_unstemmed Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome
title_short Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome
title_sort combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9846102/
https://www.ncbi.nlm.nih.gov/pubmed/36685531
http://dx.doi.org/10.3389/fimmu.2022.1084568
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