Systematic review of the use of time series data in the study of antimicrobial consumption and Pseudomonas aeruginosa resistance

Molecular characterization and differential effects of levofloxacin and ciprofloxacin on the potential for developing quinolone resistance among clinical Pseudomonas aeruginosa isolates

Background

Fluoroquinolones are some of the most used antimicrobial agents for the treatment of Pseudomonas aeruginosa. This study aimed at exploring the differential activity of ciprofloxacin and levofloxacin on the selection of resistance among P. aeruginosa isolates at our medical center.

Methods

233 P. aeruginosa clinical isolates were included in this study. Antimicrobial susceptibility testing (AST) was done using disk diffusion and broth microdilution assays. Random Amplification of Polymorphic DNA (RAPD) was done to determine the genetic relatedness between the isolates. Induction of resistance against ciprofloxacin and levofloxacin was done on 19 isolates. Fitness cost assay was done on the 38 induced mutants and their parental isolates. Finally, whole genome sequencing was done on 16 induced mutants and their 8 parental isolates.

Results

AST results showed that aztreonam had the highest non-susceptibility. RAPD results identified 18 clusters. The 19 P. aeruginosa isolates that were induced against ciprofloxacin and levofloxacin yielded MICs ranging between 16 and 256 μg/mL. Levofloxacin required fewer passages in 10 isolates and the same number of passages in 9 isolates as compared to ciprofloxacin to reach their breakpoints. Fitness cost results showed that 12 and 10 induced mutants against ciprofloxacin and levofloxacin, respectively, had higher fitness cost when compared to their parental isolates. Whole genome sequencing results showed that resistance to ciprofloxacin and levofloxacin in sequenced mutants were mainly associated with alterations in gyrA, gyrB and parC genes.

Conclusion

Understanding resistance patterns and risk factors associated with infections is crucial to decrease the emerging threat of antimicrobial resistance.

Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app

Background

Antimicrobial resistance has emerged as one of the foremost public health troubles of the 21st century. This has ended in a public health disaster of the global situation, which threatens the exercise of present-day remedy. There is an urgent requirement for a cost-effective strategy to reduce antimicrobial resistance. Infectious disease control researchers most often analyze and predict antimicrobial resistance rate data that includes zeros or ones. Commonly used time-series analysis such as autoregressive moving average model is inappropriate for such data and may arrive at biased results.

Objective

This study aims to propose a time-series model for continuous rates or proportions when the interval of series includes zeros or ones and compares the model with existing models.

Data

The Escherichia coli, isolated from blood cultures showing variable susceptibility results to different antimicrobial agents, has been obtained from a clinical microbiology laboratory of a tertiary care hospital, Udupi district, Karnataka, during the years between 2011 and 2019.

Methodology

We proposed a Degenerate Beta Autoregressive model which is a mixture of continuous and discrete distributions with probability mass at zero or one. The proposed model includes autoregressive terms along with explanatory variables. The estimation is done using maximum likelihood with a non-linear optimization algorithm. An R shiny app has been provided for the same.

Results

The proposed Degenerate Beta Autoregressive model performed well compared to the existing autoregressive moving average models. The forecasted antimicrobial resistance rate has been obtained for the next 6 months.

Conclusion

The findings of this article could be beneficial to the infectious disease researchers to use an appropriate time-series model to forecast the resistance rate for the future and to have better or advance public health policies to control the rise in resistance rate.

Clinical predictors of mortality in patients with pseudomonas aeruginosa infection

BACKGROUND

Infections caused by Pseudomonas aeruginosa are difficult to treat with a significant cost and burden. In Lebanon, P. aeruginosa is one of the most common organisms in ventilator-associated pneumonia (VAP). P. aeruginosa has developed widespread resistance to multiple antimicrobial agents such as fluoroquinolones and carbapenems. We aimed at identifying risk factors associated for P. aeruginosa infections as well as identifying independent risk factors for developing septic shock and in-hospital mortality.

METHODS

We used a cross-sectional study design where we included patients with documented P. aeruginosa cultures who developed an infection after obtaining written consent. Two multivariable regression models were used to determine independent predictors of septic shock and mortality.

RESULTS

During the observed period of 30 months 196 patients were recruited. The most common predisposing factor was antibiotic use for more than 48 hours within 30 days (55%). The prevalence of multi-drug resistant (MDR) P. aeruginosa was 10%. The strongest predictors of mortality were steroid use (aOR = 3.4), respiratory failure (aOR = 7.3), identified respiratory cultures (aOR = 6.0), malignancy (aOR = 9.8), septic shock (aOR = 18.6), and hemodialysis (aOR = 30.9).

CONCLUSION

Understanding resistance patterns and risk factors associated with mortality is crucial to personalize treatment based on risk level and to decrease the emerging threat of antimicrobial resistance.

Trends, relationships and case attribution of antibiotic resistance between children and environmental sources in rural India

Bacterial antibiotic resistance is an important global health threat and the interfaces of antibiotic resistance between humans, animals and the environment are complex. We aimed to determine the associations and overtime trends of antibiotic resistance between humans, animals and water sources from the same area and time and estimate attribution of the other sources to cases of human antibiotic resistance. A total of 125 children (aged 1-3 years old) had stool samples analysed for antibiotic-resistant bacteria at seven time points over two years, with simultaneous collection of samples of animal stools and water sources in a rural Indian community. Newey-West regression models were used to calculate temporal associations, the source with the most statistically significant relationships was household drinking water. This is supported by use of SourceR attribution modelling, that estimated the mean attribution of cases of antibiotic resistance in the children from animals, household drinking water and wastewater, at each time point and location, to be 12.6% (95% CI 4.4-20.9%), 12.1% (CI 3.4-20.7%) and 10.3% (CI 3.2-17.3%) respectively. This underlines the importance of the 'one health' concept and requires further research. Also, most of the significant trends over time were negative, suggesting a possible generalised improvement locally.

Association of Antibiotic Use with the Resistance Epidemiology of Pseudomonas aeruginosa in a Hospital Setting: A Four-Year Retrospective Time Series Analysis

Background: Even though, Pseudomonas aeruginosa is a common cause of hospital-acquired infections, treatment is challenging because of decreasing rates of susceptibility to many broad-spectrum antibiotics. Methods: Consumption data of eight broad spectrum antimicrobial agents and resistance rates of P. aeruginosa were collected for 48 consecutive months. Autoregressive integrated moving average (ARIMA) and transfer functions models were used to develop relationships between antibiotic use and resistance. Results: Positive correlations between P. aeruginosa resistance and uses of ciprofloxacin (p < 0.001), meropenem (p < 0.001), and cefepime (p = 0.005) were identified. Transfer function models showed the quantified effect of each of these antibiotics on resistance. Regarding levofloxacin, ceftazidime, piperacillin/tazobactam and imipenem, no significant relationships were found. For ceftazidime and levofloxacin, this was probably due to their low consumption, while for imipenem the reason can possibly be ascribed to the already high established P. aeruginosa resistance in the hospital. Conclusion: In the hospital setting, the effect of antimicrobial agents’ consumption on the susceptibility epidemiology of P. aeruginosa differs significantly for each one of them. In this study, the role of precedent use of meropenem, cefepime and ciprofloxacin was quantified in the development of P. aeruginosa resistance.

Epidemiology and risk factors for healthcare-associated infections caused by Pseudomonas aeruginosa

Pseudomonas aeruginosa (PA) is a globally recognized cause of healthcare-associated infections (HAIs). The aim of our cross-sectional study, conducted in a Serbian tertiary care hospital, was to investigate clinical characteristics of HAIs caused by the PA, the prevalence of various drug-resistant phenotypes of this pathogen, and risk factors for their occurrence. Prolonged ICU stay and previous carbapenem administration were independent risk factors for HAIs caused by carbapenem-resistant PA, while HAIs caused by multidrug-resistant PA were more frequent in patients with prolonged stay in an ICU, who were previously hospitalized at another department and previously treated with aminoglycosides, fluoroquinolones or glycopeptides. The prolonged ICU stay was the only significant risk factor for HAIs caused by extensively drug-resistant PA. To decrease the incidence of HAIs caused by drug-resistant PA, a multifaceted approach is necessary, including staff education, antibiotic stewardship, improving hygiene, shortening hospitalization, and minimizing exposure to invasive medical procedures/devices.