Machine learning to predict risk for community-onset Staphylococcus aureus infections in children living in southeastern United States
Year: 2023
Type: article
Study design: Observational Study
Abstract: Staphylococcus aureus (S. aureus) is known to cause human infections and since the late 1990s, community-onset antibiotic resistant infections (methicillin resistant S. aureus (MRSA)) continue to cause significant infections in the United States. Skin and soft tissue infections (SSTIs) still account for the majority of these in the outpatient setting. Machine learning can predict the location-based risks for community-level S. aureus infections. Multi-year (2002-2016) electronic health records of children <19 years old with S. aureus infections were queried for patient level data for demographic, clinical, and laboratory information. Area level data (Block group) was abstracted from U.S. Census data. A machine learning ecological niche model, maximum entropy (MaxEnt), was applied to assess model performance of specific place-based factors (determined a priori) associated with S. aureus infections; analyses were structured to compare methicillin resistant (MRSA) against methicillin sensitive S. aureus (MSSA) infections. Differences in rates of MRSA and MSSA infections were determined by comparing those which occurred in the early phase (2002-2005) and those in the later phase (2006-2016). Multi-level modeling was applied to identify risks factors for S. aureus infections. Among 16,124 unique patients with community-onset MRSA and MSSA, majority occurred in the most densely populated neighborhoods of Atlanta's metropolitan area. MaxEnt model performance showed the training AUC ranged from 0.771 to 0.824, while the testing AUC ranged from 0.769 to 0.839. Population density was the area variable which contributed the most in predicting S. aureus disease (stratified by CO-MRSA and CO-MSSA) across early and late periods. Race contributed more to CO-MRSA prediction models during the early and late periods than for CO-MSSA. Machine learning accurately predicts which densely populated areas are at highest and lowest risk for community-onset S. aureus infections over a 14-year time span.
Source: PLoS ONE
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,805
Funders: Children's Healthcare of Atlanta, Morehouse School of Medicine, National Institutes of Health, Centers for Disease Control and Prevention, Agency for Healthcare Research and Quality, National Institute on Drug Abuse, National Cancer Institute, National Institute of Allergy and Infectious Diseases, National Institute of Environmental Health Sciences, U.S. National Library of Medicine, National Center for Advancing Translational Sciences, National Institute on Minority Health and Health Disparities, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: Quanitifying the Epidemiological Impact of Targeted Indoor Residual Spraying on Aedes-borne Diseases, G2224257784, Neighborhood transportation vulnerability and geographic patterns of diabetes-related limb loss, Identifying Risk Factors for Leprosy Transmission Related to Co-infections, Unsafe WASH and Undernutrition Using Novel Serologic Multiplex and High Resolution Metabolomic Assays, Improving statistical methods for small area estimates of public health indicators and demographic characteristics, Project HIRaCAARI: Empowering Communities to Stop the Spread of Antibiotic Resistant Infections through Accessible Health Information, Limited interaction cohort to identify determinants of viral suppression in MSM and transfeminine individuals living with HIV: A multilevel approach, CE19-001, INJURY PREVENTION RESEARCH CENTER AT EMORY, G8077756555, Assessing the 5-Year Effects of a 500-day Liquefied Petroleum Gas Cooking Intervention: Continued Follow up of Participants from the Household Air Pollution Intervention Network (HAPIN) trial, HERCULES: Exposome Research Center, Training in Advanced Data Analytics to End Drug-Related Harms (TADA), Georgia Clinical & Translational Science Alliance (Georgia CTSA)
OpenAlex ID: W4386365300