# Establishment and validation of a risk prediction model for urinary tract infection in elderly patients with type 2 diabetes mellitus

## TL;DR

This study developed a risk prediction model to help identify elderly type 2 diabetes patients at higher risk of urinary tract infections.

## Contribution

A validated nomogram model was created to predict UTI risk in elderly T2DM patients using clinical factors.

## Key findings

- Six key predictors of UTI were identified, including HbA1c ≥ 6.5%, age ≥ 65 years, and duration of diabetes ≥ 10 years.
- The nomogram showed strong predictive performance with a C-index of 0.855 in the training set and 0.825 in the validation set.
- The model supports personalized care by enabling early clinical decision-making and proactive prevention strategies.

## Abstract

This study aimed to identify the risk factors for urinary tract infection (UTI) in elderly patients with type 2 diabetes mellitus (T2DM) and to develop and validate a nomogram that predicts the probability of UTI based on these factors.

We collected clinical data from patients with diabetes who were aged 60 years or older. These patients were then divided into a modeling population (n=281) and an internal validation population (n=121) based on the principle of random assignment. LASSO regression analysis was conducted using the modeling population to identify the independent risk factors for UTI in elderly patients with T2DM. Logistics univariate and multifactor regressions were performed by the screened influencing factors, and then column line graph prediction models for UTI in elderly patients with T2DM were made by these influencing factors, using receiver operating characteristic curve and area under curve, C-index validation, and calibration curve to initially evaluate the model discrimination and calibration. Model validation was performed by the internal validation set, and the ROC curve, C-index and calibration curve were used to further evaluate the column line graph model performance. Finally, using DCA (decision curve analysis), we observed whether the model could be used better in clinical settings.

The study enrolled a total of 402 patients with T2DM, of which 281 were in the training cohort, and 70 of these patients had UTI. Six key predictors of UTI were identified: “HbA1c ≥ 6.5%” (OR, 1.929; 95%CI, 1.565-3.119; P =0.045), “Age ≥ 65y” (OR, 3.170; 95% CI, 1.507-6.930; P=0.003), “DOD ≥ 10y” (OR, 2.533; 95% CI, 1.727-3.237; P = 0.036), “FPG” (OR, 2.527; 95% CI, 1.944-3.442; P = 0.000), “IUC” (OR, 2.633; 95%CI, 1.123-6.289; P = 0.027), and “COD” (OR, 1.949; 95%CI, 1.623-3.889; P = 0.041). The nomogram demonstrated a high predictive capability with a C-index of 0.855 (95% CI, 0.657-0.976) in the development set and 0.825 (95% CI, 0.568-0.976) in the validation set.

Our nomogram, incorporating factors such as “HbA1c ≥ 6.5%,” “Age ≥ 65y”, “FPG”, “DOD ≥ 10y”, “COD”, and “IUC”, provides a valuable tool for predicting UTI in elderly patients with T2DM. It offers the potential for enhanced early clinical decision-making and proactive prevention and treatment, reflecting a shift towards more personalized patient care.