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28-day sepsis mortality prediction model from combined serial interleukin-6, lactate, and procalcitonin measurements: a retrospective cohort study

Sepsis is a global medical issue owing to its unacceptably high mortality rate. Therefore, an effective approach to predicting patient outcomes is critically needed. We aimed to search for a novel 28-day sepsis mortality prediction model based on serial interleukin-6 (IL-6), lactate (LAC), and proca...

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Autores principales: Xie, Yinjing, Zhuang, Dehua, Chen, Huaisheng, Zou, Shiqing, Chen, Weibu, Chen, Yue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816294/
https://www.ncbi.nlm.nih.gov/pubmed/36383295
http://dx.doi.org/10.1007/s10096-022-04517-1
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author Xie, Yinjing
Zhuang, Dehua
Chen, Huaisheng
Zou, Shiqing
Chen, Weibu
Chen, Yue
author_facet Xie, Yinjing
Zhuang, Dehua
Chen, Huaisheng
Zou, Shiqing
Chen, Weibu
Chen, Yue
author_sort Xie, Yinjing
collection PubMed
description Sepsis is a global medical issue owing to its unacceptably high mortality rate. Therefore, an effective approach to predicting patient outcomes is critically needed. We aimed to search for a novel 28-day sepsis mortality prediction model based on serial interleukin-6 (IL-6), lactate (LAC), and procalcitonin (PCT) measurements. We enrolled 367 septic patients based on Sepsis-3 (Third International Consensus Definitions for Sepsis and Septic Shock). Serum IL-6, LAC, and PCT levels were measured serially. Results collected within 24 and 48–72 h of admission were marked as D1 and D3 (e.g., IL-6D1/D3), respectively; the IL-6, LAC, and PCT clearance (IL-6c, LACc, PCTc) at D3 were calculated. Data were split into training and validation cohorts (7:3). Logistic regression analyses were used to select variables to develop models and choose the best one according to the Akaike information criterion (AIC). Receiver operating characteristic curves (ROC), calibration plots, and decision curve analysis (DCA) were used to test model performance. A nomogram was used to validate the model. There were 314 (85.56%) survivors and 53 (14.44%) non-survivors. Logistic regression analyses showed that IL-6D1, IL-6D3, PCTD1, PCTD3, and LACcD3 could be used to develop the best prediction model. The areas under the curves (AUC) of the training (0.849, 95% CI: 0.787–0.911) and validation cohorts (0.828, 95% CI: 0.727–0.929), calibration plot, and the DCA showed that the model performed well. Thus, the predictive value of the risk nomogram was verified. Combining IL-6D1, IL-6D3, PCTD1, PCTD3, and LACcD3 may create an accurate prediction model for 28-day sepsis mortality. Multiple-center research with a larger quantity of data is necessary to determine its clinical utility.
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spelling pubmed-98162942023-01-07 28-day sepsis mortality prediction model from combined serial interleukin-6, lactate, and procalcitonin measurements: a retrospective cohort study Xie, Yinjing Zhuang, Dehua Chen, Huaisheng Zou, Shiqing Chen, Weibu Chen, Yue Eur J Clin Microbiol Infect Dis Original Article Sepsis is a global medical issue owing to its unacceptably high mortality rate. Therefore, an effective approach to predicting patient outcomes is critically needed. We aimed to search for a novel 28-day sepsis mortality prediction model based on serial interleukin-6 (IL-6), lactate (LAC), and procalcitonin (PCT) measurements. We enrolled 367 septic patients based on Sepsis-3 (Third International Consensus Definitions for Sepsis and Septic Shock). Serum IL-6, LAC, and PCT levels were measured serially. Results collected within 24 and 48–72 h of admission were marked as D1 and D3 (e.g., IL-6D1/D3), respectively; the IL-6, LAC, and PCT clearance (IL-6c, LACc, PCTc) at D3 were calculated. Data were split into training and validation cohorts (7:3). Logistic regression analyses were used to select variables to develop models and choose the best one according to the Akaike information criterion (AIC). Receiver operating characteristic curves (ROC), calibration plots, and decision curve analysis (DCA) were used to test model performance. A nomogram was used to validate the model. There were 314 (85.56%) survivors and 53 (14.44%) non-survivors. Logistic regression analyses showed that IL-6D1, IL-6D3, PCTD1, PCTD3, and LACcD3 could be used to develop the best prediction model. The areas under the curves (AUC) of the training (0.849, 95% CI: 0.787–0.911) and validation cohorts (0.828, 95% CI: 0.727–0.929), calibration plot, and the DCA showed that the model performed well. Thus, the predictive value of the risk nomogram was verified. Combining IL-6D1, IL-6D3, PCTD1, PCTD3, and LACcD3 may create an accurate prediction model for 28-day sepsis mortality. Multiple-center research with a larger quantity of data is necessary to determine its clinical utility. Springer Berlin Heidelberg 2022-11-16 2023 /pmc/articles/PMC9816294/ /pubmed/36383295 http://dx.doi.org/10.1007/s10096-022-04517-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Xie, Yinjing
Zhuang, Dehua
Chen, Huaisheng
Zou, Shiqing
Chen, Weibu
Chen, Yue
28-day sepsis mortality prediction model from combined serial interleukin-6, lactate, and procalcitonin measurements: a retrospective cohort study
title 28-day sepsis mortality prediction model from combined serial interleukin-6, lactate, and procalcitonin measurements: a retrospective cohort study
title_full 28-day sepsis mortality prediction model from combined serial interleukin-6, lactate, and procalcitonin measurements: a retrospective cohort study
title_fullStr 28-day sepsis mortality prediction model from combined serial interleukin-6, lactate, and procalcitonin measurements: a retrospective cohort study
title_full_unstemmed 28-day sepsis mortality prediction model from combined serial interleukin-6, lactate, and procalcitonin measurements: a retrospective cohort study
title_short 28-day sepsis mortality prediction model from combined serial interleukin-6, lactate, and procalcitonin measurements: a retrospective cohort study
title_sort 28-day sepsis mortality prediction model from combined serial interleukin-6, lactate, and procalcitonin measurements: a retrospective cohort study
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9816294/
https://www.ncbi.nlm.nih.gov/pubmed/36383295
http://dx.doi.org/10.1007/s10096-022-04517-1
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