november 2020 • Science Translational Medicine
A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection
O recurso ao uso em segunda-linha de antibióticos de amplo espectro no tratamento de infecções do trato urinário (ITUs) está a aumentar, provavelmente devido à prevalência de resistência aos antibióticos. Kanjilal et al. aplicaram uma abordagem de Machine Learning calibrada para dados de processo clinico electrónico de hospitais locais para prever a probabilidade de resistência a antibióticos de primeira e segunda linha para ITU não complicada. O algoritmo então recomendou o antibiótico de menor largo-espectro para o qual um determinado agente bacteriano isolado se estimava como não resistente. O uso do pipeline reduziu a prescrição de antibióticos de amplo espectro e ineficazes para ITU na coorte de pacientes em relação aos médicos, sugerindo o potencial clínico da abordagem.
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.