Abstract
It is well established that Intensive Care Units (ICUs) are a focal point in antimicrobial
consumption with a major influence on the ecological consequences of antibiotic use.
With the high prevalence and mortality of infections in critically ill patients, and
the clinical challenges of treating patients with septic shock, the impact of real
life clinical decisions made by intensivists becomes more significant. Both under-
and over-treatment with unnecessarily broad spectrum antibiotics can lead to detrimental
outcomes. Even though substantial progress has been made in developing rapid diagnostic
tests that can help guide antibiotic use, there is still a time window when clinicians
must decide the empiric antibiotic treatment with insufficient clinical data. The
continuous streams of data available in the ICU environment make antimicrobial optimization
an ongoing challenge for clinicians but at the same time can serve as the input for
sophisticated models. In this review, we summarize the evidence to help guide antibiotic
decision-making in the ICU. We focus on 1) deciding if to start antibiotics, 2) choosing the spectrum of the empiric agents to use, and
3) de-escalating the chosen empiric antibiotics. We provide a perspective on the role
of machine learning and artificial intelligence models for clinical decision support
systems that can be incorporated seamlessly into clinical practice in order to improve
the antibiotic selection process and, more importantly, current and future patients'
outcomes.
Keywords
empiric antibiotics - antimicrobial resistance - clinical decision support systems
- machine learning / artificial intelligence
