使用机器学习的个性化预测模型来区分无症状的细菌尿和症状性尿路感染
Shuangqing Liu1, Juan Li2, Yang Fang3,4
1Department of Clinical Laboratory, The Second Hospital of Tianjin Medical University, Tianjin, China.
Frontiers in endocrinology
|August 21, 2025
概括
患有尿路感染的2型糖尿病患者可能难以区分无症状细菌尿和症状性尿路感染. 一种名为ASBPredictor的新模型精确地区分这些情况,改善了抗菌药物的处方.
科学领域:
- 尿道病学
- 传染性疾病
- 糖尿病学
背景情况:
- 患有2型糖尿病 (T2DM) 的患者对尿路感染 (UTIs) 的易感性增加,通常由尿病原菌大肠杆菌 (UPEC) 引起.
- 在T2DM患者中区分无症状细菌尿症 (ASB) 和症状性尿路感染是临床上具有挑战性的,但对于适当的抗菌药物使用至关重要.
研究的目的:
- 开发和验证一种预测模型,即ASBPredictor,用于区分ASB和T2DM患者的症状性尿路感染.
- 鉴定有助于预测ASB的关键临床和实验室因素.
主要方法:
- 在2018年至2023年期间,对337名T2DM和UPEC引起的尿路感染患者进行了回顾性图表审查.
- 为了训练和开发ASBPredictor模型,收集了人口和临床数据.
主要成果:
- 这项研究包括158例ASB (46. 9%) 和179例症状性尿路感染 (53. 1%).
- ASBPredictor的曲线下的面积 (AUC) 为0.82,显示出很高的预测准确度.
- 尿液中的细菌,尿液中的白细胞群,C反应蛋白,氨酸转化酶,葡萄糖,-胺转化酶,离子 (Na+) 和乙基.
结论:
- 在T2DM患者中,ASBPredictor是临床医生区分ASB和症状性尿路感染的准确和有效工具.
- 通过ASBPredictor改进的差异诊断可以导致更合理的抗菌药物处方做法.
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