Evidence mapPaperPMID 40771269Full record

ArticleFrontiers in endocrinology2025

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

Yaqiang Li, Lin Li, Lili He

Abstract readValidation Study
In one paragraph

Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Yaqiang Li *Department of Neurology, People's Hospital of Lixin County, Bozhou, China.
Lin Li *Department of Nosocomial Awareness, Lixin County Hospital of Traditional Chinese Medicine, Bozhou, China.
Lili HeDepartment of Nosocomial Awareness, Lixin County Hospital of Traditional Chinese Medicine, Bozhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: 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. Methods: 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. Results: 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; Conclusions: 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.

Indexed as

Diabetes Mellitus, Type 2NomogramsUrinary Tract InfectionsAgedAged, 80 and overFemaleHumansMaleMiddle AgedRisk AssessmentRisk FactorsROC Curvedecision curve analysisdiabetes mellitusnomogramtype 2 diabetes mellitusurinary tract infection

Identifiers

PMID40771269
PMCPMC12326275

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.