Relative effects of barrier precautions and topical antibiotics on nosocomial bacterial transmission: results of multi-compartment models

Antimicrobial Resistance in Ventilator-Associated Pneumonia: Predictive Microbiology and Evidence-Based Therapy

Ventilator-associated pneumonia (VAP) is a serious intensive care unit (ICU)-related infection in mechanically ventilated patients that is frequent, as more than half of antibiotics prescriptions in ICU are due to VAP. Various risk factors and diagnostic criteria for VAP have been referred to in different settings. The estimated attributable mortality of VAP can go up to 50%, which is higher in cases of antimicrobial-resistant VAP. When the diagnosis of pneumonia in a mechanically ventilated patient is made, initiation of effective antimicrobial therapy must be prompt. Microbiological diagnosis of VAP is required to optimize timely therapy since effective early treatment is fundamental for better outcomes, with controversy continuing regarding optimal sampling and testing. Understanding the role of antimicrobial resistance in the context of VAP is crucial in the era of continuously evolving antimicrobial-resistant clones that represent an urgent threat to global health. This review is focused on the risk factors for antimicrobial resistance in adult VAP and its novel microbiological tools. It aims to summarize the current evidence-based knowledge about the mechanisms of resistance in VAP caused by multidrug-resistant bacteria in clinical settings with focus on Gram-negative pathogens. It highlights the evidence-based antimicrobial management and prevention of drug-resistant VAP. It also addresses emerging concepts related to predictive microbiology in VAP and sheds lights on VAP in the context of coronavirus disease 2019 (COVID-19).

Evaluation of GeneXpert PA assay compared to genomic and (semi-)quantitative culture methods for direct detection of Pseudomonas aeruginosa in endotracheal aspirates

Introduction

Pseudomonas aeruginosa is a common cause of ventilator-associated pneumonia (VAP). Rapid and accurate detection of lower respiratory tract colonization and/or infection with P. aeruginosa may advise targeted preventive (antibody-based) strategies and antibiotic therapy. To investigate this, we compared semi-quantitative culture results from 80 endotracheal aspirates (ETA) collected from mechanically-ventilated patients, to two culture and two non-culture-based methods for detection of P. aeruginosa.

Methods

P. aeruginosa-positive (n = 40) and -negative (n = 40) ETAs from mechanically ventilated patients analyzed initally by (i) routine semi-quantitative culture, were further analyzed with (ii) quantitative culture on chromogenic ChromID P. aeruginosa and blood agar; (iii) enrichment in brain heart infusion broth followed by plating on blood agar and ChromID P. aeruginosa; (iv) O-antigen acetylase gene-based TaqMan qPCR; and (v) GeneXpert PA PCR assay.

Results

Of the 80 ETA samples included, one sample that was negative for P. aeruginosa by semi-quantitative culture was found to be positive by the other four methods, and was included in an “extended” gold standard panel. Based on this extended gold standard, both semi-quantitative culture and the GeneXpert PA assay showed 97.6% sensitivity and 100% specificity. The quantitative culture, enrichment culture and O-antigen acetylase gene-based TaqMan qPCR had a sensitivity of 97.6%, 89.5%, 92.7%, and a specificity of 97.4%, 100%, and 71.1%, respectively.

Conclusion

This first evaluation of the GeneXpert PA assay with ETA samples found it to be as sensitive and specific as the routine, hospital-based semi-quantitative culture method. Additionally, the GeneXpert PA assay is easy to perform (hands-on time ≈ 5 min) and rapid (≈ 55 min assay time). The combination of the high sensitivity and high specificity together with the rapid acquisition of results makes the GeneXpert PA assay a highly recommended screening technique. Where this equipment is not available, semi-quantitative culture remains the most sensitive of the culture methods evaluated here for P. aeruginosa detection in ETA samples.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13756-021-00978-9.

Simulation models for transmission of health care-associated infection: A systematic review

BACKGROUND

Health care-associated infections (HAIs) are a global health burden because of their significant impact on patient health and health care systems. Mechanistic simulation modeling that captures the dynamics between patients, pathogens, and the environment is increasingly being used to improve understanding of epidemiological patterns of HAIs and to facilitate decisions on infection prevention and control (IPC). The purpose of this review is to present a systematic review to establish (1) how simulation models have been used to investigate HAIs and their mitigation and (2) how these models have evolved over time, as well as identify (3) gaps in their adoption and (4) useful directions for their future development.

METHODS

The review involved a systematic search and identification of studies using system dynamics, discrete event simulation, and agent-based model to study HAIs.

RESULTS

The complexity of simulation models developed for HAIs significantly increased but heavily concentrated on transmission dynamics of methicillin-resistant Staphylococcus aureus in the hospitals of high-income countries. Neither HAIs in other health care settings, the influence of contact networks within a health care facility, nor patient sharing and referring networks across health care settings were sufficiently understood.

CONCLUSIONS

This systematic review provides a broader overview of existing simulation models in HAIs to identify the gaps and to direct and facilitate further development of appropriate models in this emerging field.

Modelling the transmission of healthcare associated infections: a systematic review

Background

Dynamic transmission models are increasingly being used to improve our understanding of the epidemiology of healthcare-associated infections (HCAI). However, there has been no recent comprehensive review of this emerging field. This paper summarises how mathematical models have informed the field of HCAI and how methods have developed over time.

Methods

MEDLINE, EMBASE, Scopus, CINAHL plus and Global Health databases were systematically searched for dynamic mathematical models of HCAI transmission and/or the dynamics of antimicrobial resistance in healthcare settings.

Results

In total, 96 papers met the eligibility criteria. The main research themes considered were evaluation of infection control effectiveness (64%), variability in transmission routes (7%), the impact of movement patterns between healthcare institutes (5%), the development of antimicrobial resistance (3%), and strain competitiveness or co-colonisation with different strains (3%). Methicillin-resistant Staphylococcus aureus was the most commonly modelled HCAI (34%), followed by vancomycin resistant enterococci (16%). Other common HCAIs, e.g. Clostridum difficile, were rarely investigated (3%). Very few models have been published on HCAI from low or middle-income countries.

The first HCAI model has looked at antimicrobial resistance in hospital settings using compartmental deterministic approaches. Stochastic models (which include the role of chance in the transmission process) are becoming increasingly common. Model calibration (inference of unknown parameters by fitting models to data) and sensitivity analysis are comparatively uncommon, occurring in 35% and 36% of studies respectively, but their application is increasing. Only 5% of models compared their predictions to external data.

Conclusions

Transmission models have been used to understand complex systems and to predict the impact of control policies. Methods have generally improved, with an increased use of stochastic models, and more advanced methods for formal model fitting and sensitivity analyses. Insights gained from these models could be broadened to a wider range of pathogens and settings. Improvements in the availability of data and statistical methods could enhance the predictive ability of models.

Is patient isolation the single most important measure to prevent the spread of multidrug-resistant pathogens?

Isolation or cohorting of infected patients is an old concept. Its purpose is to prevent the transmission of microorganisms from infected or colonized patients to other patients, hospital visitors, and health care workers, who may subsequently transmit them to other patients or become infected or colonized themselves. Because the process of isolating patients is expensive, time-consuming, often uncomfortable for patients and may impede care, it should be implemented only when necessary. Conversely, failure to isolate a patient with multidrug-resistant microorganisms may lead to adverse outcomes, and may ultimately be expensive when one considers the direct costs of an outbreak investigation and the indirect costs of lost productivity. In this review, we argue that contact precautions are essential to control the spread of epidemic and endemic multidrug-resistant microorganisms, and discuss limitations of some available data.

Linking antimicrobial prescribing to antimicrobial resistance in the ICU: before and after an antimicrobial stewardship program

Antimicrobials are an effective treatment for many types of infections, but their overuse promotes the spread of resistant microorganisms that defy conventional treatments and complicate patient care. In 2009, an antimicrobial stewardship program was implemented at Mount Sinai Hospital (MSH, Toronto, Canada). Components of this program were to alter the fraction of patients prescribed antimicrobials, to shorten the average duration of treatment, and to alter the types of antimicrobials prescribed. These components were incorporated into a mathematical model that was compared to data reporting the number of patients colonized with Pseudomonas aeruginosa and the number of patients colonized with antimicrobial-resistant P. aeruginosa first isolates before and after the antimicrobial stewardship program. Our analysis shows that the reported decrease in the number of patients colonized was due to treating fewer patients, while the reported decrease in the number of patients colonized with resistant P. aeruginosa was due to the combined effect of treating fewer patients and altering the types of antimicrobials prescribed. We also find that shortening the average duration of treatment was unlikely to have produced any noticeable effects and that further reducing the fraction of patients prescribed antimicrobials would most substantially reduce P. aeruginosa antimicrobial resistance in the future. The analytical framework that we derive considers the effect of colonization pressure on infection spread and can be used to interpret clinical antimicrobial resistance data to assess different aspects of antimicrobial stewardship within the ecological context of the intensive care unit.

Risk factors for previously unknown meticillin-resistant Staphylococcus aureus carriage on admission to 13 surgical wards in Europe

BACKGROUND

Early identification of meticillin-resistant Staphylococcus aureus (MRSA) carriers may be helpful for clinical and epidemiological reasons.

AIM

To identify and compare risk factors of previously unknown MRSA carriage on admission to 13 surgical wards in France, Greece, Italy, and Spain.

METHODS

The study was a prospective observational cohort study which enrolled consecutive patients screened for MRSA on admission to surgical wards. Sociodemographic data, comorbidities and possible risk factors for MRSA were recorded. A multivariate logistic regression model was used to predict probabilities of previously unknown MRSA colonization on admission based on patient characteristics. Prediction rules for MRSA carriage were developed and evaluated using the c-statistic.

FINDINGS

Of 2901 patients enrolled, admission screening identified 111 (3.8%) new MRSA carriers. Independent risk factors for MRSA carriage were urinary catheterization (odds ratio: 4.4; 95% confidence interval: 2.0-9.9), nursing home residency (3.8; 1.9-7.7), chronic skin disease (2.9; 1.5-5.8), wounds/ulcers (2.4; 1.5-4.0), recent hospitalization (2.2; 1.5-3.3), diabetes (1.6, 1.02-2.5), and age >70 years (1.5; 1.03-2.3). However, risk factors varied between centres. The c-statistic for the common prediction rule for all centres was 0.64, indicating limited predictive power.

CONCLUSIONS

Risk profiles for MRSA carriers vary between surgical wards in European countries. Identifying local risk factors is important, as a common European prediction rule was found to be of limited clinical value.

Antibiotic resistance profiles and virulence markers of Pseudomonas aeruginosa strains isolated from composts

The aim of our work was to determine the presence of Pseudomonas aeruginosa in compost raw materials, immature and mature compost, and compost-treated soil. Twenty-five strains of P. aeruginosa were isolated from a raw material (plant straw), immature and mature compost and compost-treated soil samples. The strains were identified using the PCR method for the detection of species specific variable regions of 16S rDNA. Strains were examined for the presence of five different virulence-related gene sequences (exoA, exoU, exoT, exoS and exoY) and their antibiotic resistance profiles were determined. Based on our results, species P. aeruginosa can reach significant numbers (up to 10(6) MPN/g sample) during composting and 92.0% of the isolated strains carrying at least two gene sequences encoding toxic proteins. Various types of drug resistance were detected among compost originating strains, mainly against third generation Cephalosporins and Carbapenems. Six isolates were able to resist two different classes of antibiotics (third generation Cephalosporins and Carbapenems, wide spectrum Penicillins or Aminoglycosides, respectively). Based on our results, composts can be a source of P. aeruginosa and might be a concern to individuals susceptible to this opportunistic pathogen.

Modeling nosocomial transmission of rotavirus in pediatric wards

Nosocomial transmission of viral and bacterial infections is a major problem worldwide, affecting millions of patients (and causing hundreds of thousands of deaths) per year. Rotavirus infections affect most children worldwide at least once before age five. We present here deterministic and stochastic models for the transmission of rotavirus in a pediatric hospital ward and draw on published data to compare the efficacy of several possible control measures in reducing the number of infections during a 90-day outbreak, including cohorting, changes in healthcare worker-patient ratio, improving compliance with preventive hygiene measures, and vaccination. Although recently approved vaccines have potential to curtail most nosocomial rotavirus transmission in the future, even short-term improvement in preventive hygiene compliance following contact with symptomatic patients may significantly limit transmission as well, and remains an important control measure, especially where resources are limited.

Qualitative analysis of models with different treatment protocols to prevent antibiotic resistance

This paper is concerned with the qualitative analysis of two models [S. Bonhoeffer, M. Lipsitch, B.R. Levin, Evaluating treatment protocols to prevent antibiotic resistance, Proc. Natl. Acad. Sci. USA 94 (1997) 12106] for different treatment protocols to prevent antibiotic resistance. Detailed qualitative analysis about the local or global stability of the equilibria of both models is carried out in term of the basic reproduction number R(0). For the model with a single antibiotic therapy, we show that if R(0)<1, then the disease-free equilibrium is globally asymptotically stable; if R(0)>1, then the disease-endemic equilibrium is globally asymptotically stable. For the model with multiple antibiotic therapies, stabilities of various equilibria are analyzed and combining treatment is shown better than cycling treatment. Numerical simulations are performed to show that the dynamical properties depend intimately upon the parameters.

The impact of different antibiotic regimens on the emergence of antimicrobial-resistant bacteria

Backgroud

The emergence and ongoing spread of antimicrobial-resistant bacteria is a major public health threat. Infections caused by antimicrobial-resistant bacteria are associated with substantially higher rates of morbidity and mortality compared to infections caused by antimicrobial-susceptible bacteria. The emergence and spread of these bacteria is complex and requires incorporating numerous interrelated factors which clinical studies cannot adequately address.

Methods/Principal Findings

A model is created which incorporates several key factors contributing to the emergence and spread of resistant bacteria including the effects of the immune system, acquisition of resistance genes and antimicrobial exposure. The model identifies key strategies which would limit the emergence of antimicrobial-resistant bacterial strains. Specifically, the simulations show that early initiation of antimicrobial therapy and combination therapy with two antibiotics prevents the emergence of resistant bacteria, whereas shorter courses of therapy and sequential administration of antibiotics promote the emergence of resistant strains.

Conclusions/Significance

The principal findings suggest that (i) shorter lengths of antibiotic therapy and early interruption of antibiotic therapy provide an advantage for the resistant strains, (ii) combination therapy with two antibiotics prevents the emergence of resistance strains in contrast to sequential antibiotic therapy, and (iii) early initiation of antibiotics is among the most important factors preventing the emergence of resistant strains. These findings provide new insights into strategies aimed at optimizing the administration of antimicrobials for the treatment of infections and the prevention of the emergence of antimicrobial resistance.

Controlling nosocomial infection based on structure of hospital social networks

Nosocomial infection (i.e. infection in healthcare facilities) raises a serious public health problem, as implied by the existence of pathogens characteristic to healthcare facilities such as methicillin-resistant Staphylococcus aureus and hospital-mediated outbreaks of influenza and severe acute respiratory syndrome. For general communities, epidemic modeling based on social networks is being recognized as a useful tool. However, disease propagation may occur in a healthcare facility in a manner different from that in a urban community setting due to different network architecture. We simulate stochastic susceptible-infected-recovered dynamics on social networks, which are based on observations in a hospital in Tokyo, to explore effective containment strategies against nosocomial infection. The observed social networks in the hospital have hierarchical and modular structure in which dense substructure such as departments, wards, and rooms, are globally but only loosely connected, and do not reveal extremely right-skewed distributions of the number of contacts per individual. We show that healthcare workers, particularly medical doctors, are main vectors (i.e. transmitters) of diseases on these networks. Intervention methods that restrict interaction between medical doctors and their visits to different wards shrink the final epidemic size more than intervention methods that directly protect patients, such as isolating patients in single rooms. By the same token, vaccinating doctors with priority rather than patients or nurses is more effective. Finally, vaccinating individuals with large betweenness centrality (frequency of mediating connection between pairs of individuals along the shortest paths) is superior to vaccinating ones with large connectedness to others or randomly chosen individuals, which was suggested by previous model studies.

Structure-function aspects of PstS in multi-drug-resistant Pseudomonas aeruginosa

The increasing prevalence of multi-drug-resistant (MDR) strains of Pseudomonas aeruginosa among critically ill humans is of significant concern. In the current study, we show that MDR clinical isolates of P. aeruginosa representing three distinct genotypes that display high virulence against intestinal epithelial cells, form novel appendage-like structures on their cell surfaces. These appendages contain PstS, an extracellular phosphate binding protein. Using anti-PstS antibodies, we determined that the PstS-rich appendages in MDR strains are involved in adherence to and disruption of the integrity of cultured intestinal epithelial cell monolayers. The outer surface-expressed PstS protein was also identified to be present in P. aeruginosa MPAO1, although to a lesser degree, and its role in conferring an adhesive and barrier disruptive phenotype against intestinal epithelial cells was confirmed using an isogenic DeltaPstS mutant. Formation of the PstS rich appendages was induced during phosphate limitation and completely suppressed in phosphate-rich media. Injection of MDR strains directly into the intestinal tract of surgically injured mice, a known model of phosphate limitation, caused high mortality rates (60%-100%). Repletion of intestinal phosphate in this model completely prevented mortality. Finally, significantly less outer surface PstS was observed in the MPAO1 mutant DeltaHxcR thus establishing a role for the alternative type II secretion system Hxc in outer surface PstS expression. Gene expression analysis performed by RT-PCR confirmed this finding and further demonstrated abundant expression of pstS analogous to pa5369, pstS analogous to pa0688/pa14-55410, and hxcX in MDR strains. Taken together, these studies provide evidence that outer surface PstS expression confers a highly virulent phenotype of MDR isolates against the intestinal epithelium that alters their adhesive and barrier disrupting properties against the intestinal epithelium.