Risk model for predicting mortality in patients with necrotizing soft tissue infections in the intensive care unit
Copyright © 2024. Published by Elsevier Ltd..
BACKGROUND: The goal of this study is to look into the factors that lead to death in patients with necrotizing soft tissue infections（NSTIs） in the intensive care unit and create a mortality risk model.
METHODS: The clinical data of 106 patients with necrotizing soft tissue infections admitted to intensive care unit(ICU) of the First Affiliated Hospital of Wenzhou Medical University between January 2008 and December 2021 were retrospectively analyzed. Univariate analysis and multivariate analysis were performed to evaluate the risk factors impacting patient mortality. The regression coefficient in binary logistic regression analysis was converted into the item score in the model, and then the model score of each patient was calculated. Finally, an ROC curve was constructed to evaluate the efficiency of the model for predicting mortality. Thirteen patients with NSTIs admitted to ICU between January 2022 and November 2022 were used to validate the model.
RESULTS: The death group had 44 patients, while the survival group had 62 patients. The overall mortality was 41.5%. Binary logistic regression analysis showed that risk factors for mortality were age≥ 60 years(OR:4.419; 95%CI:1.093-17.862; P = 0.037), creatinine ≥ 132μmol/L(OR:11.166; 95%CI:2.234-55.816; P = 0.003), creatine kinase ≥ 1104 U/L(OR:4.019; 95%CI:1.134-14.250; P = 0.031), prothrombin time ≥ 24.4 s(OR:11.589; 95%CI:2.510-53.506; P = 0.002), and invasive mechanical ventilation (OR:17.404; 95%CI:4.586-66.052; P＜0.000). The AUC of the model for predicting mortality was 0.940 (95% CI:0.894-0.986). When the cut-off value for the model was 4 points, the sensitivity was 95.5% and the specificity was 83.9%.
CONCLUSION: The death risk model in this study for NSTIs patients in the intensive care unit shows high sensitivity and specificity. Patients with a score of ≥ 4 points have a higher risk of mortality.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:50 |
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| Enthalten in: |
Burns : journal of the International Society for Burn Injuries - 50(2024), 3 vom: 04. Apr., Seite 578-584 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Lu-Yao [VerfasserIn] |
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| Links: |
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| Themen: |
ICU |
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| Anmerkungen: |
Date Completed 08.04.2024 Date Revised 08.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.burns.2023.11.008 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM367287374 |
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| 245 | 1 | 0 | |a Risk model for predicting mortality in patients with necrotizing soft tissue infections in the intensive care unit |
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| 500 | |a Date Revised 08.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024. Published by Elsevier Ltd. | ||
| 520 | |a BACKGROUND: The goal of this study is to look into the factors that lead to death in patients with necrotizing soft tissue infections（NSTIs） in the intensive care unit and create a mortality risk model | ||
| 520 | |a METHODS: The clinical data of 106 patients with necrotizing soft tissue infections admitted to intensive care unit(ICU) of the First Affiliated Hospital of Wenzhou Medical University between January 2008 and December 2021 were retrospectively analyzed. Univariate analysis and multivariate analysis were performed to evaluate the risk factors impacting patient mortality. The regression coefficient in binary logistic regression analysis was converted into the item score in the model, and then the model score of each patient was calculated. Finally, an ROC curve was constructed to evaluate the efficiency of the model for predicting mortality. Thirteen patients with NSTIs admitted to ICU between January 2022 and November 2022 were used to validate the model | ||
| 520 | |a RESULTS: The death group had 44 patients, while the survival group had 62 patients. The overall mortality was 41.5%. Binary logistic regression analysis showed that risk factors for mortality were age≥ 60 years(OR:4.419; 95%CI:1.093-17.862; P = 0.037), creatinine ≥ 132μmol/L(OR:11.166; 95%CI:2.234-55.816; P = 0.003), creatine kinase ≥ 1104 U/L(OR:4.019; 95%CI:1.134-14.250; P = 0.031), prothrombin time ≥ 24.4 s(OR:11.589; 95%CI:2.510-53.506; P = 0.002), and invasive mechanical ventilation (OR:17.404; 95%CI:4.586-66.052; P＜0.000). The AUC of the model for predicting mortality was 0.940 (95% CI:0.894-0.986). When the cut-off value for the model was 4 points, the sensitivity was 95.5% and the specificity was 83.9% | ||
| 520 | |a CONCLUSION: The death risk model in this study for NSTIs patients in the intensive care unit shows high sensitivity and specificity. Patients with a score of ≥ 4 points have a higher risk of mortality | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a ICU | |
| 650 | 4 | |a Mortality | |
| 650 | 4 | |a Necrotizing fasciitis | |
| 650 | 4 | |a Necrotizing soft tissue infections | |
| 700 | 1 | |a Zheng, Wei-Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Ke |e verfasserin |4 aut | |
| 700 | 1 | |a JianPing-Ye |e verfasserin |4 aut | |
| 700 | 1 | |a Qiu, Zhi-Min |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Guang-Ju |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Pin-Pin |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Long-Wang |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Ya-Hui |e verfasserin |4 aut | |
| 700 | 1 | |a Hong, Guang-Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Zhong-Qiu |e verfasserin |4 aut | |
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