A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection
A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection
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DOI：
10.1126/scitranslmed.aay5067
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发表时间：
2020-11-04
影响因子：
17.1
通讯作者：
Sontag, David
中科院分区：
文献类型：
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作者：
Kanjilal, Sanjat;Oberst, Michael;Sontag, David
抗生素耐药性是治疗失败的一个主要原因，并导致广谱药物的使用增加，从而产生进一步的耐药性。这种恶性循环的缩影是简单的尿路感染（UTI），这影响了两名妇女中的一名，并与抗生素耐药性的增加和广谱二线药物的高处方率有关。为了解决这个问题，我们开发了机器学习模型，使用电子健康记录数据预测抗生素敏感性，并建立了一个决策算法，用于推荐样本敏感的最可能的抗生素。当应用于2014年至2016年期间的3629名患者的测试队列时，该算法相对于临床医生减少了67%的二线抗生素使用。与此同时，相对于临床医生，它减少了18%的不适当的抗生素治疗，定义为选择标本耐药的治疗。对于临床医生选择二线药物但算法选择一线药物的标本，92%（1157例中的1066例）的决定最终对一线药物敏感。当临床医生选择了不合适的一线药物时，该算法在47%（392例中的183例）的情况下选择了合适的一线药物。我们的机器学习决策算法通过最大限度地减少广谱抗生素的使用，同时保持最佳的治疗效果，为常见的感染性综合征提供抗生素管理。有必要进一步开展工作，通过在更多样化的人群中训练模型来提高普遍性。
Antibiotic resistance is a major cause of treatment failure and leads to increased use of broad-spectrum agents, which begets further resistance. This vicious cycle is epitomized by uncomplicated urinary tract infection (UTI), which affects one in two women during their life and is associated with increasing antibiotic resistance and high rates of prescription for broad-spectrum second-line agents. To address this, we developed machine learning models to predict antibiotic susceptibility using electronic health record data and built a decision algorithm for recommending the narrowest possible antibiotic to which a specimen is susceptible. When applied to a test cohort of 3629 patients presenting between 2014 and 2016, the algorithm achieved a 67% reduction in the use of second-line antibiotics relative to clinicians. At the same time, it reduced inappropriate antibiotic therapy, defined as the choice of a treatment to which a specimen is resistant, by 18% relative to clinicians. For specimens where clinicians chose a second-line drug but the algorithm chose a first-line drug, 92% (1066 of 1157) of decisions ended up being susceptible to the first-line drug. When clinicians chose an inappropriate first-line drug, the algorithm chose an appropriate first-line drug 47% (183 of 392) of the time. Our machine learning decision algorithm provides antibiotic stewardship for a common infectious syndrome by maximizing reductions in broad-spectrum antibiotic use while maintaining optimal treatment outcomes. Further work is necessary to improve generalizability by training models in more diverse populations.