Identification of early predictors and model for bacterial infection in diabetic ketoacidosis patients: A retrospective study.

<h4>Purpose</h4>The purpose of this report was to identify effective indicators capable of predicting bacterial infection during the early stages of diabetic ketoacidosis (DKA) and to establish a diagnostic model suitable for clinical application.<h4>Methods</h4>This was a re...

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Main Authors: Yaping Hao, Lei Yang, Xiaomei Meng, Yuxiao Tang, Liang Wang
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0318261
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author Yaping Hao
Lei Yang
Xiaomei Meng
Yuxiao Tang
Liang Wang
author_facet Yaping Hao
Lei Yang
Xiaomei Meng
Yuxiao Tang
Liang Wang
author_sort Yaping Hao
collection DOAJ
description <h4>Purpose</h4>The purpose of this report was to identify effective indicators capable of predicting bacterial infection during the early stages of diabetic ketoacidosis (DKA) and to establish a diagnostic model suitable for clinical application.<h4>Methods</h4>This was a retrospective cross-sectional study. Between February 2018 and May 2023, Yuhuangding Hospital admitted 101 DKA patients, of whom 45 were diagnosed with bacterial infections. A confirmed bacterial infection was defined as documented bacteriological evidence in any bacterial sample. Clinical parameters and biological markers (including cortisol, C-reactive protein (CRP), procalcitonin, etc.) were recorded during the initial DKA phase. Multivariate regression analysis was employed to construct a diagnostic model.<h4>Results</h4>CRP (OR = 1.014, 95% CI: 1.002-1.026, p = 0.017) and cortisol (OR = 1.007, 95% CI: 1.002-1.012, p = 0.003) were found to have an independent association with bacterial infection in DKA patients. The area under the receiver operating characteristic curve (AUC) for CRP in identifying bacterial infection was 0.855 (95% CI, 0.771-0.917), with a sensitivity of 76.1% and a specificity of 83.6%. The AUC for cortisol in identifying bacterial infection was 0.847 (95% CI, 0.761-0.911), with a sensitivity of 71.7% and a specificity of 89.1%. A joint diagnostic model based on cortisol and CRP was developed through multifactor regression analysis. The AUC of this diagnostic model was 0.930 (95% CI, 0.862-0.972), resulting in a sensitivity of 93.5% and a specificity of 80.0%.<h4>Conclusion</h4>CRP and cortisol are early indicators of bacterial infection in DKA patients. Furthermore, based on their combination, the regression diagnostic model exhibits enhanced diagnostic performance.
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spelling doaj-art-13ffbdd2b5b940ba9b43d501ea5e2b4b2025-08-20T02:28:16ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031826110.1371/journal.pone.0318261Identification of early predictors and model for bacterial infection in diabetic ketoacidosis patients: A retrospective study.Yaping HaoLei YangXiaomei MengYuxiao TangLiang Wang<h4>Purpose</h4>The purpose of this report was to identify effective indicators capable of predicting bacterial infection during the early stages of diabetic ketoacidosis (DKA) and to establish a diagnostic model suitable for clinical application.<h4>Methods</h4>This was a retrospective cross-sectional study. Between February 2018 and May 2023, Yuhuangding Hospital admitted 101 DKA patients, of whom 45 were diagnosed with bacterial infections. A confirmed bacterial infection was defined as documented bacteriological evidence in any bacterial sample. Clinical parameters and biological markers (including cortisol, C-reactive protein (CRP), procalcitonin, etc.) were recorded during the initial DKA phase. Multivariate regression analysis was employed to construct a diagnostic model.<h4>Results</h4>CRP (OR = 1.014, 95% CI: 1.002-1.026, p = 0.017) and cortisol (OR = 1.007, 95% CI: 1.002-1.012, p = 0.003) were found to have an independent association with bacterial infection in DKA patients. The area under the receiver operating characteristic curve (AUC) for CRP in identifying bacterial infection was 0.855 (95% CI, 0.771-0.917), with a sensitivity of 76.1% and a specificity of 83.6%. The AUC for cortisol in identifying bacterial infection was 0.847 (95% CI, 0.761-0.911), with a sensitivity of 71.7% and a specificity of 89.1%. A joint diagnostic model based on cortisol and CRP was developed through multifactor regression analysis. The AUC of this diagnostic model was 0.930 (95% CI, 0.862-0.972), resulting in a sensitivity of 93.5% and a specificity of 80.0%.<h4>Conclusion</h4>CRP and cortisol are early indicators of bacterial infection in DKA patients. Furthermore, based on their combination, the regression diagnostic model exhibits enhanced diagnostic performance.https://doi.org/10.1371/journal.pone.0318261
spellingShingle Yaping Hao
Lei Yang
Xiaomei Meng
Yuxiao Tang
Liang Wang
Identification of early predictors and model for bacterial infection in diabetic ketoacidosis patients: A retrospective study.
PLoS ONE
title Identification of early predictors and model for bacterial infection in diabetic ketoacidosis patients: A retrospective study.
title_full Identification of early predictors and model for bacterial infection in diabetic ketoacidosis patients: A retrospective study.
title_fullStr Identification of early predictors and model for bacterial infection in diabetic ketoacidosis patients: A retrospective study.
title_full_unstemmed Identification of early predictors and model for bacterial infection in diabetic ketoacidosis patients: A retrospective study.
title_short Identification of early predictors and model for bacterial infection in diabetic ketoacidosis patients: A retrospective study.
title_sort identification of early predictors and model for bacterial infection in diabetic ketoacidosis patients a retrospective study
url https://doi.org/10.1371/journal.pone.0318261
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