| Literature DB >> 35127802 |
Xun Lu1,2, Hua Jiang1,2, Dong Wang1,2, Yiduo Wang1,2, Qi Chen2,3, Shuqiu Chen1,2, Ming Chen1,2.
Abstract
PURPOSE: To develop and validate a nomogram of the 90-day urinary tract infection (UTI) risk for patients with bladder cancer undergoing radical cystectomy (RC) and urinary diversion. PATIENTS AND METHODS: The predictive nomogram was based on a retrospective study on the consecutive patients who underwent RC and urinary diversion for bladder cancer between January 2014 and March 2021. The incidence and microbiology of UTI were reported. The univariate and multivariate logistic analyses were conducted to determine independent risk factors associated with UTI. The predictive accuracy and discriminatory ability of the established nomogram were evaluated by the concordance index (C-index) and decision curve analysis (DCA). The performance of the model was validated internally.Entities:
Keywords: nomogram; prognostic nutritional index; risk factor; urinary diversion; urinary tract infection
Year: 2022 PMID: 35127802 PMCID: PMC8814316 DOI: 10.3389/fsurg.2021.782029
Source DB: PubMed Journal: Front Surg ISSN: 2296-875X
Figure 1The flowchart of protocol in the study.
Figure 2Microbiology of urinary tract infection (UTI) within 90 days after radical cystectomy (RC) and urinary diversion. (A) Identified pathogens among patients in the study. (B) Antibiotic resistance of the identified bacteria.
Figure 3The predictive nomogram of incidence of UTI within 90 days in patients with bladder cancer underwent RC and the urinary diversion.
Figure 4Receiver operating characteristic (ROC) and decision curve analysis (DCA) curve in the training and validation cohort. (A) The ROC cure in the training cohort. (B) The ROC curve in the validation cohort. (C) The DCA cure in the training cohort. (D) The DCA curve in the validation cohort.
Figure 5The calibration plot in the training and validation cohort. (A) The calibration plot in the training cohort. (B) The calibration plot in the training cohort.
A comparison of discriminatory ability of model B and model C with model A using NRI and IDI in the training and validation cohort.
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| B vs. A | −7.4% | 0.325 | 2.0% | 0.097 | 11.6% | 0.243 | 17.7% | <0.001 |
| C vs. A | 14.9% | 0.102 | 10.7% | <0.001 | 22.4% | 0.046 | 21.7% | <0.001 |
| C vs. B | 22.3% | 0.006 | 8.7% | <0.001 | 10.8% | 0.344 | 4.0% | 0.114 |
Model A, UD + Stricture; Model B, UD + Stricture + CCI; Model C, UD + Stricture + CCI + PNI.
NRI, Net Reclassification Improvement; IDI, Integrated Discrimination Improvement.