已发表论文

一种用于区分肾结核与非结核性肾感染的新诺模图

 

Authors Liu P, Qin Y

Received 27 January 2025

Accepted for publication 2 July 2025

Published 11 July 2025 Volume 2025:18 Pages 3471—3479

DOI https://doi.org/10.2147/IDR.S515231

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Oliver Planz

Pengju Liu, Yong Qin

Department of Urology, Hangzhou Red Cross Hospital, Hangzhou, People’s Republic of China

Correspondence: Yong Qin, Department of Urology, Hangzhou Red Cross Hospital, 208 East Huancheng Road, Hangzhou, 310003, People’s Republic of China, Tel +86571-56108764, Email hhyyqinyong@163.com

Background: To build a diagnostic nomogram for differentiating between renal tuberculosis (RTB) and nontuberculous renal infection.
Methods: Eligible patients were randomly categorized into derivation and validation cohorts (7:3). Univariate and multivariate regression analyses were conducted to filter variables and select predictors. Multivariate logistic regression was employed for model construction and nomogram were used for visualization. The nomogram was evaluated by Concordance index (C-index), calibration curves and decision curve analysis (DCA).
Results: Overall, 194 patients were included. The derivation and validation cohorts included 75 and 61 patients and 32 and 26 patients with RTB and nontuberculous renal infection, respectively. We included previous TB history, CRP levels, fever, chronic infection and hydronephrosis in the construction of the nomogram. A nomogram was developed and validated. This nomogram exhibited good discrimination and calibration. The C-indices of this nomogram in the derivation and validation cohorts was 0.99 and 0.98 (95% confidence intervals, 0.97– 1.00 and 0.96– 1.01), respectively. DCA revealed that the proposed nomogram was useful for the differentiation.
Conclusion: The nomogram can differentiate between RTB and nontuberculous renal infection.

Keywords: diagnostic nomogram, renal tuberculosis, nontuberculous renal infection, decision curve analysis