Cai Tommaso, Anceschi Umberto, Prata Francesco, Collini Lucia, Brugnolli Anna, Migno Serena, Rizzo Michele, Liguori Giovanni, Gallelli Luca, Wagenlehner Florian M E, Johansen Truls E Bjerklund, Montanari Luca, Palmieri Alessandro, Tascini Carlo
Department of Urology, Santa Chiara Regional Hospital, 38123 Trento, Italy.
Institute of Clinical Medicine, University of Oslo, 0315 Oslo, Norway.
Antibiotics (Basel). 2023 Feb 11;12(2):375. doi: 10.3390/antibiotics12020375.
A correct approach to recurrent urinary tract infections (rUTIs) is an important pillar of antimicrobial stewardship. We aim to define an Artificial Neural Network (ANN) for predicting the clinical efficacy of the empiric antimicrobial treatment in women with rUTIs.
We extracted clinical and microbiological data from 1043 women. We trained an ANN on 725 patients and validated it on 318.
The ANN showed a sensitivity of 87.8% and specificity of 97.3% in predicting the clinical efficacy of empirical therapy. The previous use of fluoroquinolones (HR = 4.23; = 0.008) and cephalosporins (HR = 2.81; = 0.003) as well as the presence of with resistance against cotrimoxazole (HR = 3.54; = 0.001) have been identified as the most important variables affecting the ANN output decision predicting the fluoroquinolones-based therapy failure. A previous isolation of with resistance against fosfomycin (HR = 2.67; = 0.001) and amoxicillin-clavulanic acid (HR = 1.94; = 0.001) seems to be the most influential variable affecting the output decision predicting the cephalosporins- and cotrimoxazole-based therapy failure. The previously mentioned with resistance against cotrimoxazole (HR = 2.35; < 0.001) and amoxicillin-clavulanic acid (HR = 3.41; = 0.007) seems to be the most influential variable affecting the output decision predicting the fosfomycin-based therapy failure.
ANNs seem to be an interesting tool to guide the antimicrobial choice in the management of rUTIs at the point of care.
正确处理复发性尿路感染（rUTIs）是抗菌药物管理的重要支柱。我们旨在定义一种人工神经网络（ANN），用于预测rUTIs女性经验性抗菌治疗的临床疗效。
我们从1043名女性中提取了临床和微生物学数据。我们在725名患者身上训练了一个ANN，并在318名患者身上进行了验证。
ANN在预测经验性治疗的临床疗效方面显示出87.8%的敏感性和97.3%的特异性。先前使用氟喹诺酮类药物（HR = 4.23；P = 0.008）和头孢菌素类药物（HR = 2.81；P = 0.003）以及存在对复方新诺明耐药的大肠埃希菌（HR = 3.54；P = 0.001）已被确定为影响基于氟喹诺酮类药物治疗失败的ANN输出决策的最重要变量。先前分离出对磷霉素耐药的大肠埃希菌（HR = 2.67；P = 0.001）和阿莫西林-克拉维酸耐药的大肠埃希菌（HR = 1.94；P = 0.001）似乎是影响基于头孢菌素类药物和复方新诺明治疗失败的输出决策的最有影响的变量。前面提到的对复方新诺明耐药的大肠埃希菌（HR = 2.35；P < 0.001）和对阿莫西林-克拉维酸耐药的大肠埃希菌（HR = 3.41；P = 0.007）似乎是影响基于磷霉素治疗失败的输出决策的最有影响的变量。
ANN似乎是一种有趣的工具，可在护理点指导rUTIs管理中的抗菌药物选择。
