Assessing antibiotic therapy effectiveness against the major bacterial pathogens in a hospital using an integrated index

Assessing the Effectiveness of Antibiotic Therapy Against Common Gram-Negative Bacteria in a Saudi Arabian Hospital Using the Drug Resistance Index

Introduction: Assessing the effectiveness of antibiotics and communicating the problem of resistance are essential when devising antimicrobial stewardship programs (ASPs) in hospital settings. The Drug Resistance Index (DRI) is a useful tool that combines antibiotic consumption and bacterial resistance into a single measure. In this study, we used the DRI to assess the impact of introducing a new antibiotic restriction form on antibiotic effectiveness for the treatment of Gram-negative infections in the intensive care unit (ICU).

Methods: This was an observational study to assess and evaluate the antibiotic susceptibility of Gram-negative bacteria and antibiotic prescribing rates for the antibiotics indicated for Gram-negative bacteria following the introduction of a new antibiotic restriction form. The study was conducted from 2015 to 2017 at King Abdulaziz Medical City, a tertiary care facility in Jeddah, Saudi Arabia. Changes in antibiotic effectiveness before and after the introduction of the form were evaluated by calculating the DRI for four of the most common Gram-negative pathogens and eight commonly used antibiotic classes.

Results: The overall DRI for the adult ICU was higher (59.45) in comparison to the hospital-wide DRI (47.96). A higher DRI was evident for carbapenems and antipseudomonal penicillins + beta-lactamase inhibitors. Acinetobacter baumannii had the highest DRI followed by Klebsiella pneumoniae in both the adult ICU and hospital-wide. After the implementation of antibiotic restriction in the adult ICU, the DRI for carbapenems was significantly lower in the post-intervention phase (from 31.61 to 26.05) (p = 0.031).

Conclusion: The DRI is a useful tool for tracking the effectiveness of antibiotics over time. The results of our study are significant in the way that it highlights the importance of having an effective antibiotic stewardship program in healthcare settings and regular feedback of antibiotic consumption data to the stakeholders to keep the antibiotic prescriptions in check, thereby ensuring their sustained effectiveness.

A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria

Antimicrobials have paved the way for medical and social development over the last century and are indispensable for treating infections in humans and animals. The dramatic spread and diversity of antibiotic-resistant pathogens have significantly reduced the efficacy of essentially all antibiotic classes and is a global problem affecting human and animal health. Antimicrobial resistance is influenced by complex factors such as resistance genes and dosing, which are highly nonlinear, time-lagged and multivariate coupled, and the amount of resistance data is large and redundant, making it difficult to predict and analyze. Based on machine learning methods and data mining techniques, this paper reviews (1) antimicrobial resistance data storage and analysis techniques, (2) antimicrobial resistance assessment methods and the associated risk assessment methods for antimicrobial resistance, and (3) antimicrobial resistance prediction methods. Finally, the current research results on antimicrobial resistance and the development trend are summarized to provide a systematic and comprehensive reference for the research on antimicrobial resistance.

Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived Pathogens

(1) Background: The high use of antibiotics has made the issue of antimicrobial resistance (AMR) increasingly serious, which poses a substantial threat to the health of animals and humans. However, there remains a certain gap in the AMR system and risk assessment models between China and the advanced world level. Therefore, this paper aims to provide advanced means for the monitoring of antibiotic use and AMR data, and take piglets as an example to evaluate the risk and highlight the seriousness of AMR in China. (2) Methods: Based on the principal component analysis method, a drug resistance index model of anti-E. coli drugs was established to evaluate the antibiotic risk status in China. Additionally, based on the second-order Monte Carlo methods, a disease risk assessment model for piglets was established to predict the probability of E. coli disease within 30 days of taking florfenicol. Finally, a browser/server architecture-based visualization database system for animal-derived pathogens was developed. (3) Results: The risk of E. coli in the main area was assessed and Hohhot was the highest risk area in China. Compared with the true disease risk probability of 4.1%, the result of the disease risk assessment model is 7.174%, and the absolute error was 3.074%. Conclusions: Taking E. coli as an example, this paper provides an innovative method for rapid and accurate risk assessment of drug resistance. Additionally, the established system and assessment models have potential value for the monitoring and evaluating AMR, highlight the seriousness of antimicrobial resistance, advocate the prudent use of antibiotics, and ensure the safety of animal-derived foods and human health.

A Real-world Evaluation of a Case-based Reasoning Algorithm to Support Antimicrobial Prescribing Decisions in Acute Care

Background

A locally developed case-based reasoning (CBR) algorithm, designed to augment antimicrobial prescribing in secondary care was evaluated.

Methods

Prescribing recommendations made by a CBR algorithm were compared to decisions made by physicians in clinical practice. Comparisons were examined in 2 patient populations: first, in patients with confirmed Escherichia coli blood stream infections (“E. coli patients”), and second in ward-based patients presenting with a range of potential infections (“ward patients”). Prescribing recommendations were compared against the Antimicrobial Spectrum Index (ASI) and the World Health Organization Essential Medicine List Access, Watch, Reserve (AWaRe) classification system. Appropriateness of a prescription was defined as the spectrum of the prescription covering the known or most-likely organism antimicrobial sensitivity profile.

Results

In total, 224 patients (145 E. coli patients and 79 ward patients) were included. Mean (standard deviation) age was 66 (18) years with 108/224 (48%) female sex. The CBR recommendations were appropriate in 202/224 (90%) compared to 186/224 (83%) in practice (odds ratio [OR]: 1.24 95% confidence interval [CI]: .392–3.936; P = .71). CBR recommendations had a smaller ASI compared to practice with a median (range) of 6 (0–13) compared to 8 (0–12) (P < .01). CBR recommendations were more likely to be classified as Access class antimicrobials compared to physicians’ prescriptions at 110/224 (49%) vs. 79/224 (35%) (OR: 1.77; 95% CI: 1.212–2.588; P < .01). Results were similar for E. coli and ward patients on subgroup analysis.

Conclusions

A CBR-driven decision support system provided appropriate recommendations within a narrower spectrum compared to current clinical practice. Future work must investigate the impact of this intervention on prescribing behaviors more broadly and patient outcomes.

Tracking global trends in the effectiveness of antibiotic therapy using the Drug Resistance Index

Background

Evaluating trends in antibiotic resistance and communicating the results to a broad audience are important for dealing with this global threat. The Drug Resistance Index (DRI), which combines use and resistance into a single measure, was developed as an easy-to-understand measure of the effectiveness of antibiotic therapy. We demonstrate its utility in communicating differences in the effectiveness of antibiotic therapy across countries.

Methods

We calculated the DRI for countries with data on antibiotic use and resistance for the disease-causing organisms considered by the WHO as priority pathogens: Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecium and Enterococcus faecalis. Additionally, we estimated pooled worldwide resistance rates for these pathogens.

Results

41 countries had the requisite data and were included in the study. Resistance and use rates were highly variable across countries, but A. baumannii resistance rates were uniformly higher, on average, than other organisms. High-income countries, particularly Sweden, Canada, Norway, Finland and Denmark, had the lowest DRIs; the countries with the highest DRIs, and therefore the lowest effectiveness of antibiotic therapy, were all low-income and middle-income countries.

Conclusions

The DRI is a useful indicator of the problem of resistance. By combining data on antibiotic use with resistance, it captures a snapshot of how the antibiotics a country typically uses match their resistance profiles. This single measure of the effectiveness of antibiotic therapy provides a means of benchmarking against other countries and can, over time, indicate changes in drug effectiveness that can be easily communicated.