Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach

Abstract

Aims Analysis and tracking of antimicrobial utilization (AU) are crucial in antimicrobial stewardship efforts which are used to find effective interventions for controlling antimicrobial resistance. In antimicrobial stewardship, standard risk adjustment models are needed for benchmarking appropriate AU and for fair inter‐facility comparison. In this study we identify patient‐ and facility‐level predictors of antimicrobial usage in hospitalized patients using a machine learning approach, which can be used to inform a risk adjustment model to facilitate assessment of AU. To our knowledge, this is the first time machine learning has been applied for this purpose. Methods and Results Patient admission records were retrieved from the Duke Antimicrobial Stewardship Outreach Network which include clinical data for 27 community hospitals in the southeastern United States. Candidate features (predictors) were then generated from these records. The number of features was reduced using a statistical approach, and missing values of the reduced feature set were imputed using bootstrapping and expectation‐maximization algorithm. Finally, support vector regression (SVR) and cubist regression (CB) models were applied to find root‐mean‐square error values which were used to evaluate the selected feature set. The performance of the SVR and CB models was found to be better than that of linear null and negative binomial null models, thereby demonstrating the effectiveness of our selected features. Conclusions Relevant patient‐ and facility‐level predictors of antimicrobial usage in days of therapy were obtained and evaluated. The potential predictor set can be used in risk adjustment strategies for benchmarking antimicrobial use. Significance and Impact of the Study One reason for the rapid emergence of antimicrobial resistance is inappropriate use of antibiotics in hospitalized patients. Identifying predictors of antimicrobial exposure using a machine learning technique can improve the use of AU, enhance patient health outcomes, and reduce the infection spread caused by antimicrobial‐resistant organisms.

Considerations for diagnostic COVID-19 tests

“…Some mature AI solutions are ready for application to support patient care 132 or clinical decision making, for example by reducing antibiotic use 133 (Box 3). More tools that improve the use of clinical and epidemiological big data will become increasingly available 134,135 and are currently being developed by laboratory scientists together with data scientists and software developers. New biomarkers are not only crucial for patient management by facilitating early diagnosis of severe COVID19, they are also important in the development of a COVID19 vaccine, as they can accelerate clinical trials, reduce costs, guide participant selection, reduce patient safety risks and enable easier verification of the mechanism of action.…”

“…Some mature AI solutions are ready for application to support patient care 132 or clinical decision-making, for example by reducing antibiotic use 133 (Box 3 ). More tools that improve the use of clinical and epidemiological big data will become increasingly available 134 , 135 and are currently being developed by laboratory scientists together with data scientists and software developers.…”

SU-QMI: A Feature Selection Method Based on Graph Theory for Prediction of Antimicrobial Resistance in Gram-Negative Bacteria

“…Thousands of people in the United States die each year due to infections by antimicrobialresistant bacteria [1,2]. Convergent evolution or ancient divergence can lead to genes in different organisms that encode proteins with related structure and function, but with limited sequence similarity.…”

SU-QMI: A Feature Selection Method Based on Graph Theory for Prediction of Antimicrobial Resistance in Gram-Negative Bacteria

“…Thousands of people in the United States die each year due to infections by antimicrobial-resistant bacteria [1,2]. Convergent evolution or ancient divergence can lead to genes in different organisms that encode proteins with related structure and function, but with limited sequence similarity.…”