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How likely is septic shock to develop in a patient with Fournier's gangrene? A risk prediction model based on a 7-year retrospective study
BACKGROUND: Fournier’s gangrene (FG) is a rare life-threatening form of necrotizing fasciitis. The risk factors for septic shock in patients with FG are unclear. This study aimed to identify potential risk factors and develop a prediction model for septic shock in patients with FG. METHODS: This ret...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9368829/ https://www.ncbi.nlm.nih.gov/pubmed/35966629 http://dx.doi.org/10.1093/gastro/goac038 |
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author | Yang, Yang Wang, Li-Chun Yu, Xin-Yang Zhang, Xiao-Fei Yang, Zhong-Qing Zheng, Yang-Zi Jiang, Bin-Yan Chen, Lei |
author_facet | Yang, Yang Wang, Li-Chun Yu, Xin-Yang Zhang, Xiao-Fei Yang, Zhong-Qing Zheng, Yang-Zi Jiang, Bin-Yan Chen, Lei |
author_sort | Yang, Yang |
collection | PubMed |
description | BACKGROUND: Fournier’s gangrene (FG) is a rare life-threatening form of necrotizing fasciitis. The risk factors for septic shock in patients with FG are unclear. This study aimed to identify potential risk factors and develop a prediction model for septic shock in patients with FG. METHODS: This retrospective cohort study included patients who were treated for FG between May 2013 and May 2020 at the Sixth Affiliated Hospital, Sun Yat-sen University (Guangzhou, China). The patients were divided into a septic shock group and a non-septic shock group. An L1-penalized logistic regression model was used to detect the main effect of important factors and a penalized Quadratic Discriminant Analysis method was used to identify possible interaction effects between different factors. The selected main factors and interactions were used to obtain a logistic regression model based on the Bayesian information criterion. RESULTS: A total of 113 patients with FG were enrolled and allocated to the septic shock group (n = 24) or non-septic shock group (n = 89). The best model selected identified by backward logistic regression based on Bayesian information criterion selected temperature, platelets, total bilirubin (TBIL) level, and pneumatosis on pelvic computed tomography/magnetic resonance images as the main linear effect and Na(+) × TBIL as the interaction effect. The area under the ROC curve of the probability of FG with septic shock by our model was 0.84 (95% confidence interval, 0.78–0.95). The Harrell's concordance index for the nomogram was 0.864 (95% confidence interval, 0.78–0.95). CONCLUSION: We have developed a prediction model for evaluation of the risk of septic shock in patients with FG that could assist clinicians in identifying critically ill patients with FG and prevent them from reaching a crisis state. |
format | Online Article Text |
id | pubmed-9368829 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-93688292022-08-12 How likely is septic shock to develop in a patient with Fournier's gangrene? A risk prediction model based on a 7-year retrospective study Yang, Yang Wang, Li-Chun Yu, Xin-Yang Zhang, Xiao-Fei Yang, Zhong-Qing Zheng, Yang-Zi Jiang, Bin-Yan Chen, Lei Gastroenterol Rep (Oxf) Original Article BACKGROUND: Fournier’s gangrene (FG) is a rare life-threatening form of necrotizing fasciitis. The risk factors for septic shock in patients with FG are unclear. This study aimed to identify potential risk factors and develop a prediction model for septic shock in patients with FG. METHODS: This retrospective cohort study included patients who were treated for FG between May 2013 and May 2020 at the Sixth Affiliated Hospital, Sun Yat-sen University (Guangzhou, China). The patients were divided into a septic shock group and a non-septic shock group. An L1-penalized logistic regression model was used to detect the main effect of important factors and a penalized Quadratic Discriminant Analysis method was used to identify possible interaction effects between different factors. The selected main factors and interactions were used to obtain a logistic regression model based on the Bayesian information criterion. RESULTS: A total of 113 patients with FG were enrolled and allocated to the septic shock group (n = 24) or non-septic shock group (n = 89). The best model selected identified by backward logistic regression based on Bayesian information criterion selected temperature, platelets, total bilirubin (TBIL) level, and pneumatosis on pelvic computed tomography/magnetic resonance images as the main linear effect and Na(+) × TBIL as the interaction effect. The area under the ROC curve of the probability of FG with septic shock by our model was 0.84 (95% confidence interval, 0.78–0.95). The Harrell's concordance index for the nomogram was 0.864 (95% confidence interval, 0.78–0.95). CONCLUSION: We have developed a prediction model for evaluation of the risk of septic shock in patients with FG that could assist clinicians in identifying critically ill patients with FG and prevent them from reaching a crisis state. Oxford University Press 2022-08-11 /pmc/articles/PMC9368829/ /pubmed/35966629 http://dx.doi.org/10.1093/gastro/goac038 Text en © The Author(s) 2022. Published by Oxford University Press and Sixth Affiliated Hospital of Sun Yat-sen University https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Article Yang, Yang Wang, Li-Chun Yu, Xin-Yang Zhang, Xiao-Fei Yang, Zhong-Qing Zheng, Yang-Zi Jiang, Bin-Yan Chen, Lei How likely is septic shock to develop in a patient with Fournier's gangrene? A risk prediction model based on a 7-year retrospective study |
title | How likely is septic shock to develop in a patient with Fournier's gangrene? A risk prediction model based on a 7-year retrospective study |
title_full | How likely is septic shock to develop in a patient with Fournier's gangrene? A risk prediction model based on a 7-year retrospective study |
title_fullStr | How likely is septic shock to develop in a patient with Fournier's gangrene? A risk prediction model based on a 7-year retrospective study |
title_full_unstemmed | How likely is septic shock to develop in a patient with Fournier's gangrene? A risk prediction model based on a 7-year retrospective study |
title_short | How likely is septic shock to develop in a patient with Fournier's gangrene? A risk prediction model based on a 7-year retrospective study |
title_sort | how likely is septic shock to develop in a patient with fournier's gangrene? a risk prediction model based on a 7-year retrospective study |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9368829/ https://www.ncbi.nlm.nih.gov/pubmed/35966629 http://dx.doi.org/10.1093/gastro/goac038 |
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