Comparing intervention strategies for reducing Clostridioides difficile transmission in acute healthcare settings: an agent-based modeling study

An agent-based model on antimicrobial de-escalation in intensive care units: Implications on clinical trial design

Antimicrobial de-escalation refers to reducing the spectrum of antibiotics used in treating bacterial infections. This strategy is widely recommended in many antimicrobial stewardship programs and is believed to reduce patients' exposure to broad-spectrum antibiotics and prevent resistance. However, the ecological benefits of de-escalation have not been universally observed in clinical studies. This paper conducts computer simulations to assess the ecological effects of de-escalation on the resistance prevalence of Pseudomonas aeruginosa-a frequent pathogen causing nosocomial infections. Synthetic data produced by the models are then used to estimate the sample size and study period needed to observe the predicted effects in clinical trials. Our results show that de-escalation can reduce colonization and infections caused by bacterial strains resistant to the empiric antibiotic, limit the use of broad-spectrum antibiotics, and avoid inappropriate empiric therapies. Further, we show that de-escalation could reduce the overall super-infection incidence, and this benefit becomes more evident under good compliance with hand hygiene protocols among health care workers. Finally, we find that any clinical study aiming to observe the essential effects of de-escalation should involve at least ten arms and last for four years-a size never attained in prior studies. This study explains the controversial findings of de-escalation in previous clinical studies and illustrates how mathematical models can inform outcome expectations and guide the design of clinical studies.

Health care-associated Clostridioides difficile infection: Learning the perspectives of health care workers to build successful strategies

BACKGROUND

Clostridioides difficile (C difficile) is one of the most common health care-associated infections that negatively impact patient care and health care costs. This study takes a unique approach to C difficile infection (CDI) control by investigating key prevention obstacles through the perspectives of Stanford health care (SHC) frontline health care personnel.

METHODS

An anonymous qualitative survey was distributed at SHC, focusing on knowledge and practice of CDI prevention guidelines, as well as education, communication, and perspectives regarding CDI at SHC.

RESULTS

112 survey responses were analyzed. Our findings unveiled gaps in personnel's knowledge of C difficile diagnostic guidelines and revealed a need for targeted communication and guideline-focused education. Health care staff shared preferences and recommendations, with the majority recommending enhanced communication of guidelines and information as a strategy for reducing CDI rates. The findings were then used to design and propose internal recommendations for SHC to mitigate the gaps found.

DISCUSSION

Many guidelines and improvement strategies are based on strong scientific and medical foundations; however, it is important to ask whether these guidelines are effectively translated into practice. Frontline health care workers hold empirical perspectives that could be key in infection control.

CONCLUSIONS

Our findings emphasize the importance of including frontline health care personnel in infection prevention decision-making processes and the strategies presented here can be applied to mitigating infections in different health care settings.

Clostridioides Difficile: A Concise Review of Best Practices and Updates

Clostridioides difficile infection (CDI) is one of the most common and severe nosocomial infections worldwide. It can also affect healthy individuals in the community. The incidence of CDI has been on the rise globally for the past decade, necessitating a proactive approach to combat its spread; new strategies are being developed to enhance diagnostic accuracy and optimize treatment outcomes. Implementing the 2-step testing has increased diagnostic specificity, reducing the usage of CD-specific antibiotics with no concomitant increase in surgical complication rates. In 2021, the Infectious Diseases Society of America/Society for Healthcare Epidemiology of America (IDSA/SHEA) shifted its preference for initial treatment to fidaxomicin over vancomycin and metronidazole due to its lower recurrence rate. It also prioritized fidaxomicin for the treatment of recurrent CDI. There are new developments on the frontiers of fecal microbiota therapies, with RBX2660 and SER-109 approved recently by the FDA for prevention, with other microbiome-based therapies in various development and clinical trials. This review offers providers an updated and practical guide for CDI management.

Early coronavirus disease 2019 (COVID-19) pandemic effects on individual-level risk for healthcare-associated infections in hospitalized patients

OBJECTIVE

We compared the individual-level risk of hospital-onset infections with multidrug-resistant organisms (MDROs) in hospitalized patients prior to and during the coronavirus disease 2019 (COVID-19) pandemic. We also quantified the effects of COVID-19 diagnoses and intrahospital COVID-19 burden on subsequent MDRO infection risk.

DESIGN

Multicenter, retrospective, cohort study.

SETTING

Patient admission and clinical data were collected from 4 hospitals in the St. Louis area.

PATIENTS

Data were collected for patients admitted between January 2017 and August 2020, discharged no later than September 2020, and hospitalized ≥48 hours.

METHODS

Mixed-effects logistic regression models were fit to the data to estimate patients' individual-level risk of infection with MDRO pathogens of interest during hospitalization. Adjusted odds ratios were derived from regression models to quantify the effects of the COVID-19 period, COVID-19 diagnosis, and hospital-level COVID-19 burden on individual-level hospital-onset MDRO infection probabilities.

RESULTS

We calculated adjusted odds ratios for COVID-19-era hospital-onset Acinetobacter spp., P. aeruginosa and Enterobacteriaceae spp infections. Probabilities increased 2.64 (95% confidence interval [CI], 1.22-5.73) times, 1.44 (95% CI, 1.03-2.02) times, and 1.25 (95% CI, 1.00-1.58) times relative to the prepandemic period, respectively. COVID-19 patients were 4.18 (95% CI, 1.98-8.81) times more likely to acquire hospital-onset MDRO S. aureus infections.

CONCLUSIONS

Our results support the growing body of evidence indicating that the COVID-19 pandemic has increased hospital-onset MDRO infections.

Evaluating the impact of testing strategies for the detection of nosocomial COVID-19 in English hospitals through data-driven modeling

Introduction

During the first wave of the COVID-19 pandemic 293,204 inpatients in England tested positive for SARS-CoV-2. It is estimated that 1% of these cases were hospital-associated using European centre for disease prevention and control (ECDC) and Public Health England (PHE) definitions. Guidelines for preventing the spread of SARS-CoV-2 in hospitals have developed over time but the effectiveness and efficiency of testing strategies for preventing nosocomial transmission has not been explored.

Methods

Using an individual-based model, parameterised using multiple datasets, we simulated the transmission of SARS-CoV-2 to patients and healthcare workers between March and August 2020 and evaluated the efficacy of different testing strategies. These strategies were: 0) Testing only symptomatic patients on admission; 1) Testing all patients on admission; 2) Testing all patients on admission and again between days 5 and 7, and 3) Testing all patients on admission, and again at days 3, and 5-7. In addition to admissions testing, patients that develop a symptomatic infection while in hospital were tested under all strategies. We evaluated the impact of testing strategy, test characteristics and hospital-related factors on the number of nosocomial patient infections.

Results

Modelling suggests that 84.6% (95% CI: 84.3, 84.7) of community-acquired and 40.8% (40.3, 41.3) of hospital-associated SARS-CoV-2 infections are detectable before a patient is discharged from hospital. Testing all patients on admission and retesting after 3 or 5 days increases the proportion of nosocomial cases detected by 9.2%. Adding discharge testing increases detection by a further 1.5% (relative increase). Increasing occupancy rates, number of beds per bay, or the proportion of admissions wrongly suspected of having COVID-19 on admission and therefore incorrectly cohorted with COVID-19 patients, increases the rate of nosocomial transmission. Over 30,000 patients in England could have been discharged while incubating a non-detected SARS-CoV-2 infection during the first wave of the COVID-19 pandemic, of which 3.3% could have been identified by discharge screening. There was no significant difference in the rates of nosocomial transmission between testing strategies or when the turnaround time of the test was increased.

Discussion

This study provides insight into the efficacy of testing strategies in a period unbiased by vaccines and variants. The findings are relevant as testing programs for SARS-CoV-2 are scaled back, and possibly if a new vaccine escaping variant emerges.

Combining a parsimonious mathematical model with infection data from tailor-made experiments to understand environmental transmission

Although most infections are transmitted through the environment, the processes underlying the environmental stage of transmission are still poorly understood for most systems. Improved understanding of the environmental transmission dynamics is important for effective non-pharmaceutical intervention strategies. To study the mechanisms underlying environmental transmission we formulated a parsimonious modelling framework including hypothesised mechanisms of pathogen dispersion and decay. To calibrate and validate the model, we conducted a series of experiments studying distance-dependent transmission of Campylobacter jejuni in broilers. We obtained informative simultaneous estimates for all three model parameters: the parameter of C. jejuni inactivation, the diffusion coefficient describing pathogen dispersion, and the transmission rate parameter. The time and distance dependence of transmission in the fitted model is quantitatively consistent with marked spatiotemporal patterns in the experimental observations. These results, for C. jejuni in broilers, show that the application of our modelling framework to suitable transmission data can provide mechanistic insight in environmental pathogen transmission.

Systematic Design of Adenosine Analogs as Inhibitors of a Clostridioides difficile-Specific DNA Adenine Methyltransferase Required for Normal Sporulation and Persistence

Antivirulence agents targeting endospore-transmitted Clostridioides difficile infections are urgently needed. C. difficile-specific DNA adenine methyltransferase (CamA) is required for efficient sporulation and affects persistence in the colon. The active site of CamA is conserved and closely resembles those of hundreds of related S-adenosyl-l-methionine (SAM)-dependent methyltransferases, which makes the design of selective inhibitors more challenging. We explored the solvent-exposed edge of the SAM adenosine moiety and systematically designed 42 analogs of adenosine carrying substituents at the C6-amino group (N6) of adenosine. We compare the inhibitory properties and binding affinity of these diverse compounds and present the crystal structures of CamA in complex with 14 of them in the presence of substrate DNA. The most potent of these inhibitors, compound 39 (IC50 ∼ 0.4 μM and KD ∼ 0.2 μM), is selective for CamA against closely related bacterial and mammalian DNA and RNA adenine methyltransferases, protein lysine and arginine methyltransferases, and human adenosine receptors.

Progress Report: Antimicrobial Drug Discovery in the Resistance Era

Antibiotic resistance continues to be a most serious threat to public health. This situation demands that the scientific community increase their efforts for the discovery of alternative strategies to circumvent the problems associated with conventional small molecule therapeutics. The Global Antimicrobial Resistance and Use Surveillance System (GLASS) Report (published in June 2021) discloses the rapidly increasing number of bacterial infections that are mainly caused by antimicrobial-resistant bacteria. These concerns have initiated various government agencies and other organizations to educate the public regarding the appropriate use of antibiotics. This review discusses a brief highlight on the timeline of antimicrobial drug discovery with a special emphasis on the historical development of antimicrobial resistance. In addition, new antimicrobial targets and approaches, recent developments in drug screening, design, and delivery were covered. This review also discusses the emergence and roles of various antibiotic adjuvants and combination therapies while shedding light on current challenges and future perspectives. Overall, the emergence of resistant microbial strains has challenged drug discovery but their efforts to develop alternative technologies such as nanomaterials seem to be promising for the future.

A review of epidemiological models of Clostridioides difficile transmission and control (2009-2021)

Clostridioides difficile is the leading cause of infectious diarrhea and one of the most common healthcare-acquired infections worldwide. We performed a systematic search and a bibliometric analysis of mathematical and computational models for Clostridioides difficile transmission. We identified 33 publications from 2009 to 2021. Models have underscored the importance of asymptomatic colonized patients in maintaining transmission in health-care settings. Infection control, antimicrobial stewardship, active testing, and vaccination have often been evaluated in models. Despite active testing and vaccination being not currently implemented, they are the most commonly evaluated interventions. Some aspects of C. difficile transmission, such community transmission and interventions in health-care settings other than in acute-care hospitals, remained less evaluated through modeling.

Inactivation of Spores and Vegetative Forms of Clostridioides difficile by Chemical Biocides: Mechanisms of Biocidal Activity, Methods of Evaluation, and Environmental Aspects

Clostridioides difficile infections (CDIs) are the most common cause of acquired diseases in hospitalized patients. Effective surface disinfection, focused on the inactivation of the spores of this pathogen, is a decisive factor in reducing the number of nosocomial cases of CDI infections. An efficient disinfection procedure is the result of both the properties of the biocidal agent used and the technology of its implementation as well as a reliable, experimental methodology for assessing the activity of the biocidal active substance based on laboratory models that adequately represent real clinical conditions. This study reviews the state of knowledge regarding the properties and biochemical basis of the action mechanisms of sporicidal substances, with emphasis on chlorine dioxide (ClO₂). Among the analyzed biocides, in addition to ClO₂, active chlorine, hydrogen peroxide, peracetic acid, and glutaraldehyde were characterized. Due to the relatively high sporicidal effectiveness and effective control of bacterial biofilm, as well as safety in a health and environmental context, the use of ClO₂ is an attractive alternative in the control of nosocomial infections of CD etiology. In terms of the methods of assessing the biocidal effectiveness, suspension and carrier standards are discussed.

Evaluating intervention strategies in controlling coronavirus disease 2019 (COVID-19) spread in care homes: An agent-based model

BACKGROUND

Care homes are vulnerable to widespread transmission of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) with poor outcomes for staff and residents. Infection control interventions in care homes need to not only be effective in containing the spread of coronavirus disease 2019 (COVID-19) but also feasible to implement in this special setting which is both a healthcare institution and a home.

METHODS

We developed an agent-based model that simulates the transmission dynamics of COVID-19 via contacts between individuals, including residents, staff members, and visitors in a care home setting. We explored a representative care home in Scotland in our base case and explore other care home setups in an uncertainty analysis. We evaluated the effectiveness of a range of intervention strategies in controlling the spread of COVID-19.

RESULTS

In the presence of the reference interventions that have been implemented in many care homes, including testing of new admissions, isolation of symptomatic residents, and restricted public visiting, routine testing of staff appears to be the most effective and practical approach. Routine testing of residents is no more effective as a reference strategy while routine testing of both staff and residents only shows a negligible additive effect. Modeling results are very sensitive to transmission probability per contact, but the qualitative finding is robust to varying parameter values in our uncertainty analysis.

CONCLUSIONS

Our model predictions suggest that routine testing should target staff in care homes in conjunction with adherence to strict hand hygiene and using personal protective equipment to reduce risk of transmission per contact.

Modeling transmission of pathogens in healthcare settings

PURPOSE OF REVIEW

Mathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings.

RECENT FINDINGS

The COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts.

SUMMARY

As the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.

Mathematically modeling the effect of touch frequency on the environmental transmission of Clostridioides difficile in healthcare settings

Clostridioides difficile, formerly Clostridium difficile, is the leading cause of infectious diarrhea and one of the most common healthcare acquired infections in United States hospitals. C. difficile persists well in healthcare environments because it forms spores that can survive for long periods of time and can be transmitted to susceptible patients through contact with contaminated hands and fomites, objects or surfaces that can harbor infectious agents. Fomites can be classified as high-touch or low-touch based on the frequency they are contacted. The mathematical model in this study investigates the relative contribution of high-touch and low-touch fomites on new cases of C. difficile colonization among patients of a hospital ward. The dynamics of transmission are described by a system of ordinary differential equations representing four patient population classes and two pathogen environmental reservoirs. Parameters that have a significant effect on incidence, as determined by a global sensitivity analysis, are varied in stochastic simulations of the system to identify feasible strategies to prevent disease transmission. Results indicate that on average, under one-quarter of asymptomatically colonized patients are exposed to C. difficile via low-touch fomites. In comparison, over three-quarters of colonized patients are colonized through high-touch fomites, despite additional cleaning of high-touch fomites. Increased contacts with high-touch fomites increases the contribution of these fomites to the incidence of colonized individuals and decreasing the duration of a hospital visit reduces the amount of pathogen in the environment. Thus, enhanced efficacy of disinfection upon discharge and extra cleaning of high-touch fomites, reduced contact with high-touch fomites, and higher discharge rates, among other control measures, could lead to a decrease in the incidence of colonized individuals.

Cost-Benefit Analysis of Allowing Additional Time in Cleaning Hospital Contact Precautions Rooms

Increasing cleaning time may reduce hospital-acquired transmission of Clostridioides difficile, methicillin-resistant Staphylococcus aureus (MRSA), and vancomycin-resistant enterococcus (VRE). We constructed a cost-benefit model to estimate the impact of implementing an enhanced cleaning protocol, allowing hospital housekeepers an additional 15 minutes to terminally clean contact precautions rooms. The enhanced cleaning protocol saved the hospital $758 per terminally-cleaned room when accounting for only C. difficile. Scaling up to a hospital with 100 cases of C. difficile/year, and the US annual C. difficile incidence, cost savings were $75,832/year and $169.8 million/year, respectively. These results may inform infection control strategic decision-making and resource allocation.