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A simple nomogram for predicting infectious diseases in adult kidney transplantation recipients

OBJECTIVE: To investigate the risk factors of infectious diseases in adult kidney transplantation recipients and to establish a simple and novel nomogram to guide the prophylactic antimicrobial therapy. METHODS: Patients who received kidney transplantation between January 2018 and October 2021 were...

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Autores principales: Chen, Ruo-Yang, Zhang, Sheng, Zhuang, Shao-Yong, Li, Da-Wei, Zhang, Ming, Zhu, Cheng, Yu, Yue-Tian, Yuan, Xiao-Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9471136/
https://www.ncbi.nlm.nih.gov/pubmed/36117592
http://dx.doi.org/10.3389/fpubh.2022.944137
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author Chen, Ruo-Yang
Zhang, Sheng
Zhuang, Shao-Yong
Li, Da-Wei
Zhang, Ming
Zhu, Cheng
Yu, Yue-Tian
Yuan, Xiao-Dong
author_facet Chen, Ruo-Yang
Zhang, Sheng
Zhuang, Shao-Yong
Li, Da-Wei
Zhang, Ming
Zhu, Cheng
Yu, Yue-Tian
Yuan, Xiao-Dong
author_sort Chen, Ruo-Yang
collection PubMed
description OBJECTIVE: To investigate the risk factors of infectious diseases in adult kidney transplantation recipients and to establish a simple and novel nomogram to guide the prophylactic antimicrobial therapy. METHODS: Patients who received kidney transplantation between January 2018 and October 2021 were included in the study and were divided into a training and a testing set at a 1:1 ratio. Risk factors correlated to infectious diseases were selected using a Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The prediction model was built by incorporating the variables selected by the LASSO model into a logistic regression equation. Calibration curves and receiver operating characteristic (ROC) curves were also applied to assess the model calibration and discrimination. A nomogram consisting of the selected factors was established to provide individualized risks of developing infections. Decision curve analysis (DCA) was adopted to estimate the net benefit and reduction in interventions for a range of clinically reasonable risk thresholds. RESULTS: In all, 863 adult kidney recipients were included in the study, and 407 (47.16%) of them developed infectious diseases during the 3-year follow–up period. A total of 8 variables were selected using LASSO regression and were retained for subsequent model construction and infection prediction. The area under the curve (AUC) was 0.83 and 0.81 in the training and testing sets, with high F scores of 0.76 and 0.77, sensitivity of 0.76 and 0.81, and specificity of 0.88 and 0.74, respectively. A novel nomogram was developed based on 8 selected predictors (requirement for albumin infusion, requirement for red blood cell infusion, triglyceride, uric acid, creatinine, globulin, neutrophil percentage, and white blood cells). The net benefit indicated that the nomogram would reduce unnecessary interventions at a wide range of threshold probabilities in both sets. CONCLUSIONS: Adult kidney transplantation recipients are high-risk hosts for infectious diseases. The novel nomogram consisting of 8 factors reveals good predictive performance and may promote the reasonable antimicrobial prescription. More external validations are required to confirm its effectiveness for further clinical application.
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spelling pubmed-94711362022-09-15 A simple nomogram for predicting infectious diseases in adult kidney transplantation recipients Chen, Ruo-Yang Zhang, Sheng Zhuang, Shao-Yong Li, Da-Wei Zhang, Ming Zhu, Cheng Yu, Yue-Tian Yuan, Xiao-Dong Front Public Health Public Health OBJECTIVE: To investigate the risk factors of infectious diseases in adult kidney transplantation recipients and to establish a simple and novel nomogram to guide the prophylactic antimicrobial therapy. METHODS: Patients who received kidney transplantation between January 2018 and October 2021 were included in the study and were divided into a training and a testing set at a 1:1 ratio. Risk factors correlated to infectious diseases were selected using a Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The prediction model was built by incorporating the variables selected by the LASSO model into a logistic regression equation. Calibration curves and receiver operating characteristic (ROC) curves were also applied to assess the model calibration and discrimination. A nomogram consisting of the selected factors was established to provide individualized risks of developing infections. Decision curve analysis (DCA) was adopted to estimate the net benefit and reduction in interventions for a range of clinically reasonable risk thresholds. RESULTS: In all, 863 adult kidney recipients were included in the study, and 407 (47.16%) of them developed infectious diseases during the 3-year follow–up period. A total of 8 variables were selected using LASSO regression and were retained for subsequent model construction and infection prediction. The area under the curve (AUC) was 0.83 and 0.81 in the training and testing sets, with high F scores of 0.76 and 0.77, sensitivity of 0.76 and 0.81, and specificity of 0.88 and 0.74, respectively. A novel nomogram was developed based on 8 selected predictors (requirement for albumin infusion, requirement for red blood cell infusion, triglyceride, uric acid, creatinine, globulin, neutrophil percentage, and white blood cells). The net benefit indicated that the nomogram would reduce unnecessary interventions at a wide range of threshold probabilities in both sets. CONCLUSIONS: Adult kidney transplantation recipients are high-risk hosts for infectious diseases. The novel nomogram consisting of 8 factors reveals good predictive performance and may promote the reasonable antimicrobial prescription. More external validations are required to confirm its effectiveness for further clinical application. Frontiers Media S.A. 2022-08-31 /pmc/articles/PMC9471136/ /pubmed/36117592 http://dx.doi.org/10.3389/fpubh.2022.944137 Text en Copyright © 2022 Chen, Zhang, Zhuang, Li, Zhang, Zhu, Yu and Yuan. 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 Public Health
Chen, Ruo-Yang
Zhang, Sheng
Zhuang, Shao-Yong
Li, Da-Wei
Zhang, Ming
Zhu, Cheng
Yu, Yue-Tian
Yuan, Xiao-Dong
A simple nomogram for predicting infectious diseases in adult kidney transplantation recipients
title A simple nomogram for predicting infectious diseases in adult kidney transplantation recipients
title_full A simple nomogram for predicting infectious diseases in adult kidney transplantation recipients
title_fullStr A simple nomogram for predicting infectious diseases in adult kidney transplantation recipients
title_full_unstemmed A simple nomogram for predicting infectious diseases in adult kidney transplantation recipients
title_short A simple nomogram for predicting infectious diseases in adult kidney transplantation recipients
title_sort simple nomogram for predicting infectious diseases in adult kidney transplantation recipients
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9471136/
https://www.ncbi.nlm.nih.gov/pubmed/36117592
http://dx.doi.org/10.3389/fpubh.2022.944137
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