Predicting Antimicrobial Resistance in the Intensive Care Unit
This addresses the need for faster AMR prediction to inform clinical decisions and reduce unnecessary antibiotic use in healthcare, though it is incremental as it builds on existing modeling approaches.
The study tackled the problem of slow antimicrobial resistance (AMR) assays in intensive care units by developing predictive models using clinical and microbiological data, achieving improved classification performance with AUCs of 0.88-0.89 compared to a naive model's 0.86 for 6 organisms and 10 antibiotics.
Antimicrobial resistance (AMR) is a risk for patients and a burden for the healthcare system. However, AMR assays typically take several days. This study develops predictive models for AMR based on easily available clinical and microbiological predictors, including patient demographics, hospital stay data, diagnoses, clinical features, and microbiological/antimicrobial characteristics and compares those models to a naive antibiogram based model using only microbiological/antimicrobial characteristics. The ability to predict the resistance accurately prior to culturing could inform clinical decision-making and shorten time to action. The machine learning algorithms employed here show improved classification performance (area under the receiver operating characteristic curve 0.88-0.89) versus the naive model (area under the receiver operating characteristic curve 0.86) for 6 organisms and 10 antibiotics using the Philips eICU Research Institute (eRI) database. This method can help guide antimicrobial treatment, with the objective of improving patient outcomes and reducing the usage of unnecessary or ineffective antibiotics.