Institute of Biochemical Technology National Chiayi University, Taiwan.
Department of Urology, China Medical University and Beigang Hospital, Taiwan.
Biomed Res Int. 2022 Mar 21;2022:5775447. doi: 10.1155/2022/5775447. eCollection 2022.
Urinary tract infections (UTIs) are the most common infections among hospitalized patients. Cystoscopy is a minimally invasive procedure to check bladder disease, among the patients receiving procedure, approximately 10% of patients may experience UTI. In this study, a neural network model with high accuracy, sensitivity, and specificity was developed to predict the probability of UTIs caused by cystoscopic procedures. To reduce antibiotic overuse during cystoscopic procedures, the model can provide clinicians with a rapid assessment of whether patients require prophylactic antibiotics.
Patients who underwent cystoscopic procedures at China Medical University Beigang Hospital from 2016 to 2019 were retrospectively reviewed. A total of 1647 patients were enrolled, and 147 cases of urinary tract infection occurred. An artificial neural network (ANN) and logistic regression analysis were used to develop the prediction models, and the two models were compared.
The logistic regression analysis model had an accuracy of 91%, sensitivity of 2%, and specificity of 99%, indicating that the logistic regression model predicted that most patients had a low risk of infection. The neural network model had a high accuracy of 85%, sensitivity of 80%, and specificity of 88%.
Because the logistic regression model had low sensitivity and missed most cases of UTI, the logistic regression model is inappropriate for clinical application. The neural network model has superior predictive ability and can be considered a tool in clinical practice.
尿路感染（UTI）是住院患者中最常见的感染。膀胱镜检查是一种检查膀胱疾病的微创手术，在接受该检查的患者中，约有 10%可能会发生 UTI。在这项研究中，开发了一种具有高精度、高灵敏度和高特异性的神经网络模型，以预测膀胱镜检查引起 UTI 的概率。为了减少膀胱镜检查过程中的抗生素过度使用，该模型可以为临床医生提供一种快速评估患者是否需要预防性使用抗生素的方法。
回顾性分析了 2016 年至 2019 年在中国医科大学北岗医院接受膀胱镜检查的患者。共纳入 1647 例患者，其中 147 例发生尿路感染。采用人工神经网络（ANN）和逻辑回归分析来开发预测模型，并对这两种模型进行比较。
逻辑回归分析模型的准确率为 91%，灵敏度为 2%，特异性为 99%，表明逻辑回归模型预测大多数患者感染风险较低。神经网络模型的准确率为 85%，灵敏度为 80%，特异性为 88%。
由于逻辑回归模型的灵敏度较低，且错过了大多数 UTI 病例，因此该逻辑回归模型不适合临床应用。神经网络模型具有优越的预测能力，可以考虑作为临床实践中的一种工具。
