ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Clinical Diabetes
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1593735
This article is part of the Research TopicTransforming Chronic Disease Treatment with AI and Big DataView all 5 articles
A personalized prediction model for distinguishing between asymptomatic bacteriuria and symptomatic urinary tract infections in patients with type 2 diabetes mellitus using machine learning
Provisionally accepted
- 1Department of Clinical Laboratory, Second Hospital of Tianjin Medical University, Tianjin, China
- 2Department of Respiratory, Featured Medical Center of Chinese People's Armed Police Force, Tianjin, China, Tianjin, China
- 3Department of Laboratory Medicine, the Third Affiliated Hospital of Zhengzhou University; Zhengzhou Key Laboratory for In Vitro Diagnosis of Hypertensive Disorders of Pregnancy, Zhengzhou, China
- 4People’s Hospital of Zhengzhou University, Heart Center of Henan Provincial People’s Hospital, Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China, Zhengzhou, China
- 5Department of urology, Tianjin Medical University Nankai Hospital Tianjin, China; Department of urology, Tianjin Nankai Hospital, Tianjin, China, Tianjin, China
- 6Department of Endocrinology, the Second Hospital of Tianjin Medical University, Tianjin, China, Tianjin, China
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Background: Patients with type 2 diabetes mellitus (T2DM) have an increased susceptibility to urinary tract infections (UTIs), caused by uropathogenic Escherichia coli (UPEC). Asymptomatic bacteriuria (ASB) is a significant contributor, but lots of patients are difficult to distinguish. Distinguishing between ASB and symptomatic UTIs can greatly assist clinicians in rational use of antimicrobials.Methods: Patients with T2DM and UTIs caused exclusively by UPEC were recruited from the Second Hospital of Tianjin Medical University between 2018 and 2023. Demographic and clinical data were systematically collected for these patients through a retrospective electronic chart review, in accordance with the inclusion and exclusion criteria. We utilized this dataset as training set to develop an ASB predictive model called ASBPredictor.Results: A total of 337 cases were collected, comprising 158 cases (46.9%) of ASB and 179 cases (53.1%) of symptomatic UTIs. Based on the optimal predictive model, ASBPredictor exhibited a remarkable level of precision, achieving an area under the curve score of 0.82. The identification of ASB is influenced by several crucial factors, including urinary bacteria, urinary white blood cell clusters, C-reactive protein, alanine aminotransferase, glucose, gamma-glutamyl transpeptidase, sodium ions (Na + ), and eosinophils.The ASBPredictor is an accurate, efficient, and reliable tool that helps doctors differentiate between ASB and symptomatic UTIs. This precise differential diagnosis has the potential to enhance the quality of antimicrobial prescribing.
Keywords: Asymptomatic bacteriuria, type 2 diabetes mellitus, Urinary Tract Infections, Uropathogenic Escherichia coli, machine learning
Received: 14 Mar 2025; Accepted: 17 Jul 2025.
Copyright: © 2025 Liu, Li, Fang, Wu, Cao, Cai, Yu, Zhao and Duan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yan Zhao, Department of Endocrinology, the Second Hospital of Tianjin Medical University, Tianjin, China, Tianjin, China
Yitao Duan, Department of Laboratory Medicine, the Third Affiliated Hospital of Zhengzhou University; Zhengzhou Key Laboratory for In Vitro Diagnosis of Hypertensive Disorders of Pregnancy, Zhengzhou, China
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