Machine learning to predict risk for community-onset Staphylococcus aureus infections in children living in southeastern United States

PLOS ONE RESEARCH ARTICLE Machine learning to predict risk for community-onset Staphylococcus aureus infections in children living in southeastern United States Xiting Lin ID1, Ruijin Geng1¤a, Kurt Menke2, Mike Edelson3¤b, Fengxia Yan4, Traci Leong5, George S. Rust6, Lance A. Waller5, Erica L. Johnson1, Lilly Cheng Immergluck ID1* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Morehouse School of Medicine, Department of Microbiology/Biochemistry/Immunology and Clinical Research Center, Atlanta, Georgia, United States of America, 2 Septima, Copenhagen, Denmark, 3 InterDev, Roswell, Georgia, United States of America, 4 Morehouse School of Medicine, Department of Community Health and Preventive Medicine, Atlanta, Georgia, United States of America, 5 Emory University, Rollins School of Public Health, Department of Biostatistics & Bioinformatics, Atlanta, Georgia, United States of America, 6 College of Medicine, and Center for Medicine and Public Health, Florida State University, Tallahassee, Florida, United States of America ¤a Current address: Sinovac Biotech, Ltd, Beijing, China ¤b Current address: Axim Geospatial, Sun Prairie, Wisconsin, United States of America * OPEN ACCESS Citation: Lin X, Geng R, Menke K, Edelson M, Yan F, Leong T, et al. (2023) Machine learning to predict risk for community-onset Staphylococcus aureus infections in children living in southeastern United States. PLoS ONE 18(9): e0290375. https:// doi.org/10.1371/journal.pone.0290375 Editor: Eili Y. Klein, Johns Hopkins University, UNITED STATES Received: October 1, 2022 Accepted: August 7, 2023 Published: September 1, 2023 Copyright: © 2023 Lin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data for this study are available within the paper and its Supporting information files. Funding: LCI receives funding from the following sources: National Library Medicine, Grant #1G08LM013190-01; K-08 AHRQ-Mentored Clinical Scientist Career Research Development Award- HS024338-01. LCI, RG supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378. LAW receives 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 PLOS ONE | https://doi.org/10.1371/journal.pone.0290375 September 1, 2023 1 / 20 PLOS ONE funding from the following sources: LW receives funding from NIH/NICHHD grant R01HD092580, NIH/NIDA T32DA050552,NIH/NIAID R01AI149527, U01AI148069, UG3AI176853,NIH/NIEHS R01ES033530, P30ES019776,NIH/NCI R01CA266572,NIH/NIMHD R21MD017943,CDC 6R49CE003072, 23IPA2312301. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Pediatric CO-S. aureus and machine learning are at highest and lowest risk for community-onset S. aureus infections over a 14-year time span. Introduction Staphylococcus aureus (S. aureus) is a bacterium that is a part of normal human flora and also is a source of human infection. Approximately 30–40% of humans can be asymptomatic ‘carriers’ of S. aureus [1], and from the late 1990s until recently, community-onset antibiotic resistant S. aureus infections, also known as methicillin resistant S. aureus (CO-MRSA), have increased dramatically in causing both non-invasive and invasive infections [2]. Infections due to community-onset S. aureus (CO-S. aureus) appear to be increasing nationally and globally [3]. Skin and soft tissue infections (SSTIs) account for most community-onset infections due to both CO-MRSA and community-onset methicillin sensitive S. aureus (CO-MSSA) [4, 5]. Moreover, over the last decade, while community-onset SSTIs continue to occur at high rates, the etiology has proportionately shifted more to CO-MSSA than CO-MRSA [6, 7]. Risk factors for these community-onset infections include densely populated areas [8, 9] and populations which are socioeconomically disadvantaged [10]. Race and ethnic disparities exist for community-onset S. aureus infections, and risks associated with pediatricrelated infections include daycare attendance, prior antibiotic use, family history of SSTIs, and public health insurance [10, 11]. However, the relationship between specific geographic location and risks tied to location for S. aureus infections has not been well characterized. Although several studies have explored socio-ecological risk factors for CO-MRSA [8, 12], the place-based associations between patients with CO-MRSA infections and identified risks have not been elucidated [9]. Moreover, the location-based associations tied to risk for staphylococcal infections at the community level among children have only been recentl (...truncated)