Create a predictive model for neurogenic bladder patients: upper urinary tract damage predictive nomogram.

Objective: To create a nomogram to evaluate the risk of upper urinary tract damage (UUTD) in patients with neurogenic bladder (NGB) Methods: A retrospective analysis was conducted on 301 patients with NGB who were admitted to certain hospitals. Data collected included clinical symptoms, patients' characteristics, laboratory parameters, imaging findings and urodynamic parameters. The least absolute shrinkage and selection operator(LASSO)regression model was used to optimize the selection of predictors. Multivariate logistic regression analysis was performed to develop a UUTD risk predictive model. Validation was performed by bootstrap. Results: The predictors included in the nomogram included sex, duration of disease, history of UTI, bladder compliance, and fecal incontinence. The model presented good discrimination with a C-index value of 0.796 (95% confidence interval: 0.74896-0.84304) and good calibration. The C-index value of the interval validation was 0.7872112. The results of decision curve analysis (DCA) demonstrated that the UUTD-risk predictive nomogram was clinically useful. Conclusion: The nomogram incorporating the sex, duration of disease, history of UTI, bladder compliance, and fecal incontinence could be an important tool of UUTD risk prediction in NGB patients.

The International journal of neuroscience. 2019 Aug 10 [Epub ahead of print]

Wenqiang Wang, Hengying Fang, Peng Xie, Qunduo Cao, Ling He, Wenzhi Cai

a Department of Nursing, Shenzhen Hospital, Southern Medical University , Shenzhen , China., b Department of Nursing, the Third Affiliated Hospital of Sun Yat-Sen University , Guangzhou , China., c Department of Critical Care Medicine, Nanchong Central Hospital, the Second Clinical Medical College of North Sichuan Medical College , Nanchong , China ., d Department of Urology, Peking University Shenzhen Hospital , Shenzhen , China., e Department of radiation oncology department, Nanfang hospital, southern medical university , Guangzhou , China .

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