1170. Applying Machine Learning Algorithms to Predict Multi-Drug Resistant Bacterial Infections from Prior Drug Exposure
Session: Poster Abstract Session: Healthcare Epidemiology: MDR-Gram Negative Infections
Friday, October 5, 2018
Room: S Poster Hall
Background: Multi-drug resistant (MDR) infection in the acute care setting prolongs hospital stay and causes high mortality, especially in the pediatric population. Being able to predict MDR infection risk upon or during admission could help prevent and reduce morbidity and mortality in children requiring acute care in the future. This study aimed to develop and validate a predictive model for MDR infection in the pediatric population using Machine Learning (ML) analysis.

Methods: The study population included hospitalized pediatric patients diagnosed with MDR infection between January 1, 2010 and March 8, 2018. All positive cultures during that period were coded as growing either an MDR or non-MDR organism. ML was performed with Random Forest (RF) analysis to determine whether hospital drug exposure in the 90 days prior to culture was able to accurately classify cultures as positive for an MDR or non-MDR organism.

Results: During the study period, 7551 positive cultures were defined as MDR out of a total of 26913 cultures (28% of all positive cultures). When all cultures were included in the analysis, RF was modestly successful at classifying MDR versus non-MDR organisms. Significant improvements in classification accuracy were obtained by subdividing cultures based on growth of individual species. RF was able to classify MDR Enterococcus with accuracy = 0.87, positive predictive value of 0.81, and negative predictive value of 0.88. Surprisingly, exposure to many non-antibiotic drugs were important in predicting antibiotic resistance, indicating either that these drugs altered risk directly, or were correlated with MDR risk indirectly.

Conclusion: Drugs without known antimicrobial activity were important predictors of MDR status. Non-antimicrobial drug exposure may be a marker for disease-types or therapeutic interventions that place patients at higher risk of MDR infection. Monitoring antimicrobial and non-antimicrobial drug exposure may accurately identify patients at highest risk of MDR infection.

Elizabeth Lendrum, B.S., College of Medicine, University of Cincinnati, Cincinnati, OH, David Haslam, MD, Pediatric Infectious Diseases, Cincinnati Children's Hospital, Cincinnati, OH and Lilliam Ambroggio, PhD, MPH, Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH


E. Lendrum, None

D. Haslam, None

L. Ambroggio, None

Findings in the abstracts are embargoed until 12:01 a.m. PDT, Wednesday Oct. 3rd with the exception of research findings presented at the IDWeek press conferences.