Werneburg Glenn T, Rhoads Daniel D, Milinovich Alex, McSweeney Sean, Knorr Jacob, Mourany Lyla, Zajichek Alex, Goldman Howard B, Haber Georges-Pascal, Vasavada Sandip P
Department of Urology, Glickman Urological Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
Department of Pathology and Laboratory Medicine, Cleveland Clinic Foundation, Cleveland, OH, USA.
BJU Int. 2025 Apr;135(4):629-637. doi: 10.1111/bju.16626. Epub 2024 Dec 22.
To develop, externally validate, and test a series of computer algorithms to accurately predict antibiotic susceptibility test (AST) results at the time of clinical diagnosis, up to 3 days before standard urine culture results become available, with the goal of improving antibiotic stewardship and patient outcomes.
Machine learning algorithms were developed and trained to predict susceptibility or resistance using over 4.7 million discrete AST classifications from urine cultures in a cohort of adult patients from outpatient and inpatient settings from 2012 to 2022. The algorithms were validated on a cohort from a geographically-distant hospital system, 1931 km (1200 miles) from the training cohort facilities, from the same time period. Finally, algorithms were clinically validated in a contemporary cohort and compared to the empiric therapy prescribed by clinicians. Appropriateness of the antibiotics selected by clinicians and the algorithm during the clinical validation was compared.
Algorithms were accurate during clinical validation (area under the receiver operating characteristic curve [AUC] 0.71-0.94) for all 11 tested antibiotics. The algorithms' accuracy improved as the organism was identified (AUC 0.79-0.97). In external validation in a geographically-distant cohort, the algorithms remained accurate even without additional training on this group (AUC 0.69-0.87). When the algorithms were trained on the antibiogram from the geographically-distant hospital, the accuracy improved (AUC 0.70-0.93). When algorithms' performances were tested against clinicians in a contemporary cohort for the empiric prescription of oral antibiotics, the drug agent suggested by the algorithms more frequently resulted in adequate empiric coverage.
Machine learning algorithms trained on a large dataset are accurate in prediction of urine culture susceptibility vs resistance up to 3 days prior to urine AST availability. Clinical implementation of such an algorithm could improve both clinical care and antimicrobial stewardship.
开发、外部验证并测试一系列计算机算法，以在临床诊断时准确预测抗生素敏感性试验（AST）结果，比标准尿培养结果可得时间提前至多3天，目标是改善抗生素管理和患者预后。
开发并训练机器学习算法，使用2012年至2022年来自门诊和住院环境的成年患者队列中超过470万个来自尿培养的离散AST分类来预测敏感性或耐药性。这些算法在一个地理距离较远的医院系统的队列中进行验证，该系统距离训练队列设施约1931公里（约1200英里），时间相同。最后，算法在当代队列中进行临床验证，并与临床医生开出的经验性治疗进行比较。比较临床验证期间临床医生和算法选择的抗生素的适宜性。
对于所有11种测试抗生素，算法在临床验证期间准确（受试者操作特征曲线下面积[AUC]为0.71 - 0.94）。随着病原体被识别，算法的准确性提高（AUC为0.79 - 0.97）。在地理距离较远的队列的外部验证中，即使没有对该组进行额外训练，算法仍然准确（AUC为0.69 - 0.87）。当算法根据地理距离较远的医院的抗菌谱进行训练时，准确性提高（AUC为0.70 - 0.93）。当在当代队列中针对口服抗生素的经验性处方将算法的性能与临床医生进行比较时，算法建议的药物更频繁地导致足够的经验性覆盖。
在大型数据集上训练的机器学习算法在尿AST结果可得前3天内预测尿培养敏感性与耐药性方面准确。这种算法的临床应用可改善临床护理和抗菌药物管理。
