Influence of Microbiome Interactions on Antibiotic Resistance Development in the ICU Environment: Insights and Opportunities with Machine Learning
Aikaterini Sakagianni, Christina Koufopoulou, Petros Koufopoulos, Georgios Feretzakis, Αθανάσιος Αναστασίου, Nikolaos Theodorakis, Pavlos Myrianthefs
Abstract
Antibiotic resistance is a global health crisis exacerbated by the misuse of antibiotics in healthcare, agriculture, and the environment. In an intensive care unit (ICU), where high antibiotic usage, invasive procedures, and immunocompromised patients converge, resistance risks are amplified, leading to multidrug-resistant organisms (MDROs) and poor patient outcomes. The human microbiome plays a crucial role in the development and dissemination of antibiotic resistance genes (ARGs) through mechanisms like horizontal gene transfer, biofilm formation, and quorum sensing. Disruptions to the microbiome balance, or dysbiosis, further exacerbate resistance, particularly in high-risk ICU environments. This study explores microbiome interactions and antibiotic resistance in the ICU, highlighting machine learning (ML) as a transformative tool. Machine learning algorithms analyze high-dimensional microbiome data, predict resistance patterns, and identify novel therapeutic targets. By integrating genomic, microbiome, and clinical data, these models support personalized treatment strategies and enhance infection control measures. The results demonstrate the potential of machine learning to improve antibiotic stewardship and predict patient outcomes, emphasizing its utility in ICU-specific interventions. In conclusion, addressing antibiotic resistance in the ICU requires a multidisciplinary approach combining advanced computational methods, microbiome research, and clinical expertise. Enhanced surveillance, targeted interventions, and global collaboration are essential to mitigate antibiotic resistance and improve patient care.