Cargando…

Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus

BACKGROUND: In sepsis patients, Type 2 Diabetes Mellitus (T2DM) was associated with an increased risk of kidney injury. Furthermore, kidney damage is among the dangerous complications, with a high mortality rate in sepsis patients. However, the underlying predictive model on the prediction of major...

Descripción completa

Detalles Bibliográficos
Autores principales: Xin, Qi, Xie, Tonghui, Chen, Rui, Wang, Hai, Zhang, Xing, Wang, Shufeng, Liu, Chang, Zhang, Jingyao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800518/
https://www.ncbi.nlm.nih.gov/pubmed/36589822
http://dx.doi.org/10.3389/fendo.2022.1024500
_version_ 1784861307099414528
author Xin, Qi
Xie, Tonghui
Chen, Rui
Wang, Hai
Zhang, Xing
Wang, Shufeng
Liu, Chang
Zhang, Jingyao
author_facet Xin, Qi
Xie, Tonghui
Chen, Rui
Wang, Hai
Zhang, Xing
Wang, Shufeng
Liu, Chang
Zhang, Jingyao
author_sort Xin, Qi
collection PubMed
description BACKGROUND: In sepsis patients, Type 2 Diabetes Mellitus (T2DM) was associated with an increased risk of kidney injury. Furthermore, kidney damage is among the dangerous complications, with a high mortality rate in sepsis patients. However, the underlying predictive model on the prediction of major adverse kidney events within 30 days (MAKE30) in sepsis patients with T2DM has not been reported by any study. METHODS: A total of 406 sepsis patients with T2DM were retrospectively enrolled and divided into a non-MAKE30 group (261 cases) and a MAKE30 group (145 cases). In sepsis patients with T2DM, univariate and multivariate logistic regression analyses were conducted to identify independent predictors of MAKE30. Based on the findings of multivariate logistic regression analysis, the corresponding nomogram was constructed. The nomogram was evaluated using the calibration curve, Receiver Operating Characteristic (ROC) curve, and decision curve analysis. A composite of death, new Renal Replacement Therapy (RRT), or Persistent Renal Dysfunction (PRD) comprised MAKE30. Finally, subgroup analyses of the nomogram for 30-day mortality, new RRT, and PRD were performed. RESULTS: In sepsis patients with T2DM, Mean Arterial Pressure (MAP), Platelet (PLT), cystatin C, High-Density Lipoprotein (HDL), and apolipoprotein E (apoE) were independent predictors for MAKE30. According to the ROC curve, calibration curve, and decision curve analysis, the nomogram model based on those predictors had satisfactory discrimination (AUC = 0.916), good calibration, and clinical application. Additionally, in sepsis patients with T2DM, the nomogram model exhibited a high ability to predict the occurrence of 30-day mortality (AUC = 0.822), new RRT (AUC = 0.874), and PRD (AUC = 0.801). CONCLUSION: The nomogram model, which is available within 24 hours after admission, had a robust and accurate assessment for the MAKE30 occurrence, and it provided information to better manage sepsis patients with T2DM.
format Online
Article
Text
id pubmed-9800518
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-98005182022-12-31 Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus Xin, Qi Xie, Tonghui Chen, Rui Wang, Hai Zhang, Xing Wang, Shufeng Liu, Chang Zhang, Jingyao Front Endocrinol (Lausanne) Endocrinology BACKGROUND: In sepsis patients, Type 2 Diabetes Mellitus (T2DM) was associated with an increased risk of kidney injury. Furthermore, kidney damage is among the dangerous complications, with a high mortality rate in sepsis patients. However, the underlying predictive model on the prediction of major adverse kidney events within 30 days (MAKE30) in sepsis patients with T2DM has not been reported by any study. METHODS: A total of 406 sepsis patients with T2DM were retrospectively enrolled and divided into a non-MAKE30 group (261 cases) and a MAKE30 group (145 cases). In sepsis patients with T2DM, univariate and multivariate logistic regression analyses were conducted to identify independent predictors of MAKE30. Based on the findings of multivariate logistic regression analysis, the corresponding nomogram was constructed. The nomogram was evaluated using the calibration curve, Receiver Operating Characteristic (ROC) curve, and decision curve analysis. A composite of death, new Renal Replacement Therapy (RRT), or Persistent Renal Dysfunction (PRD) comprised MAKE30. Finally, subgroup analyses of the nomogram for 30-day mortality, new RRT, and PRD were performed. RESULTS: In sepsis patients with T2DM, Mean Arterial Pressure (MAP), Platelet (PLT), cystatin C, High-Density Lipoprotein (HDL), and apolipoprotein E (apoE) were independent predictors for MAKE30. According to the ROC curve, calibration curve, and decision curve analysis, the nomogram model based on those predictors had satisfactory discrimination (AUC = 0.916), good calibration, and clinical application. Additionally, in sepsis patients with T2DM, the nomogram model exhibited a high ability to predict the occurrence of 30-day mortality (AUC = 0.822), new RRT (AUC = 0.874), and PRD (AUC = 0.801). CONCLUSION: The nomogram model, which is available within 24 hours after admission, had a robust and accurate assessment for the MAKE30 occurrence, and it provided information to better manage sepsis patients with T2DM. Frontiers Media S.A. 2022-12-16 /pmc/articles/PMC9800518/ /pubmed/36589822 http://dx.doi.org/10.3389/fendo.2022.1024500 Text en Copyright © 2022 Xin, Xie, Chen, Wang, Zhang, Wang, Liu and Zhang 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 Endocrinology
Xin, Qi
Xie, Tonghui
Chen, Rui
Wang, Hai
Zhang, Xing
Wang, Shufeng
Liu, Chang
Zhang, Jingyao
Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus
title Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus
title_full Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus
title_fullStr Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus
title_full_unstemmed Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus
title_short Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus
title_sort predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800518/
https://www.ncbi.nlm.nih.gov/pubmed/36589822
http://dx.doi.org/10.3389/fendo.2022.1024500
work_keys_str_mv AT xinqi predictivenomogrammodelformajoradversekidneyeventswithin30daysinsepsispatientswithtype2diabetesmellitus
AT xietonghui predictivenomogrammodelformajoradversekidneyeventswithin30daysinsepsispatientswithtype2diabetesmellitus
AT chenrui predictivenomogrammodelformajoradversekidneyeventswithin30daysinsepsispatientswithtype2diabetesmellitus
AT wanghai predictivenomogrammodelformajoradversekidneyeventswithin30daysinsepsispatientswithtype2diabetesmellitus
AT zhangxing predictivenomogrammodelformajoradversekidneyeventswithin30daysinsepsispatientswithtype2diabetesmellitus
AT wangshufeng predictivenomogrammodelformajoradversekidneyeventswithin30daysinsepsispatientswithtype2diabetesmellitus
AT liuchang predictivenomogrammodelformajoradversekidneyeventswithin30daysinsepsispatientswithtype2diabetesmellitus
AT zhangjingyao predictivenomogrammodelformajoradversekidneyeventswithin30daysinsepsispatientswithtype2diabetesmellitus