Independent validation of a predictive nomogram for risk of reinfection in women with recurrent non-complicated urinary tract infections

Recurrent UTI in Women-Risk Factors and Management

Urinary tract infections (UTIs) are common in women; more than 50% of women will be diagnosed with a UTI in her lifetime. Many of these women will go on to develop recurrent UTI. Nevertheless, evidence-based prevention of recurrent UTI is under-utilized. Here, the authors provide detailed practical advice on UTI prevention with a thorough review of the evidence. Non-antibiotic prevention measures discussed include increased fluid intake, vaginal estrogen therapy, methenamine, and cranberry. Antibiotic prophyalxis for carefully selected patients is also discussed.

Nomogram Model for Predicting the Risk of Multidrug-Resistant Bacteria Infection in Diabetic Foot Patients

Objective

This study established an individualized nomogram for predicting the risk of multidrug-resistant bacterial (MDRB) infection in patients with the diabetic foot (DF), and providing a reference for clinical prevention and treatment.

Methods

A total of 199 DF patients admitted to the hospital from July 2015 to December 2018 were included in this study. The pathogenic bacteria at the site of infection were detected and the factors affecting the occurrence of MDRB infection in DF patients summarized. The R software was used to draw the nomogram, and the Bootstrap Method used to internally verify the model. The calibration curve and the Harrell’s Concordance Index (C-index) were used to evaluate the predictive effect of the nomogram model.

Results

Logistic regression analysis showed that age, course of diabetes, previous use of antibacterial drugs, types of antibacterial drugs, and osteoporosis were risk factors for multidrug-resistant infections in DF (P<0.05). The area under the receiver operating characteristic curve (AUC, Area Under Curve) of the nomogram model after internal verification was 0.773 (95% CI: 0.704–0.830). The mean absolute error between the predicted probability of infection in the nomogram and the actual occurrence of MDRB was 0.032, indicating that the nomogram model had good forecasting efficiency and stability.

Conclusion

The risk factors for multidrug-resistant infections in DF are age, course of diabetes, previous use of antibacterial drugs, types of antibacterial drugs used, and osteoporosis. The nomogram model drawn on these risk factors has good predictive accuracy and can assist medical staff in formulating targeted infection prevention strategies for patients.