Anil Gayatri, Glass Joshua, Mosaddegh Abdolreza, Cazer Casey L
Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY.
Department of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, NY.
Am J Vet Res. 2025 Feb 27;86(S1):S70-S79. doi: 10.2460/ajvr.24.10.0314. Print 2025 Mar 1.
Antimicrobial resistance (AMR) is a public health threat requiring monitoring across multiple sectors because AMR genes and pathogens can pass between humans, animals, and the environment. Idiosyncrasies in AMR data, including missing data and changes in testing protocols, make characterizing AMR trends over time and sectors challenging. Therefore, this study applied machine learning methods to impute missing minimum inhibitory concentrations.
Models were built using cattle-associated Escherichia coli from the National Antimicrobial Resistance Monitoring System. Random forest models were designed to predict the minimum inhibitory concentration of a given E coli isolate for 10 antimicrobials. Predictors included isolate metadata and the minimum inhibitory concentrations of other antimicrobials. Model performance was evaluated on held-out test data and 2 external datasets (E coli isolated from chickens and humans).
Overall, the accuracy within 1 minimum inhibitory concentration category was over 80% for all 10 antimicrobials and over 90% for 5 antimicrobials on test data. Six of the models performed as well on both external datasets as on test data, whereas the remaining 4 had similar accuracy on the human dataset but lower on the chicken data.
These results indicate that the models can predict minimum inhibitory concentration values at a level of accuracy that would be helpful for imputation in resistance datasets.
The imputation of missing minimum inhibitory concentrations would allow for better evaluation of AMR trends over time, helping inform stewardship policies. These models may also help streamline surveillance and clinical susceptibility testing because they suggest which antimicrobials need to be laboratory-tested and which can be extrapolated by modeling.
抗菌药物耐药性（AMR）是一种公共卫生威胁，需要跨多个部门进行监测，因为AMR基因和病原体可在人类、动物和环境之间传播。AMR数据存在特殊性，包括数据缺失和检测方案的变化，这使得表征AMR随时间和部门的趋势具有挑战性。因此，本研究应用机器学习方法来估算缺失的最低抑菌浓度。
使用来自国家抗菌药物耐药性监测系统的与牛相关的大肠杆菌构建模型。设计随机森林模型来预测给定大肠杆菌分离株对10种抗菌药物的最低抑菌浓度。预测因素包括分离株元数据和其他抗菌药物的最低抑菌浓度。在留出的测试数据和2个外部数据集（从鸡和人类分离的大肠杆菌）上评估模型性能。
总体而言，在测试数据上，所有10种抗菌药物在1个最低抑菌浓度类别内的准确率超过80%，5种抗菌药物超过90%。其中6个模型在两个外部数据集上的表现与在测试数据上一样好，而其余4个模型在人类数据集上的准确率相似，但在鸡数据集上较低。
这些结果表明，这些模型能够以有助于在耐药性数据集中进行估算的准确度预测最低抑菌浓度值。
估算缺失的最低抑菌浓度将有助于更好地评估AMR随时间的趋势，为管理政策提供参考。这些模型还可能有助于简化监测和临床药敏试验，因为它们表明哪些抗菌药物需要进行实验室检测，哪些可以通过建模推断。
