Synergistic use of multimodal observations improves identification of asymptomatic carriers of antimicrobial-resistant organisms
Asymptomatic carriers of antimicrobial-resistant organisms (AMROs) can unwittingly transmit these pathogens in healthcare systems, contributing to the burden of healthcare-associated infections (HAIs). Surveillance in hospitals can involve different types of observations to monitor AMRO spread; however, a framework to coherently synthesize these datasets to identify AMRO carriers is lacking. Here, we develop an inference framework combining a data-driven mechanistic transmission model and multimodal observations from clinical cultures, electronic health records, patient mobility, and genomic sequence data. We apply the inference framework to carbapenem-resistant Klebsiella pneumoniae (CRKP) at an urban quaternary care hospital and find that the addition of genome sequence data to patient characteristics supports more accurate identification of carriers. Model simulations further suggest that inference-guided targeted isolation leads to more reduction of AMRO burdens compared to alternative, heuristic approaches. Thus, the synergistic effect of utilizing multimodal observations for estimating AMRO carriage risk may inform improved interventions in hospital settings.