Müller Martin, Sägesser Nadine, Keller Peter M, Arampatzis Spyridon, Steffens Benedict, Ehrhard Simone, Leichtle Alexander B
Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland.
University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland.
Diagnostics (Basel). 2022 Apr 16;12(4):1008. doi: 10.3390/diagnostics12041008.
Urine flow cytometry (UFC) analyses urine samples and determines parameter counts. We aimed to predict different types of urine culture growth, including mixed growth indicating urine culture contamination.
A retrospective cohort study (07/2017-09/2020) was performed on pairs of urine samples and urine cultures obtained from adult emergency department patients. The dataset was split into a training (75%) and validation set (25%). Statistical analysis was performed using a machine learning approach with extreme gradient boosting to predict urine culture growth types (i.e., negative, positive, and mixed) using UFC parameters obtained by UF-4000, sex, and age.
In total, 3835 urine samples were included. Detection of squamous epithelial cells, bacteria, and leukocytes by UFC were associated with the different types of culture growth. We achieved a prediction accuracy of 80% in the three-class approach. Of the = 126 mixed cultures in the validation set, 11.1% were correctly predicted; positive and negative cultures were correctly predicted in 74.0% and 96.3%.
Significant bacterial growth can be safely ruled out using UFC parameters. However, positive urine culture growth (rule in) or even mixed culture growth (suggesting contamination) cannot be adequately predicted using UFC parameters alone. Squamous epithelial cells are associated with mixed culture growth.
尿流式细胞术（UFC）分析尿液样本并确定参数计数。我们旨在预测不同类型的尿培养生长情况，包括提示尿培养污染的混合生长。
对从成人急诊科患者获取的尿液样本和尿培养样本进行回顾性队列研究（2017年7月至2020年9月）。数据集被分为训练集（75%）和验证集（25%）。采用机器学习方法和极端梯度提升算法进行统计分析，以利用UF - 4000获得的UFC参数、性别和年龄预测尿培养生长类型（即阴性、阳性和混合性）。
共纳入3835份尿液样本。UFC检测到的鳞状上皮细胞、细菌和白细胞与不同类型的培养生长相关。在三类预测方法中，我们实现了80%的预测准确率。在验证集中的126份混合培养样本中，11.1%被正确预测；阳性和阴性培养样本的正确预测率分别为74.0%和96.3%。
使用UFC参数可以安全地排除显著的细菌生长。然而，仅使用UFC参数不能充分预测阳性尿培养生长（纳入标准），甚至不能充分预测混合培养生长（提示污染）。鳞状上皮细胞与混合培养生长相关。
