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Predictive Model for Systemic Infection After Percutaneous Nephrolithotomy and Related Factors Analysis

Objectives: To investigate the factors associated with systemic infection after percutaneous nephrolithotomy (PCNL) and establish a predictive model to provide theoretical basis for the prevention of systemic inflammatory response syndrome (SIRS) and urosepsis correlate to percutaneous nephrostomy....

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Autores principales: Tang, Yiming, Zhang, Chi, Mo, Chengqiang, Gui, Chengpeng, Luo, Junhang, Wu, Rongpei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342809/
https://www.ncbi.nlm.nih.gov/pubmed/34368217
http://dx.doi.org/10.3389/fsurg.2021.696463
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author Tang, Yiming
Zhang, Chi
Mo, Chengqiang
Gui, Chengpeng
Luo, Junhang
Wu, Rongpei
author_facet Tang, Yiming
Zhang, Chi
Mo, Chengqiang
Gui, Chengpeng
Luo, Junhang
Wu, Rongpei
author_sort Tang, Yiming
collection PubMed
description Objectives: To investigate the factors associated with systemic infection after percutaneous nephrolithotomy (PCNL) and establish a predictive model to provide theoretical basis for the prevention of systemic inflammatory response syndrome (SIRS) and urosepsis correlate to percutaneous nephrostomy. Methods: Patients received PCNL between January 2016 and December 2020 were retrospectively enrolled. All patients were categorized into groups according to postoperative SIRS and urosepsis status. Single factor analysis and multivariate logistic regression analysis were performed to determine the predictive factors of SIRS and urosepsis after PCNL. The nomograms were generated using the predictors respectively and the discriminative ability of was assessed by analyses of receiver operating characteristic curves (ROC curves). Results: A total of 758 PCNL patients were enrolled in this study, including 97 (12.8%) patients with SIRS and 42 (5.5%) patients with urosepsis. Multivariate logistic regression analysis suggested that there were 5 factors related to SIRS, followed by preoperative neutrophil to lymphocyte ratio (NLR) (odds ratio, OR = 1.721, 95% confidence interval, CI [1.116–2.653], p = 0.014), S.T.O.N.E. score (OR = 1.902, 95% CI [1.473–2.457], p < 0.001), female gender (OR = 2.545, 95% CI [1.563–4.144], p < 0.001), diabetes history (OR = 1.987, 95% CI [1.051–3.755], p = 0.035), positive urine culture (OR = 3.184, 95% CI [1.697–5.974], p < 0.001). And there were four factors related to urosepsis, followed by preoperative NLR (OR = 1.604, 95% CI [1.135–2.266], p = 0.007), S.T.O.N.E. score (OR = 1.455, 95% CI [1.064–1.988], p = 0.019), female gender (OR = 2.08, 95% CI [1.063–4.07], p = 0.032), positive urine culture (OR = 2.827, 95% CI [1.266–6.313], p = 0.011). A nomogram prediction model was established to calculate the cumulative probability of SIRS and urosepsis after PCNL and displayed favorable fitting by Hosmer–Lemeshow test (p = 0.953, p = 0.872). The area under the ROC curve was 0.784 (SIRS) and 0.772 (urosepsis) respectively. Conclusion: Higher preoperative NLR, higher S.T.O.N.E. score, female gender, and positive urine culture are the most significant predictors of SIRS and urosepsis. Diabetes history is the predictor of SIRS. These data will help identify high-risk individuals and facilitate early detection of SIRS and urosepsis post-PCNL.
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spelling pubmed-83428092021-08-07 Predictive Model for Systemic Infection After Percutaneous Nephrolithotomy and Related Factors Analysis Tang, Yiming Zhang, Chi Mo, Chengqiang Gui, Chengpeng Luo, Junhang Wu, Rongpei Front Surg Surgery Objectives: To investigate the factors associated with systemic infection after percutaneous nephrolithotomy (PCNL) and establish a predictive model to provide theoretical basis for the prevention of systemic inflammatory response syndrome (SIRS) and urosepsis correlate to percutaneous nephrostomy. Methods: Patients received PCNL between January 2016 and December 2020 were retrospectively enrolled. All patients were categorized into groups according to postoperative SIRS and urosepsis status. Single factor analysis and multivariate logistic regression analysis were performed to determine the predictive factors of SIRS and urosepsis after PCNL. The nomograms were generated using the predictors respectively and the discriminative ability of was assessed by analyses of receiver operating characteristic curves (ROC curves). Results: A total of 758 PCNL patients were enrolled in this study, including 97 (12.8%) patients with SIRS and 42 (5.5%) patients with urosepsis. Multivariate logistic regression analysis suggested that there were 5 factors related to SIRS, followed by preoperative neutrophil to lymphocyte ratio (NLR) (odds ratio, OR = 1.721, 95% confidence interval, CI [1.116–2.653], p = 0.014), S.T.O.N.E. score (OR = 1.902, 95% CI [1.473–2.457], p < 0.001), female gender (OR = 2.545, 95% CI [1.563–4.144], p < 0.001), diabetes history (OR = 1.987, 95% CI [1.051–3.755], p = 0.035), positive urine culture (OR = 3.184, 95% CI [1.697–5.974], p < 0.001). And there were four factors related to urosepsis, followed by preoperative NLR (OR = 1.604, 95% CI [1.135–2.266], p = 0.007), S.T.O.N.E. score (OR = 1.455, 95% CI [1.064–1.988], p = 0.019), female gender (OR = 2.08, 95% CI [1.063–4.07], p = 0.032), positive urine culture (OR = 2.827, 95% CI [1.266–6.313], p = 0.011). A nomogram prediction model was established to calculate the cumulative probability of SIRS and urosepsis after PCNL and displayed favorable fitting by Hosmer–Lemeshow test (p = 0.953, p = 0.872). The area under the ROC curve was 0.784 (SIRS) and 0.772 (urosepsis) respectively. Conclusion: Higher preoperative NLR, higher S.T.O.N.E. score, female gender, and positive urine culture are the most significant predictors of SIRS and urosepsis. Diabetes history is the predictor of SIRS. These data will help identify high-risk individuals and facilitate early detection of SIRS and urosepsis post-PCNL. Frontiers Media S.A. 2021-07-23 /pmc/articles/PMC8342809/ /pubmed/34368217 http://dx.doi.org/10.3389/fsurg.2021.696463 Text en Copyright © 2021 Tang, Zhang, Mo, Gui, Luo and Wu. 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 Surgery
Tang, Yiming
Zhang, Chi
Mo, Chengqiang
Gui, Chengpeng
Luo, Junhang
Wu, Rongpei
Predictive Model for Systemic Infection After Percutaneous Nephrolithotomy and Related Factors Analysis
title Predictive Model for Systemic Infection After Percutaneous Nephrolithotomy and Related Factors Analysis
title_full Predictive Model for Systemic Infection After Percutaneous Nephrolithotomy and Related Factors Analysis
title_fullStr Predictive Model for Systemic Infection After Percutaneous Nephrolithotomy and Related Factors Analysis
title_full_unstemmed Predictive Model for Systemic Infection After Percutaneous Nephrolithotomy and Related Factors Analysis
title_short Predictive Model for Systemic Infection After Percutaneous Nephrolithotomy and Related Factors Analysis
title_sort predictive model for systemic infection after percutaneous nephrolithotomy and related factors analysis
topic Surgery
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342809/
https://www.ncbi.nlm.nih.gov/pubmed/34368217
http://dx.doi.org/10.3389/fsurg.2021.696463
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