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1000 Titel
  • Predictive Model for Systemic Infection After Percutaneous Nephrolithotomy and Related Factors Analysis
1000 Autor/in
  1. Tang, Yiming |
  2. Zhang, Chi |
  3. Mo, Chengqiang |
  4. Gui, Chengpeng |
  5. Luo, Junhang |
  6. Wu, Rongpei |
1000 Erscheinungsjahr 2021
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-07-23
1000 Erschienen in
1000 Quellenangabe
  • 8:696463
1000 Copyrightjahr
  • 2021
1000 Embargo
  • 2022-01-25
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.3389/fsurg.2021.696463 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342809/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Abstract/Summary
  • <jats:p><jats:bold>Objectives:</jats:bold> 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.</jats:p><jats:p><jats:bold>Methods:</jats:bold> 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).</jats:p><jats:p><jats:bold>Results:</jats:bold> 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], <jats:italic>p</jats:italic> = 0.014), S.T.O.N.E. score (OR = 1.902, 95% CI [1.473–2.457], <jats:italic>p</jats:italic> &amp;lt; 0.001), female gender (OR = 2.545, 95% CI [1.563–4.144], <jats:italic>p</jats:italic> &amp;lt; 0.001), diabetes history (OR = 1.987, 95% CI [1.051–3.755], <jats:italic>p</jats:italic> = 0.035), positive urine culture (OR = 3.184, 95% CI [1.697–5.974], <jats:italic>p</jats:italic> &amp;lt; 0.001). And there were four factors related to urosepsis, followed by preoperative NLR (OR = 1.604, 95% CI [1.135–2.266], <jats:italic>p</jats:italic> = 0.007), S.T.O.N.E. score (OR = 1.455, 95% CI [1.064–1.988], <jats:italic>p</jats:italic> = 0.019), female gender (OR = 2.08, 95% CI [1.063–4.07], <jats:italic>p</jats:italic> = 0.032), positive urine culture (OR = 2.827, 95% CI [1.266–6.313], <jats:italic>p</jats:italic> = 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 (<jats:italic>p</jats:italic> = 0.953, <jats:italic>p</jats:italic> = 0.872). The area under the ROC curve was 0.784 (SIRS) and 0.772 (urosepsis) respectively.</jats:p><jats:p><jats:bold>Conclusion:</jats:bold> 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.</jats:p>
1000 Sacherschließung
lokal urolithiasis
lokal prognostic factors
lokal percutaneous nephrolithotomy
lokal Surgery
lokal systemic inflammatory response syndrome
lokal urosepsis
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/VGFuZywgWWltaW5n|https://frl.publisso.de/adhoc/uri/WmhhbmcsIENoaQ==|https://frl.publisso.de/adhoc/uri/TW8sIENoZW5ncWlhbmc=|https://frl.publisso.de/adhoc/uri/R3VpLCBDaGVuZ3Blbmc=|https://frl.publisso.de/adhoc/uri/THVvLCBKdW5oYW5n|https://frl.publisso.de/adhoc/uri/V3UsIFJvbmdwZWk=
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  1. National Natural Science Foundation of China-Guangdong Joint Fund |
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    1000 Förderer National Natural Science Foundation of China-Guangdong Joint Fund |
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