Cox S M L, Hoitinga P, Oudhuis G J, Hopstaken R M, Savelkoul P H M, Cals J W L, de Bont E G P M
Department of Family Medicine, CAPHRI, Maastricht University, Maastricht, The Netherlands.
Medical Microbiology, Infectious Diseases, and Infection Prevention, Maastricht University Medical Centre+, Maastricht, The Netherlands.
Scand J Prim Health Care. 2025 Mar;43(1):59-65. doi: 10.1080/02813432.2024.2392776. Epub 2024 Aug 20.
Urinary symptoms constitute the primary reason for female patients to consult their general practitioner. The urinary dipstick test serves as a cornerstone for diagnosing urinary tract infections (UTIs), yet traditional visual interpretation may be subject to variability. Automated devices for dipstick urinalysis are routinely used as alternatives, yet the evidence regarding their accuracy remains limited. Therefore we aimed to compare concordance between visual and automated urinary dipstick interpretation and determine their test characteristics for the prediction of bacteriuria.
We conducted a prospective validation study including urine samples originating from adult patients in general practice that were sent to the Maastricht Medical Centre + for urinary culture. Urinary dipstick tests were performed on each sample, which were interpreted visually and automatically. We calculated Cohen's κ and percentage agreement and used 2 × 2 tables to calculate test characteristics.
We included 302 urine samples. Visual and automated analysis showed almost perfect agreement (κ = 0.82 and κ = 0.86, respectively) for both nitrite and leukocyte esterase, but moderate agreement for erythrocytes (κ = 0.51). Interpretation of clinically relevant (nitrite and/or leukocyte esterase positive) samples showed almost perfect agreement (κ = 0.88). Urinary dipsticks show similar test characteristics with urinary culture as gold standard, with sensitivities of 0.92 and 0.91 and specificities of 0.37 and 0.41 for visual and automated interpretation respectively.
Automated and visual dipstick analysis show near perfect agreement and perform similarly in predicting bacteriuria. However, automated analysis requires maintenance and occasionally measurement errors can occur.
泌尿系统症状是女性患者咨询全科医生的主要原因。尿液试纸检测是诊断尿路感染(UTIs)的基石,但传统的视觉判读可能存在差异。尿液试纸分析的自动化设备经常被用作替代方法,但其准确性的证据仍然有限。因此,我们旨在比较视觉和自动化尿液试纸判读之间的一致性,并确定它们预测菌尿症的检测特征。
我们进行了一项前瞻性验证研究,纳入了来自全科医疗中成年患者的尿液样本,这些样本被送往马斯特里赫特医学中心+进行尿液培养。对每个样本进行尿液试纸检测,并进行视觉和自动判读。我们计算了科恩kappa系数和一致性百分比,并使用2×2表格计算检测特征。
我们纳入了302份尿液样本。亚硝酸盐和白细胞酯酶的视觉和自动分析显示几乎完全一致(kappa系数分别为0.82和0.86),但红细胞的一致性为中等(kappa系数为0.51)。临床相关(亚硝酸盐和/或白细胞酯酶阳性)样本的判读显示几乎完全一致(kappa系数为0.88)。以尿液培养作为金标准,尿液试纸在视觉和自动判读时显示出相似的检测特征,敏感性分别为0.92和0.91,特异性分别为0.37和0.41。
自动化和视觉试纸分析显示出近乎完美的一致性,在预测菌尿症方面表现相似。然而,自动化分析需要维护,偶尔会出现测量误差。