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A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment

BACKGROUND: Acute kidney injury (AKI) is not a rare complication during anti-tuberculosis treatment in some patients with pulmonary tuberculosis (PTB). We aimed to develop a risk prediction model for early recognition of patients with PTB at high risk for AKI during anti-TB treatment. METHODS: This...

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Autores principales: Du, Zhi Xiang, Chang, Fang Qun, Wang, Zi Jian, Zhou, Da Ming, Li, Yang, Yang, Jiang Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986302/
https://www.ncbi.nlm.nih.gov/pubmed/35373713
http://dx.doi.org/10.1080/0886022X.2022.2058405
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author Du, Zhi Xiang
Chang, Fang Qun
Wang, Zi Jian
Zhou, Da Ming
Li, Yang
Yang, Jiang Hua
author_facet Du, Zhi Xiang
Chang, Fang Qun
Wang, Zi Jian
Zhou, Da Ming
Li, Yang
Yang, Jiang Hua
author_sort Du, Zhi Xiang
collection PubMed
description BACKGROUND: Acute kidney injury (AKI) is not a rare complication during anti-tuberculosis treatment in some patients with pulmonary tuberculosis (PTB). We aimed to develop a risk prediction model for early recognition of patients with PTB at high risk for AKI during anti-TB treatment. METHODS: This retrospective cohort study assessed the clinical baseline, and laboratory test data of 315 inpatients with active PTB who were screened for predictive factors from January 2019 to June 2020. The elements were analyzed by logistic regression analysis. A nomogram was established by the results of the logistic regression analysis. The prediction model discrimination and calibration were evaluated by the concordance index (C-index), ROC curve, and Hosmer-Lemeshow analysis. RESULTS: A total of 315 patients with PTB were enrolled (67 patients with AKI and 248 patients without AKI). Seven factors, including microalbuminuria, hematuria, cystatin-C (CYS-C), albumin (ALB), creatinine-based estimated glomerular filtration rates (eGFRs), body mass index (BMI), and CA-125 were acquired to develop the predictive model. According to the logistic regression, microalbuminuria (OR = 3.038, 95%CI 1.168–7.904), hematuria (OR = 3.656, 95%CI 1.325–10.083), CYS-C (OR = 4.416, 95%CI 2.296–8.491), and CA-125 (OR = 3.93, 95%CI 1.436–10.756) were risk parameter, while ALB (OR = 0.741, 95%CI 0.650–0.844) was protective parameter. The nomogram demonstrated good prediction in estimating AKI (C-index= 0.967, AUC = 0.967, 95%CI (0.941–0.984), sensitivity = 91.04%, specificity = 93.95%, Hosmer-Lemeshow analysis SD = 0.00054, and quantile of absolute error = 0.049). CONCLUSIONS: Microalbuminuria, hematuria, ALB reduction, elevated CYS-C, and CA-125 are predictive factors for the development of AKI in patients with PTB during anti-TB treatments. The predictive nomogram based on five predictive factors is achieved good risk prediction for AKI during anti-TB treatments.
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spelling pubmed-89863022022-04-07 A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment Du, Zhi Xiang Chang, Fang Qun Wang, Zi Jian Zhou, Da Ming Li, Yang Yang, Jiang Hua Ren Fail Clinical Study BACKGROUND: Acute kidney injury (AKI) is not a rare complication during anti-tuberculosis treatment in some patients with pulmonary tuberculosis (PTB). We aimed to develop a risk prediction model for early recognition of patients with PTB at high risk for AKI during anti-TB treatment. METHODS: This retrospective cohort study assessed the clinical baseline, and laboratory test data of 315 inpatients with active PTB who were screened for predictive factors from January 2019 to June 2020. The elements were analyzed by logistic regression analysis. A nomogram was established by the results of the logistic regression analysis. The prediction model discrimination and calibration were evaluated by the concordance index (C-index), ROC curve, and Hosmer-Lemeshow analysis. RESULTS: A total of 315 patients with PTB were enrolled (67 patients with AKI and 248 patients without AKI). Seven factors, including microalbuminuria, hematuria, cystatin-C (CYS-C), albumin (ALB), creatinine-based estimated glomerular filtration rates (eGFRs), body mass index (BMI), and CA-125 were acquired to develop the predictive model. According to the logistic regression, microalbuminuria (OR = 3.038, 95%CI 1.168–7.904), hematuria (OR = 3.656, 95%CI 1.325–10.083), CYS-C (OR = 4.416, 95%CI 2.296–8.491), and CA-125 (OR = 3.93, 95%CI 1.436–10.756) were risk parameter, while ALB (OR = 0.741, 95%CI 0.650–0.844) was protective parameter. The nomogram demonstrated good prediction in estimating AKI (C-index= 0.967, AUC = 0.967, 95%CI (0.941–0.984), sensitivity = 91.04%, specificity = 93.95%, Hosmer-Lemeshow analysis SD = 0.00054, and quantile of absolute error = 0.049). CONCLUSIONS: Microalbuminuria, hematuria, ALB reduction, elevated CYS-C, and CA-125 are predictive factors for the development of AKI in patients with PTB during anti-TB treatments. The predictive nomogram based on five predictive factors is achieved good risk prediction for AKI during anti-TB treatments. Taylor & Francis 2022-04-04 /pmc/articles/PMC8986302/ /pubmed/35373713 http://dx.doi.org/10.1080/0886022X.2022.2058405 Text en © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Study
Du, Zhi Xiang
Chang, Fang Qun
Wang, Zi Jian
Zhou, Da Ming
Li, Yang
Yang, Jiang Hua
A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment
title A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment
title_full A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment
title_fullStr A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment
title_full_unstemmed A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment
title_short A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment
title_sort risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment
topic Clinical Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8986302/
https://www.ncbi.nlm.nih.gov/pubmed/35373713
http://dx.doi.org/10.1080/0886022X.2022.2058405
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