Imperial College London
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DrBernardHernandez Perez

Faculty of Medicine, Department of Infectious Disease

Research Fellow
 
 
 
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Contact

 

b.hernandez-perez Website CV

 
 
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Location

 

B420Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Hernandez:2021:10.3390/antibiotics10101267,
author = {Hernandez, B and Herrero-Viñas, P and Rawson, TM and Moore, LSP and Holmes, A and Georgiou, P},
doi = {10.3390/antibiotics10101267},
journal = {Antibiotics},
pages = {1--16},
title = {Resistance trend estimation using regression analysis to enhance antimicrobial surveillance: a multi-centre study in London 2009-2016},
url = {http://dx.doi.org/10.3390/antibiotics10101267},
volume = {10},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In the last years, there has been an increase of antimicrobial resistance rates around the world with the misuse and overuse of antimicrobials as one of the main leading drivers. In response to this threat, a variety of initiatives have arisen to promote the efficient use of antimicrobials. These initiatives rely on antimicrobial surveillance systems to promote appropriate prescription practices and are provided by national or global health care institutions with limited consideration of the variations within hospitals. As a consequence, physicians’ adherence to these generic guidelines is still limited. To fill this gap, this work presents an automated approach to performing local antimicrobial surveillance from microbiology data. Moreover, in addition to the commonly reported resistance rates, this work estimates secular resistance trends through regression analysis to provide a single value that effectively communicates the resistance trend to a wider audience. The methods considered for trend estimation were ordinary least squares regression, weighted least squares regression with weights inversely proportional to the number of microbiology records available and autoregressive integrated moving average. Among these, weighted least squares regression was found to be the most robust against changes in the granularity of the time series and presented the best performance. To validate the results, three case studies have been thoroughly compared with the existing literature: (i) Escherichia coli in urine cultures; (ii) Escherichia coli in blood cultures; and (iii) Staphylococcus aureus in wound cultures. The benefits of providing local rather than general antimicrobial surveillance data of a higher quality is two fold. Firstly, it has the potential to stimulate engagement among physicians to strengthen their knowledge and awareness on antimicrobial resistance which might encourage prescribers to change their prescription habits more willingly. Moreover, it pro
AU  - Hernandez,B
AU  - Herrero-Viñas,P
AU  - Rawson,TM
AU  - Moore,LSP
AU  - Holmes,A
AU  - Georgiou,P
DO  - 10.3390/antibiotics10101267
EP  - 16
PY  - 2021///
SN  - 2079-6382
SP  - 1
TI  - Resistance trend estimation using regression analysis to enhance antimicrobial surveillance: a multi-centre study in London 2009-2016
T2  - Antibiotics
UR  - http://dx.doi.org/10.3390/antibiotics10101267
UR  - https://www.mdpi.com/2079-6382/10/10/1267
UR  - http://hdl.handle.net/10044/1/92613
VL  - 10
ER  -
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