scholarly journals Resistance Trend Estimation Using Regression Analysis to Enhance Antimicrobial Surveillance: A Multi-Centre Study in London 2009–2016

Antibiotics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1267
Author(s):  
Bernard Hernandez ◽  
Pau Herrero-Viñas ◽  
Timothy M. Rawson ◽  
Luke S. P. Moore ◽  
Alison H. Holmes ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Regression Analysis ◽  
Antimicrobial Resistance ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Least Squares Regression ◽  
Trend Estimation ◽  
Antimicrobial Surveillance ◽  
Wide Range ◽  
Resistance Rates

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 provides fundamental knowledge to the wide range of stakeholders to revise and potentially tailor existing guidelines to the specific needs of each hospital.

scholarly journals ESBL Activity, MDR, and Carbapenem Resistance among Predominant Enterobacterales Isolated in 2019

Antibiotics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 744
Author(s):  
Altaf Bandy ◽  
Bilal Tantry
Keyword(s):  
Escherichia Coli ◽  
Saudi Arabia ◽  
Antimicrobial Resistance ◽  
Carbapenem Resistance ◽  
Resistant Bacteria ◽  
Sociodemographic Characteristics ◽  
Chi Square ◽  
E Coli ◽  
Chi Square Test ◽  
Resistance Rates

Antimicrobial-resistance in Enterobacterales is a serious concern in Saudi Arabia. The present study retrospectively analyzed the antibiograms of Enterobacterales identified from 1 January 2019 to 31 December 2019 from a referral hospital in the Aljouf region of Saudi Arabia. The revised document of the Centers for Disease Control (CDC) CR-2015 and Magiorakos et al.’s document were used to define carbapenem resistance and classify resistant bacteria, respectively. The association of carbapenem resistance, MDR, and ESBL with various sociodemographic characteristics was assessed by the chi-square test and odds ratios. In total, 617 Enterobacterales were identified. The predominant (n = 533 (86.4%)) isolates consisted of 232 (37.6%), 200 (32.4%), and 101 (16.4%) Escherichia coli, Klebsiella pneumoniae, and Proteus mirabilis, respectively. In general, 432 (81.0%) and 128 (24.0%) isolates were of MDR and ESBL, respectively. The MDR strains were recovered in higher frequency from intensive care units (OR = 3.24 (1.78–5.91); p < 0.01). E. coli and K. pneumoniae resistance rates to imipenem (2.55 (1.21–5.37); p < 0.01) and meropenem (2.18 (1.01–4.67); p < 0.04), respectively, were significantly higher in winter. The data emphasize that MDR isolates among Enterobacterales are highly prevalent. The studied Enterobacterales exhibited seasonal variation in antimicrobial resistance rates towards carbapenems and ESBL activity.

Modeling and Interpretation of Tidal Turbine Vibration Through Weighted Least Squares Regression

2020 ◽  
Vol 50 (4) ◽  
pp. 1252-1259 ◽  
Author(s):  
Grant S. Galloway ◽  
Victoria M. Catterson ◽  
Craig Love ◽  
Andrew Robb ◽  
Thomas Fay
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Least Squares Regression ◽  
Tidal Turbine

scholarly journals Tolerance Intervals in a Heteroscedastic Linear Regression Context with Applications to Aerospace Equipment Surveillance

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Janet Myhre ◽  
Daniel R. Jeske ◽  
Michael Rennie ◽  
Yingtao Bi
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Performance Metrics ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Automated System ◽  
Least Squares Regression ◽  
Data Set ◽  
Tolerance Intervals ◽  
Power Of The Test

A heteroscedastic linear regression model is developed from plausible assumptions that describe the time evolution of performance metrics for equipment. The inherited motivation for the related weighted least squares analysis of the model is an essential and attractive selling point to engineers with interest in equipment surveillance methodologies. A simple test for the significance of the heteroscedasticity suggested by a data set is derived and a simulation study is used to evaluate the power of the test and compare it with several other applicable tests that were designed under different contexts. Tolerance intervals within the context of the model are derived, thus generalizing well-known tolerance intervals for ordinary least squares regression. Use of the model and its associated analyses is illustrated with an aerospace application where hundreds of electronic components are continuously monitored by an automated system that flags components that are suspected of unusual degradation patterns.

scholarly journals Multi-omic surveillance of Escherichia coli and Klebsiella spp. in hospital sink drains and patients

2020 ◽  
Author(s):  
B Constantinides ◽  
KK Chau ◽  
TP Quan ◽  
G Rodger ◽  
M Andersson ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Antimicrobial Resistance ◽  
Resistance Genes ◽  
Bacterial Infections ◽  
Wide Spectrum ◽  
Klebsiella Oxytoca ◽  
Clinical Disease ◽  
E Coli ◽  
Antimicrobial Resistance Genes ◽  
Wide Range

ABSTRACTEscherichia coli and Klebsiella spp. are important human pathogens that cause a wide spectrum of clinical disease. In healthcare settings, sinks and other wastewater sites have been shown to be reservoirs of antimicrobial-resistant E. coli and Klebsiella spp., particularly in the context of outbreaks of resistant strains amongst patients. Without focusing exclusively on resistance markers or a clinical outbreak, we demonstrate that many hospital sink drains are abundantly and persistently colonised with diverse populations of E. coli, Klebsiella pneumoniae and Klebsiella oxytoca, including both antimicrobial-resistant and susceptible strains. Using whole genome sequencing (WGS) of 439 isolates, we show that environmental bacterial populations are largely structured by ward and sink, with only a handful of lineages, such as E. coli ST635, being widely distributed, suggesting different prevailing ecologies which may vary as a result of different inputs and selection pressures. WGS of 46 contemporaneous patient isolates identified one (2%; 95% CI 0.05-11%) E. coli urine infection-associated isolate with high similarity to a prior sink isolate, suggesting that sinks may contribute to up to 10% of infections caused by these organisms in patients on the ward over the same timeframe. Using metagenomics from 20 sink-timepoints, we show that sinks also harbour many clinically relevant antimicrobial resistance genes including blaCTX-M, blaSHV and mcr, and may act as niches for the exchange and amplification of these genes. Our study reinforces the potential role of sinks in contributing to Enterobacterales infection and antimicrobial resistance in hospital patients, something that could be amenable to intervention.IMPORTANCEEscherichia coli and Klebsiella spp. cause a wide range of bacterial infections, including bloodstream, urine and lung infections. Previous studies have shown that sink drains in hospitals may be part of transmission chains in outbreaks of antimicrobial-resistant E. coli and Klebsiella spp., leading to colonisation and clinical disease in patients. We show that even in non-outbreak settings, contamination of sink drains by these bacteria is common across hospital wards, and that many antimicrobial resistance genes can be found and potentially exchanged in these sink drain sites. Our findings demonstrate that the colonisation of handwashing sink drains by these bacteria in hospitals is likely contributing to some infections in patients, and that additional work is needed to further quantify this risk, and to consider appropriate mitigating interventions.

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