Establishing Statistical Equivalence of Data from Different Sampling Approaches for Assessment of Bacterial Phenotypic Antimicrobial Resistance

Navigating the network: a narrative overview of AMR surveillance and data flow in the United States

The antimicrobial resistance (AMR) surveillance landscape in the United States consists of a data flow that starts in the clinical setting and is maintained by a network of national and state public health laboratories. These organizations are well established, with robust methodologies to test and confirm antimicrobial susceptibility. Still, the bridge that guides the flow of data is often one directional and caught in a constant state of rush hour that can only be refined with improvements to infrastructure and automation in the data flow. Moreover, there is an absence of information in the literature explaining the processes clinical laboratories use to coalesce and share susceptibility test data for AMR surveillance, further complicated by variability in testing procedures. This knowledge gap limits our understanding of what is needed to improve and streamline data sharing from clinical to public health laboratories. Successful models of AMR surveillance display attributes like 2-way communication between clinical and public health laboratories, centralized databases, standardized data, and the use of electronic health records or data systems, highlighting areas of opportunity and improvement. This article explores the roles and processes of the organizations involved in AMR surveillance in the United States and identifies current knowledge gaps and opportunities to improve communication between them through standardization, communication, and modernization of data flow.

Characterization of Antibiotic Resistance in Select Tertiary Hospitals in Uganda: An Evaluation of 2020 to 2023 Routine Surveillance Data

Antimicrobial resistance (AMR) is a public health concern in Uganda. We sought to conduct an extended profiling of AMR burden at selected Ugandan tertiary hospitals. We analyzed routine surveillance data collected between October 2020 and March 2023 from 10 tertiary hospitals. The analysis was stratified according to the hospital unit, age, gender, specimen type, and time. Up to 2754 isolates were recovered, primarily from pus: 1443 (52.4%); urine: 1035 (37.6%); and blood: 245 (8.9%). Most pathogens were Staphylococcus aureus, 1020 (37%), Escherichia coli, 808 (29.3%), and Klebsiella spp., 200 (7.3%). Only 28% of Escherichia coli and 42% of the other Enterobacterales were susceptible to ceftriaxone, while only 44% of Staphylococcus aureus were susceptible to methicillin (56% were MRSA). Enterococcus spp. susceptibility to vancomycin was 72%. The 5-24-year-old had 8% lower ampicillin susceptibility than the >65-year-old, while the 25-44-year-old had 8% lower ciprofloxacin susceptibility than the >65-year-old. The 0-4-year-old had 8% higher ciprofloxacin susceptibility. Only erythromycin susceptibility varied by sex, being higher in males. Escherichia coli ciprofloxacin susceptibility in blood (57%) was higher than in urine (39%) or pus (28%), as was ceftriaxone susceptibility in blood (44%) versus urine (34%) or pus (14%). Klebsiella spp. susceptibility to ciprofloxacin and meropenem decreased by 55% and 47%, respectively, during the evaluation period. During the same period, Escherichia coli ciprofloxacin susceptibility decreased by 40%, while Staphylococcus aureus gentamicin susceptibility decreased by 37%. Resistance was high across the Access and Watch antibiotic categories, varying with time, age, sex, specimen type, and hospital unit. Effective antimicrobial stewardship targeted at the critical AMR drivers is urgently needed.

Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas Aeruginosa from Whole Genome Sequence Data

AIM

Due to the growing availability of genomic datasets, machine learning models have shown impressive diagnostic potential in identifying emerging and reemerging pathogens. This study aims to use machine learning techniques to develop and compare a model for predicting bacterial resistance to a panel of 12 classes of antibiotics using whole genome sequence (WGS) data of Pseudomonas aeruginosa.

METHOD

A machine learning technique called Random Forest (RF) and BioWeka was used for classification accuracy assessment and logistic regression (LR) for statistical analysis.

RESULTS

Our results show 44.66% of isolates were resistant to twelve antimicrobial agents and 55.33% were sensitive. The mean classification accuracy was obtained ≥98% for BioWeka and ≥96 for RF on these families of antimicrobials. Where ampicillin was 99.31% and 94.00%, amoxicillin was 99.02% and 95.21%, meropenem was 98.27% and 96.63%, cefepime was 99.73% and 98.34%, fosfomycin was 96.44% and 99.23%, ceftazidime was 98.63% and 94.31%, chloramphenicol was 98.71% and 96.00%, erythromycin was 95.76% and 97.63%, tetracycline was 99.27% and 98.25%, gentamycin was 98.00% and 97.30%, butirosin was 99.57% and 98.03%, and ciprofloxacin was 96.17% and 98.97% with 10-fold-cross validation. In addition, out of twelve, eight drugs have found no false-positive and false-negative bacterial strains.

CONCLUSION

The ability to accurately detect antibiotic resistance could help clinicians make educated decisions about empiric therapy based on the local antibiotic resistance pattern. Moreover, infection prevention may have major consequences if such prescribing practices become widespread for human health.

Antimicrobial resistance in commensal opportunistic pathogens isolated from non-sterile sites can be an effective proxy for surveillance in bloodstream infections

Antimicrobial resistance (AMR) surveillance in bloodstream infections (BSIs) is challenging in low/middle-income countries (LMICs) given limited laboratory capacity. Other specimens are easier to collect and process and are more likely to be culture-positive. In 8102 E. coli BSIs, 322,087 E. coli urinary tract infections, 6952 S. aureus BSIs and 112,074 S. aureus non-sterile site cultures from Oxfordshire (1998-2018), and other (55,296 isolates) rarer commensal opportunistic pathogens, antibiotic resistance trends over time in blood were strongly associated with those in other specimens (maximum cross-correlation per drug 0.51-0.99). Resistance prevalence was congruent across drug-years for each species (276/312 (88%) species-drug-years with prevalence within ± 10% between blood/other isolates). Results were similar across multiple countries in high/middle/low income-settings in the independent ATLAS dataset (103,559 isolates, 2004-2017) and three further LMIC hospitals/programmes (6154 isolates, 2008-2019). AMR in commensal opportunistic pathogens cultured from BSIs is strongly associated with AMR in commensal opportunistic pathogens cultured from non-sterile sites over calendar time, suggesting the latter could be used as an effective proxy for AMR surveillance in BSIs.

Development and selection of low-level multi-drug resistance over an extended range of sub-inhibitory ciprofloxacin concentrations in Escherichia coli

To better combat bacterial antibiotic resistance, a growing global health threat, it is imperative to understand its drivers and underlying biological mechanisms. One potential driver of antibiotic resistance is exposure to sub-inhibitory concentrations of antibiotics. This occurs in both the environment and clinic, from agricultural contamination to incorrect dosing and usage of poor-quality medicines. To better understand this driver, we tested the effect of a broad range of ciprofloxacin concentrations on antibiotic resistance development in Escherichia coli. We observed the emergence of stable, low-level multi-drug resistance that was both time and concentration dependent. Furthermore, we identified a spectrum of single mutations in strains with resistant phenotypes, both previously described and novel. Low-level class-wide resistance, which often goes undetected in the clinic, may allow for bacterial survival and establishment of a reservoir for outbreaks of high-level antibiotic resistant infections.

Making Sense of Pharmacovigilance and Drug Adverse Event Reporting: Comparative Similarity Association Analysis Using AI Machine Learning Algorithms in Dogs and Cats

Drug-associated adverse events cause approximately 30 billion dollars a year of added health care expense, along with negative health outcomes including patient death. This constitutes a major public health concern. The US Food and Drug Administration (FDA) requires drug labeling to include potential adverse effects for each newly developed drug product. With the advancement in incidence of adverse drug events (ADEs) and potential adverse drug events, published studies have mainly concluded potential ADEs from labeling documents obtained from the FDA's preapproval clinical trials, and very few analyzed their research work based on reported ADEs after widespread use of a drug to animal subjects. The aforesaid procedure of deriving practice based on information from preapproval labeling may misrepresent or deprecate the incidence and prevalence of specific ADEs. In this study, we make the most of the recently disseminated ADE data by the FDA for animal drugs and devices used in animals to address this public and welfare concern. For this purpose, we implemented 5 different methods (Pearson distance, Spearman distance, cosine distance, Yule distance, and Euclidean distance) to determine the most efficient and robust approach to properly discover highly associated ADEs from the reported data and accurately exclude noise-induced reported events, while maintaining a high level of correlation precision. Our comparative analysis of ADEs based on an artificial intelligence (AI) approach for the 5 robust similarity methods revealed high ADE associations for 2 drugs used in dogs and cats. In addition, the described distance methods systematically analyzed and compared ADEs from the drug labeling sections with a specific emphasis on analyzing serious ADEs. Our finding showed that the cosine method significantly outperformed all the other methods by correctly detecting and validating ADEs based on the comparative similarity association analysis compared with ADEs reported by preapproval clinical trials, premarket testing, or postapproval complication experience of FDA-approved animal drugs.