Establishment and validation of a risk prediction model for urinary tract infection in elderly patients with type 2 diabetes mellitus
ObjectivesThis 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.MethodsWe collected clinical data from patients with d...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1557185/full |
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| Summary: | ObjectivesThis 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.MethodsWe 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.ResultsThe 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.ConclusionsOur 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. |
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| ISSN: | 1664-2392 |