A risk prediction model of urinary tract infections for patients with neurogenic bladder

Abstract

Objectives: To develop a nomogram to evaluate the risk of urinary tract infections (UTI) in patients with neurogenic bladder (NGB)

Methods: A retrospective analysis was conducted on 337 patients with NGB admitted to three hospitals. Collected data included clinical symptoms, patients’ general characteristics, laboratory examinations and imaging findings. Multivariate logistic regression analysis was conducted to develop the risk prediction nomogram of UTIs for NGB patients. C index was used for the internal and external validation of that model.

Results: The occurrence of UTIs was 45.7% (154 of 337), 52.6% (102 of 194), and 36.4% (52 of 143) in the overall, training and validation data sets, respectively. The prediction nomogram was developed with 5 prognostic factors which included white blood cell (WBC) in blood, Leukocyte (LEU) in urine, Urinary pH, length of stay and urination mode. The nomogram presented good discrimination with a C-index value of 0.921 (95% confidence interval: 0.87396 − 0.96804) and good calibration. The C-index values of the interval validation and external validation were 0.8905541 and 0.817, respectively. The results of decision curve analysis (DCA) demonstrated that the model was clinically useful.

Conclusions: The prediction nomogram we developed is a simple and accurate tool for early prediction of UTIs in patients with NGB. This tool can assess risk of UTIs early, allowing for timely initiation of appropriate preventive measures.

Keywords:

• Neurogenic bladder
• urinary tract infections
• risk
• prediction model

Disclosure statement

The authors have stated explicitly that there are no conflicts of interest in connection with this article.

Additional information

Funding

This work was supported by grants from the Natural Science Foundation of Guangdong Province China (No. 2016A030313623), the Breeding Project of Clinical Research of Southern Medical University (No. LC2016PY060) the Shenzhen Healthcare Research Project (No. SZXJ2018069) and three famous medical and health projects in Shenzhen (SZSM201612018).