Thomas F O'Brien1, Adam Clark1, Rob Peters1, John Stelling2. 1. Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. 2. Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. Electronic address: jstelling@whonet.org.
Abstract
OBJECTIVES: Surveillance of antimicrobial resistance (AMR) can now be automated to analyse the reports of microbiology laboratories continually without operator assistance. It can also be made comprehensive to monitor all the reports of all the world's microbiology laboratories. METHODS AND RESULTS: As illustrated through examples provided in this work, each clinical report can be scanned automatically by algorithms to suspect emerging problems and to prompt sampling to confirm such problems, now increasingly by nucleotide sequencing. An emerging problem may be an excess (clustering) of similar microbes owing to their spread among patients who are interrelated in some way, as by shared locations, caregivers or food products. Or it might be a microbe new to an area or to a laboratory but already seen nearby, such as Elizabethkingia anophelis or mcr-1-positive Escherichia coli. Automated early alerting of responders enables them to contain spread sooner and to avert infections downstream. 'Big Data' informatics now also enables surveillance of AMR to be made comprehensive, to monitor all reports of all the world's microbiology laboratories. Such orders of magnitude increase in analysed data would accordingly increase its granularity and thus detect many more global problems sooner. It would also reduce surveillance-blind areas where problems may now emerge and spread undetected. CONCLUSIONS: The world's microbiology laboratories need to integrate and analyse all of their reports for surveillance to make their own patients safer from existing and approaching problems otherwise hard to notice. Making automated surveillance an easy-to-adopt laboratory standard of care can make it comprehensive.
OBJECTIVES: Surveillance of antimicrobial resistance (AMR) can now be automated to analyse the reports of microbiology laboratories continually without operator assistance. It can also be made comprehensive to monitor all the reports of all the world's microbiology laboratories. METHODS AND RESULTS: As illustrated through examples provided in this work, each clinical report can be scanned automatically by algorithms to suspect emerging problems and to prompt sampling to confirm such problems, now increasingly by nucleotide sequencing. An emerging problem may be an excess (clustering) of similar microbes owing to their spread among patients who are interrelated in some way, as by shared locations, caregivers or food products. Or it might be a microbe new to an area or to a laboratory but already seen nearby, such as Elizabethkingia anophelis or mcr-1-positive Escherichia coli. Automated early alerting of responders enables them to contain spread sooner and to avert infections downstream. 'Big Data' informatics now also enables surveillance of AMR to be made comprehensive, to monitor all reports of all the world's microbiology laboratories. Such orders of magnitude increase in analysed data would accordingly increase its granularity and thus detect many more global problems sooner. It would also reduce surveillance-blind areas where problems may now emerge and spread undetected. CONCLUSIONS: The world's microbiology laboratories need to integrate and analyse all of their reports for surveillance to make their own patients safer from existing and approaching problems otherwise hard to notice. Making automated surveillance an easy-to-adopt laboratory standard of care can make it comprehensive.
Authors: Cherry Lim; Thyl Miliya; Vilada Chansamouth; Myint Thazin Aung; Abhilasha Karkey; Prapit Teparrukkul; Batra Rahul; Nguyen Phu Huong Lan; John Stelling; Paul Turner; Elizabeth Ashley; H Rogier van Doorn; Htet Naing Lin; Clare Ling; Soawapak Hinjoy; Sopon Iamsirithaworn; Susanna Dunachie; Tri Wangrangsimakul; Viriya Hantrakun; William Schilling; Lam Minh Yen; Le Van Tan; Htay Htay Hlaing; Mayfong Mayxay; Manivanh Vongsouvath; Buddha Basnyat; Jonathan Edgeworth; Sharon J Peacock; Guy Thwaites; Nicholas Pj Day; Ben S Cooper; Direk Limmathurotsakul Journal: J Med Internet Res Date: 2020-10-02 Impact factor: 5.428