Abstract
Antibiotic resistance is constantly evolving, requiring frequent reevaluation of resistance patterns to guide treatment of bacterial infections. Antibiograms, aggregate antimicrobial susceptibility reports, are critical for evaluating the likelihood of antibiotic effectiveness. However, these antibiograms provide outdated resistance knowledge. Thus, this research employs predictive modeling of historic antibiograms to forecast the current year’s resistance rates. Utilizing subsets of the expansive 15-year Massachusetts statewide antibiogram dataset, we demonstrate the effectiveness of using our model selector PYPER with regression-based models to forecast current antimicrobial susceptibility. A PYPER variant is effective since it leverages the fact that different antibiotic-bacteria-location combinations have different antimicrobial susceptibility trends over time. In addition, we discuss relative weighting of the regression-variant models, the impact of location granularity, and the ability to forecast multiple years into the future.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alvarez-Uria, G., Gandra, S., Mandal, S., Laxminarayan, R.: Global forecast of antimicrobial resistance in invasive isolates of Escherichia coli and Klebsiella pneumoniae. Int. J. Infect. Dis. 68, 50–53 (2018)
Anderson, D., Miller, B., Marfatia, R., Drew, R.: Ability of an antibiogram to predict pseudomonas aeruginosa susceptibility to targeted antimicrobials based on hospital day of isolation. Infect. Control Hosp. Epidemiol. 33(6), 589–593 (2012)
Bureau of Infectious Disease and Laboratory Sciences: 2015 statewide antibiogram report. Massachusetts State Public Health Laboratory (2016). Accessed 24 Jan 2017
Centers for Disease Control and Prevention: Antibiotic resistance threats in the United States, 2013. U.S. Department of Health and Human Services (2015). https://www.cdc.gov/drugresistance/pdf/ar-threats-2013-508.pdf
Centers For Disease Control and Prevention: About Antimicrobial Resistance. Antibiotic/Antimicrobial Resistance (2018). https://www.cdc.gov/drugresistance/about.html. Accessed May 2018
Crnich, C., Safdar, N., Robinson, J., Zimmerman, D.: Longitudinal trends in antibiotic resistance in US nursing homes, 2000–2004. Infect. Control Hosp. Epidemiol. 28(8), 1006–1008 (2017)
Food and Drug Administration: Stewardship guidelines. The National Antimicrobial Resistance Monitoring System (2016)
Hastey, C., et al.: Changes in the antibiotic susceptibility of anaerobic bacteria from 2007–2009 to 2010–2012 based on CLSI methodology. Anaerobe 42, 27–30 (2016)
James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R, 1st edn. Springer, New York (2013)
Lagace-Wiens, P., et al.: Trends in antibiotic resistance over time among pathogens from Canadian hospitals: results of the CANWARD study 2007–11. J. Antimicrob. Chemother. 6, i23–i29 (2013)
Moore, D.: The Basic Practice of Statistics, 4th edn. WH Freeman, New York (2007)
Nau, R.: Statistical forecasting: notes on regression and time series analysis. Fuqua School of Buisness, Duke University, (2018). https://people.duke.edu/~rnau/411home.htm. Accessed May 2018
O’Neill, J.: Tackling Drug-Resistant Infections Globally: Final Report and Reccomendations. The Review on Antimicrobial Resistance (2016). https://amr-review.org/sites/default/files/160525_Final%20paper_with%20cover.pdf
Seidman, S., et al.: Longitudinal comparison of antibiotic resistance in diarrheagenic and non-pathogenic Escherichia coli from young Tanzanian children. Front. Microbiol. 7, 1420 (2016)
Smola, A., Scholkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2014)
Tlachac, M., Rundensteiner, E., Barton, K., Troppy, S., Beaulac, K., Doron, S.: Predicting future antibiotic susceptibility using regression-based methods on longitudinal Massachusetts antibiogram data. In: Proceedings of the 11th International Conference on Health Informatics (2018)
Tlachac, M., et al.: CASSIA: an assistant for identifying clinically and statistically significant decreases in antimicrobial susceptibility. In: 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (2018)
Ventola, L.: The antibiotic resistance crisis. Pharm. Ther. 40(4), 277–283 (2015)
World Health Organization: Antimicrobial resistance global report on surveillance 2014. World Health Organization (2014). http://apps.who.int/iris/bitstream/handle/10665/112642/9789241564748_eng.pdf
Yang, H., King, L.: Localized support vector regression for time series prediction. Nuerocomputing 72(10–12), 2659–2669 (2009)
Acknowledgements
This work is supported by WPI and the US Department of Education P200A150306: GAANN Fellowships to Support Data-Driven Computing Research. We thank Jian Zou, PhD and Tom Hartvigsen at WPI, Matthew Tlachac at University of Minnesota, and Alfred DeMaria, MD and Monina Klevens, DMD at MDPH for their input on this work. We thank the DSRG community at WPI for providing a stimulating research environment.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Tlachac, M.L., Rundensteiner, E.A., Troppy, T.S., Beaulac, K., Doron, S., Barton, K. (2019). Predictive Modeling of Emerging Antibiotic Resistance Trends. In: Cliquet Jr., A., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2018. Communications in Computer and Information Science, vol 1024. Springer, Cham. https://doi.org/10.1007/978-3-030-29196-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-29196-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29195-2
Online ISBN: 978-3-030-29196-9
eBook Packages: Computer ScienceComputer Science (R0)