JAC-Antimicrobial Resistance

BackgroundThe overuse and misuse of antimicrobials contribute significantly to antimicrobial resistance (AMR), which is a global public health concern. India has particularly high rates of antimicrobial resistance, posing a threat to effective treatment. The WHO AWaRe classification system was introduced to address this issue and guide appropriate antibiotic prescribing. However, there is a lack of studies examining the prescribing patterns of antimicrobials using the AWaRe classification, especially in North India. Therefore, this study aimed to assess the prescribing patterns of antimicrobials using the WHO AWaRe classification in a tertiary care centre in North India. AimTo study the prescribing patterns of antimicrobials using WHO AWaRe classification through a cross-sectional study in AIIMS Rishikesh. MethodsA descriptive, cross-sectional study was conducted from July 2022 to August 2022 at a tertiary care hospital. Prescriptions containing at least one antimicrobial were included in the study. Data on prescriptions, including patient demographics, departments, types of antimicrobials prescribed, and duration of treatment, were collected. A questionnaire-based survey was also conducted to assess the knowledge and practices of prescribing doctors regarding the utility of AWaRe classification. ResultsA total of 123 patients were included in the study, with antibiotic prescriptions being written for all of them. Most prescriptions were for inpatients, evenly distributed between Medicine and Surgical departments. Metronidazole and Ceftriaxone were the most prescribed antibiotics. According to the AWaRe classification, 57.61% of antibiotics fell under the Access category, 38.27% in Watch, and 4.11% in Reserve. The majority of Access antibiotics were prescribed in the Medicine department, while Watch antibiotics were more common in the Medicine department as well. The questionnaire survey showed that only a third of participants were aware of the AWaRe classification, and there was a lack of knowledge regarding antimicrobial resistance and the potential impact of AWaRe usage. ConclusionThis study highlights the need for better antimicrobial prescribing practices and increased awareness of the WHO AWaRe classification and antimicrobial resistance (AMR) among healthcare professionals. The findings indicate a high proportion of prescriptions falling under the Access category, suggesting appropriate antibiotic selection. However, there is a significant difference between the WHO DDD and the prescribed daily dose in the analysed prescriptions suggesting overuse and underuse of antibiotics. There is room for improvement and educational interventions and antimicrobial stewardship programs should be implemented to enhance knowledge and adherence to guidelines, ultimately contributing to the containment of antimicrobial resistance.

Antimicrobial resistance (AMR) threatens global health. Understanding resistance patterns aids in treatment and promotes responsible antimicrobial use. This review and meta-analysis assessed the prevalence of antimicrobial resistance among clinically relevant pathogens in Tanzania. A total of 18,265 studies identified from Google Scholar (18,000), PubMed (13), and Science Direct (252) underwent screening and full article review. Finally, 28 studies were included. A subgroup analysis was performed to evaluate the resistance patterns within antibiotic classes for specific pathogens. Descriptive statistics were used to describe the characteristics of the studies and the prevalence of antibiotic resistance. Heterogeneity was assessed using forest plots and the I{superscript 2} statistic. Among the included studies, most isolates (25.0%) were obtained from urine samples. Of these studies, 75% were cross-sectional studies and 92.9% were conducted in hospital settings. The analysis revealed high resistance to penicillin, particularly amoxicillin-clavulanic and ampicillin, with Klebsiella pneumoniae (0.96 [0.83-0.99]), Acinetobacter baumannii (0.94 [0.67-0.99]) and Escherichia coli (0.90 [0.81-0.95]). Similarly, erythromycin resistance was most prevalent in Campylobacter spp. (0.85 [0.80-0.89]). Ciprofloxacin resistance was highest in Acinetobacter baumannii (0.54 [0.33-0.73]), whereas amikacin resistance was highest in Proteus spp. (0.86 [0.35-0.99]). Ceftriaxone resistance was particularly high in Acinetobacter baumannii (0.91 [0.70-0.98]) and Pseudomonas aeruginosa (0.85 [0.74-0.92]). Resistance to meropenem was lowest among Escherichia coli (0.04 [0.01-0.10]) and Klebsiella spp. (0.07 [0.03-0.15]), with an overall pooled resistance to the ESKAPE-E pathogen of (0.11[0.06-0.19]). Imipenem and clindamycin each had an overall pooled resistance of (0.06[0.02-0.14]) against both Escherichia coli and Klebsiella pneumoniae. The findings highlight widespread resistance among key bacterial pathogens, ESKAPE-E, particularly in the Access and Watch groups of antibiotics. The variability in resistance patterns underscores the need to re-evaluate empirical treatment protocols (STG/NEMLIT) to ensure effective treatment regimens, strengthen antimicrobial stewardship, enhance surveillance systems, and promote rational antibiotic use.

BackgroundSepsis is a critical medical condition characterized by life-threatening organ dysfunction triggered by a dysregulated response to infection. It poses a substantial global health burden, with significant morbidity, mortality, and economic costs, particularly pronounced in low- and middle-income countries. Effective management of sepsis relies on early recognition and appropriate intervention, underscoring the importance of accurate classification to guide treatment decisions. ObjectiveThis longitudinal observational study aimed to assess the distribution of sepsis categories and the use of empirical antibiotics classified by the WHO AWaRe system in a tertiary care hospital in Northern India. The study also aimed to highlight implications for antimicrobial stewardship by examining the use of AWaRe group antibiotics and their correlation with sepsis classifications. MethodsA total of 1867 patients admitted with suspected sepsis were screened, with 230 meeting inclusion criteria. Patients were categorized into different sepsis classes (Asepsis, Possible Sepsis, Probable Sepsis, Confirm Sepsis) and followed until discharge or Day-28. Descriptive statistical analysis was employed to assess sepsis categories and empirical antibiotic usage classified by Access, Watch, and Reserve categories according to the WHO AWaRe system. ResultsAmong the study cohort (mean age 40.70 {+/-} 14.49 years, 50.9% female), initial sepsis classification predominantly included Probable Sepsis (51.3%) and Possible Sepsis (35.7%), evolving to Asepsis (57.8%) upon final classification. Empirical antibiotic use showed a concerning predominance of Watch group antibiotics (92.5%), with Ceftriaxone (45.7%) and piperacillin-tazobactam (31.7%) being the most commonly prescribed. ConclusionThe dynamic nature of sepsis classification underscores the complexity of diagnosing and managing this condition. Accurate categorization is pivotal for clinical decision-making, optimizing antibiotic use, and combating antimicrobial resistance. The majority of the asepsis category was levelled as probable or possible sepsis and given antibiotics. The high reliance on Watch group antibiotics in empirical therapy signals a need for enhanced diagnostic strategies to refine treatment initiation, potentially reducing unnecessary antibiotic exposure. Future efforts should focus on establishing sepsis classification checklists and promoting adherence to antimicrobial stewardship principles to mitigate the global threat of antimicrobial resistance.

BackgroundAntimicrobial resistance is a growing global public health concern, and multidrug-resistant tuberculosis is responsible for roughly one-quarter of all antimicrobial-resistant infection-related deaths worldwide. GeneXpert is a rapid, automated molecular test that detects multi-drug-resistant tuberculosis using rifampicin as a predictor. It was recommended by the World Health Organization (WHO) in 2010 for national tuberculosis programs in developing countries; however, it has limitations. Indeterminate results for Mycobacterium tuberculosis indicate that the test was unable to determine whether the bacteria were resistant to rifampicin. This study used Shewhart Control Chart, which has action limits, to investigate the causes of indeterminate results. MethodsThe control limits on the Shewhart chart are central, upper, and lower. GeneXpert indeterminate results obtained between January 2017 and December 2020 in a tertiary hospital in a low and middle-income country were plotted. Points above the upper control limit were used to determine whether or not the process was under control. ResultThe proportion of GeneXpert results that were indeterminate varied, with 58% exceeding the upper limit. Only 42% were within the control limit, in comparison. The majority of the laboratory results revealed an out-of-control signal by displaying points outside the control limits or non-random patterns of points known as special-cause variation, according to this study. ConclusionsGeneXpert indeterminate results have an impact on patient management by preventing accurate diagnosis and delaying the start of anti-tuberculosis medication. Machine malfunctions, insufficient bacterial load, poor quality samples, operator errors, or faulty laboratory materials could all be to blame. Regular equipment checks by laboratory personnel, program sponsors, or leadership will be extremely beneficial in achieving the desired results and initiating appropriate treatment. Statistical process control is widely used in hospital performance monitoring and improvement, and it is becoming more popular in public health surveillance.

BackgroundCarbapenems are drugs of last resort and resistance to them is considered a great public health threat, especially in notorious nosocomial pathogens like Acinetobacter baumannii and Pseudomonas aeruginosa. In this study, we aimed to determine the prevalence of carbapenem resistance in A. baumannii and P. aeruginosa infections in Sub-Saharan Africa. MethodsDatabases (PubMed, Scopus, Web of Science, and African Journal Online) were systematically searched following the Preferred Reporting Items for Systematic review and meta-analysis protocols (PRISMA-P) 2020 statements for articles reporting carbapenem-resistant Acinetobacter baumannii (CRAB) and carbapenem-resistant Pseudomonas aeruginosa (CRPA) prevalence between 2012 and 2022. Pooled prevalence was determined with the random effect model in R. ResultsA total of 47 articles were scanned for eligibility, among which 25 (14 for carbapenem-resistant A. baumanii and 11 for carbapenem-resistant P. aeruginosa) were included in the study after fulfilling the eligibility criteria. The pooled prevalence of CRPA in the present study was estimated at 8% (95% CI; 0.02 - 0.17; I2=98%; P <0.01). There was high heterogeneity (Q=591.71, I2=98.9%; P<0.0001). The pooled prevalence of CRAB in the present study was estimated at 20% (95% CI; 0.04 - 0.43; I2=99%; P <0.01). There was high heterogeneity (Q=1452.57, I2=99%; P<0.0001). Carbapenem-resistant A. baumannii prevalence based on sample source gave estimates of 24% (95% CI; 6 - 49; I2=99%; P<0.01). The carbapenamse genes commonly isolated from A. baumanii in this study include blaOXA23, blaOXA48, blaGES., blaNDM, blaVIM,, blaOXA24, blaOXA58, blaOXA51, blaSIM-1, blaOXA40, blaOXA66, blaOXA69, blaOXA91, with blaOXA23 and blaVIM being the most common. On the other hand, blaNDM, blaVIM, blaIMP,, blaOXA48, blaOXA51, blaSIM-1, blaOXA181, blaKPC, blaOXA23, blaOXA50 were the commonly isolated carbapenemase genes in P. aeruginosa, among which blaVIM and blaNDM genes were the most frequently isolated. ConclusionSurveillance of drug-resistant pathogens in sub-Saharan Africa is essential in reducing the disease burden in the region and this study has shown that the region has significantly high multi-drug resistant pathogen prevalence. This is a wake-up call for policymakers to put in place measures to reduce the spread of these critical priority pathogens.

BackgroundThe never-ending emergence of superbugs casts a shadow over the victorious age of antibiotics. In fact, the triumph of antibiotics was previously viewed in retrospection as our final victory over bacteria. Bacteria like Klebsiella pneumoniae, Acinetobacter baumannii, and Escherichia coli are now raising an alarming number of infections across hospitals and communities around the globe. The objective was to evaluate the implications for antimicrobial stewardship based on identifying the antibiotic resistance profiles, genotype mechanisms, and trends in common pathogenic bacteria found in various hospitals across Iraq. MethodsWe used a two-fold approach that was comprehensive in scope and involved both efficient multicenter surveillance as well as cutting edge genetic analysis to unravel the complex topography of antibiotic resistance. We provided a geographically heterogeneous but diverse set of clinically obtained isolates to participate in hospitals for a period of 24 months and concentrated our efforts on prioritized pathogens K. pneumoniae, A. baumannii, E. coli, P. aeruginosa, and S. aureus that are well known to pose serious threats. Beginning with clinically obtained isolates sourced across the entire globe, we used standardized techniques such as broth microdilution to first undertake phenotyping in a central reference lab to establish microbial identity based on resistance phenotypes to a set of prioritized antibiotics that include carbapenems, third generation cephalosporins, or fluoroquinolones. Finally, we derived data concerning the emergence patterns and geographic distribution of resistant microbes such as MRSA or CRE. We used genome-wide sequencing to unlock information concerning the genetic blueprints for a set of specifically chosen isolates based on their representational diversity across geographic locales, resistance phenotypes, and specific times. ResultsThe sample was made up of Escherichia coli (n = 225), Klebsiella pneumoniae (n = 185), Staphylococcus aureus (n = 135), Pseudomonas aeruginosa (n= 90), and Acinetobacter baumannii (n = 125). Ceftriaxone resistance was found in 80.4% of E. Coli, ciprofloxacin resistance in 45.6%, and meropenem resistance in 15.1%. K. pneumoniae exhibited 38.9% resistance to aminoglycosides and 70.2% resistance to carbapenems. The percentage of MRSA in S. aureus was 55.5%. P. aeruginosa showed 22.2% resistance to colistin, 37.8% resistance to piperacillin tazobactam, and 50.0% resistance to ceftazidime. Imipenem resistance was found in 85.6% of A. baumannii isolates, whereas colistin resistance was found in 28.8% of isolates. In all, 3.4% of isolates are pan-drug-resistant (PDR), 14.6% are extensively drug-resistant (XDR), and 52.1% are multidrug-resistant (MDR). WGS identified common genes such bla_NDM-1, bla_OXA-48, mcr-1, aac (6)-Ib, and plasmid replicons IncF, IncL/M, and IncI2. Carbapenem resistance in Gram-negative bacteria rose by around 18% over the course of five years. ConclusionsThis study shows that the rapid spread of complex genetic information in bacteria causes antibiotic resistance problems. High-level resistance represents an expected consequence of the spread of resistance genes and successful bacteria within healthcare systems. We demonstrate in our results that our expertise in overcoming resistance at a molecular level will play a crucial role in combating infectious diseases in the coming years.

SynopsisO_ST_ABSBackgroundC_ST_ABSThe overwhelming, irrational behaviour of using antimicrobial (AM) has added to the amplification and spread of antimicrobial resistance (AMR) burden. Healthcare professionals can curtail the AMR by practicing antimicrobial stewardship (AMS). Keeping this in view WHO has laid down a global action plan to combat AMR including free online availability of openWHO course. So, our study aimed at accessing the knowledge, attitude, and practice (KAP) of practicing doctors towards AMS based on this course in a tertiary care hospital. MethodsThe study was conducted among practitioners (faculty, senior residents, junior residents) in different clinical departments. The study was designed as a KAP survey. A validated self-administered questionnaire consisting of 29 questions was designed and shared among 200 participants through the mail and physically. Apart from observing knowledge/attitude/practice gaps, the difference in response to questions was evaluated among various groups (surgeon vs physician, faculty vs senior resident vs junior resident, openWHO course participant vs openWHO course aware non-participant vs openWHO course unaware non-participants. ResultsResponse rate was 62.5% (n=200). Knowledge on AMS was observed among doctors with >50% near correct responses in each question except for the question asking on IV route of AM administration. A significant knowledge gap was found when a comparison was made between faculty members, senior residents, and junior residents (p <0.001) in the spectrum of activity of AM. Almost all the participants agreed that ASP is a necessity in the hospital and believed that ASP reduces healthcare costs and adverse effects of inappropriate AM prescription. A significant difference between the various groups aspects was also observed. ConclusionKnowledge gap on ASP is observed among all HCPs but significant differences among faculty, senior residents, and junior residents, among openWHO course vs unaware openWHO course participant. This shows faculty has to take the lead including more and more practice and education in ASP. The openWHO course may help in achieving this.

BackgroundThe increasing prevalence of meropenem-resistant gram-negative bacteria has significantly undermined its effectiveness and has increased treatment failure and mortality rates. The global availability of bacterial WGS data with antimicrobial resistance phenotypes enables large-scale genome analysis to explore resistance determinants. This study investigated the meropenem resistance mechanism in multidrug-resistant (MDR) Klebsiella pneumoniae (KP) and Acinetobacter baumannii (AB) isolates using advanced data analytics approaches. MethodsWe analysed 2,411 KP and 375 AB isolates with meropenem-resistant and susceptible phenotypes from the BV-BRC database. AMR genes and mutations were identified from the isolates using the CARD database as a reference. Significant AMR genes and missense mutations, determined through chi-square tests, were subsequently used to train ML and DL models. The best-performing SVM model was used for sequential feature selection to identify key features. Additionally, association mining was conducted separately on the selected features and the antibiotics data. ResultsNotable differences were observed in the proportions of genes contributing to the meropenem resistance mechanism categories between KP and AB, including carbapenemases (4% in KP, 23% in AB), antibiotic efflux (30%, 60%), target alteration (23%, 12%), and reduced permeability (18%, 3%). Mutation frequencies also vary, with antibiotic efflux (26%, 67%), target alteration (64%, 5%), and reduced permeability (7%, 15%). A total of 410 significant features in KP and 211 in AB were identified for model building. SVM-based feature selection pinpointed seven key features in KP and 10 in AB, resulting in 95% accuracy for both. Association mining revealed blaKPC-2, blaKPC-3, bleMBL, and aac(6)-Ib9 as key factors in KP, and blaOXA-23, Abau_gyrA_FLO|Ser81Leu, and Abau_OprD_IMP|Asn411Asp in AB associated with meropenem resistance. The observed prevalence of AAC genes and the gyrA mutation, along with insights from association mining, reveals the co-resistance of meropenem with aminoglycosides and fluoroquinolones, while oprD mutations imply potential shared resistance across antibiotics. ConclusionThe analysis of AMR genes and mutations based on resistance mechanisms revealed distinct differences in meropenem resistance between KP and AB. The ML/DL models and association mining approaches identified key resistance features and cross-antibiotic resistance insights. These findings deepen our understanding of meropenem resistance, enabling more precise and effective antimicrobial interventions.

BackgroundWhile associations of antibiotic resistance traits are not random in multidrug-resistant (MDR) bacteria, clinically relevant resistance patterns remain relatively underexplored. This study used machine learning, specifically association-set mining, to explore resistance associations within E. coli isolates from community-acquired urinary tract infections (UTIs). MethodsWe analysed antibiograms of community-acquired E. coli UTI isolates collected from 2018 to 2022 by Frances national surveillance system. Association-set mining was applied separately to extended-spectrum beta-lactamase-producing E. coli (ESBL-EC) and non-ESBL-EC. MDR patterns that had expected support (reflecting pattern frequency) and conditional lift (reflecting association strength) higher than expected by chance (p-value[&le;]0.05) were used to construct resistance networks, and analysed according to time, age and gender. FindingsThe number of isolates increased from 360 287 in 2018 (10 150 ESBL-EC, 350 137 non-ESBL-EC) to 629 017 in 2022 (18 663 ESBL-EC, 610 354 non-ESBL-EC). More MDR patterns were selected in ESBL-EC than non-ESBL-EC (2022: 1770 vs 93 patterns), with higher respective network densities (2022: 0.230 vs 0.074). Fluoroquinolone, third-generation cephalosporin and penicillin resistances were strongly associated in ESBL-EC. The median densities of resistance association networks increased from 2018 to 2022 in both ESBL-EC (0.238 to 0.302, p-value=0.06, Pearson test) and non-ESBL-EC (0.074 to 0.100, p-value=0.04). Across all years, median network densities were higher in men than women in both ESBL-EC (2022: 0.305 vs 0.276) and non-ESBL-EC (2022: 0.128 vs 0.094); they were also higher in individuals over 65 years old than under 65 in ESBL-EC (2022: 0.289 vs 0.275) and non-ESBL-EC (2022: 0.103 vs 0.088). InterpretationThese findings, which show increasing MDR associations, especially in men and older individuals, highlight the importance of ongoing resistance surveillance to understand the future evolution of resistance patterns. FundingThis work received funding from the French government through the National Research Agency project COMBINE ANR-22-PAMR-0003. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched Pubmed for previously published articles without any date or language restrictions using the search terms (multiresistan* OR "multidrug-resistan*") AND ("data mining" OR "machine learning" OR "artificial intelligence") AND (pattern* OR associat*). We found three studies that used machine learning to identify multiresistance patterns in various pathogens (chicken-associated Escherichia coli, human-associated Staphylococcus aureus and cattle-associated Salmonella enterica) in the United States. However, to our knowledge, no machine-learning studies to date have explored multiresistance patterns in human-associated Enterobacterales, especially within European contexts. Added value of this studyOur study provided a novel and detailed analysis of multiresistance patterns in community-acquired E. coli urinary tract infection collected from a French national surveillance system. Our findings confirmed that association-set mining is effective for identifying resistance associations in antibiotic resistance surveillance data. We explored the temporal evolution of resistance associations, gender-specific and age-specific differences, which to our knowledge, had not been previously analysed. Implications of all the available evidenceOur results suggest a temporal increase of resistance associations in community-acquired E. coli UTI and identify key patterns in different subpopulations. In the context of rising antibiotic resistance, optimizing the use of current medications is crucial, as few new antibiotics have been developed in the past two decades. With further research, this work could provide insight for targeted antibiotic stewardship strategies.

BackgroundElectronic prescribing registries are widely used for antimicrobial stewardship surveillance. Existing indicators predominantly measure structure or process, while validated outcome indicators remain rare. The present study evaluates how well rule-based measures capture clinically meaningful postdiagnostic antibiotic decision making in pediatric febrile urinary tract infection. MethodsWe conducted a retrospective, multicenter validation study including all empirically treated febrile UTI episodes across three Swedish pediatric emergency departments. Prescribing outcomes were classified using registry rules and compared with outcomes determined by clinician review and laboratory findings. Guidance Ratio (GR) and Discontinuation Ratio (DR) were calculated monthly and in aggregate for both clinically validated- and registry rule classifications. ResultsIn total, 909 febrile UTI episodes were included across all sites. The rule-based GR was 49%. GR increased consistently with stronger diagnostic evidence. Among the 431 episodes with clinician-adjudicated follow-up, 63% resulted in guided treatment; 28% discontinued treatment, and 9% lacked follow-up documentation. The rule-based algorithm showed a sensitivity of 0.78 and a specificity of 1.00 for identifying guided outcomes. Monthly rule-based GR tracked validated temporal patterns but underestimated absolute values. A calibration function substantially improved agreement. ConclusionsRule-based indicators captured overall prescribing patterns but underestimated the level of prescribing concordant with guidelines. Validation against clinician reviewed reference data enabled calibration and improved the interpretability of indicators based on registry data for antimicrobial stewardship.

BackgroundThe World Health Organization (WHO) (AWaRe (Access/Watch/Reserve) book gives detailed guidance on the optimal use of antibiotics across primary care and hospitals for adults and children with the aim of improving the quality of use. ObjectivesTo develop universally applicable, model sets of appropriate and feasible quality indicators based on the WHO AWaRe system for primary care, hospital, and general indicators for optimal antibiotic use. MethodsIndicators from a scoping review were revised to focus on clinical infections in the AWaRe book. They were assessed using consensus techniques through two rounds each of the Global Delphi Technique and RAND/UCLA Appropriateness Method, evaluating appropriateness and feasibility at national and global levels respectively. In Round 1 of each method, panellists rated clarity and suggested revisions or new indicator. Round 2 results are reported. FindingsThere were 102 quality indicators (Primary Care: 46; Hospital: 39; General: 17) included in Round 2 of the Delphi Technique and 136 indicators (Primary Care: 56; Hospital: 60; General: 20) in Round 2 of the RAND/UCLA method, which are presented as model sets of indicators. From these broad sets, 12 indicators from the Delphi Technique and 31 indicators from the RAND/UCLA method were rated both appropriate and feasible with agreement respectively. ConclusionThese model AWaRe-based, universally applicable quality indicators can be locally adapted to improve the optimal use of antibiotics and inform global and country specific antimicrobial stewardship programs (AMS).

BackgroundThe paucity of data on the epidemiology of bloodstream infection (BSI) in low and middle-income countries (LMICs) limits its effective prevention and management. This review sought to determine the prevalence, bacteriological and antimicrobial resistance profiles of bacteria implicated in BSI in Cameroon. MethodsPubMed and Google Scholar databases were searched to identify relevant articles, which were screened according to the PRISMA guidelines. The data were analysed using comprehensive meta-analysis software. The I2 was used to evaluate heterogeneity between studies, Beggs and Eggers regression tests were used to evaluate publication bias, and random effects analysis was used to calculate the pooled prevalence. ResultsA total of 4223 blood cultures were obtained from the 10 included studies. The overall pooled prevalence of bacterial BSI was 26.31% (95% CI= 17.01%-38.35%). Escherichia coli (23.09%; 95% CI= 9.21%-47.05%), Klebsiella spp. (22.95%; 95% CI= 13.09%-37.07%), and Staphylococcus aureus (16.09%; 95% CI= 8.11%-29.43%) were the most common bacteria species. E. coli and Klebsiella spp. displayed the highest resistance to amoxicillin (82.65%; 95% CI= 63.25%-92.95% vs 86.42%; 95% CI= 55.90%-96.97%), amoxicillin + clavulanic acid (71.74%; 43.96-89.15% vs 73.06%; 95% CI= 38.70%-92.09%) and cotrimoxazole (76.22%; 95% CI= 51.33%-90.79% vs 65.81%; 95% CI= 45.08-81.86%). However, meropenem (26.73%; 95% CI= 20.76%-33.68%) and fosfomycin (14.85%; 95% CI= 9.07%-23.37%) were the least resistant in E. coli and Klebsiella spp., respectively. Staphylococcus aureus strains exhibited highest resistance to penicillin (84.37%; 95% CI= 68.13%-93.16%), erythromycin (44.80%; 95% CI= 33.37%-56.79%) and oxacillin (37.35%; 95% CI= 8.76%-78.74%) and lowest resistance to rifampicin (2.94%; 95% CI= 0.59%-13.39%), fusidic acid (6.73%; 95% CI= 2.55%-16.62%) and vancomycin (13.18%; 95% CI= 2.26%-49.86%). ConclusionThis study reports a high prevalence of bacterial BSIs in Cameroon and the high resistance of these bacteria to common antibiotics. There is a pressing need to conduct BSI surveillance studies in all regions of Cameroon to generate data for evidence-based measures regarding BSI prevention and management. Prospero registration numberCRD42023482760

Antibiotic-resistant Pseudomonas aeruginosa is a common nosocomial pathogen all over the world. We detected the presence of P. aeruginosa in 22% (53 out of 238) of the test samples collected from patients with infections including secondary wound infections, abscesses and urinary tract infections admitted to two academic hospitals in Bangladesh. Resistance to carbapenems (imipenem, and meropenem) was present among 30% (16 out of 53) of these clinical P. aeruginosa isolates, which is more than 2-fold higher compared to that of previous studies. Such a rapid increase in carbapenem resistance was mediated by metallo-{beta}-lactamase (MBL). Expression of MBL was detected in 90% (14 out of 16) of these resistant isolates. Molecular analyses revealed that the carbapenem-resistant isolates carried at least one of the MBL variants, either bla-VIM or bla-NDM-1. All the bla-NDM-1 positives carried a 0.5 MDa plasmid. ERIC-PCR revealed the highly heterogeneous nature of the P. aeruginosa isolates indicating multiple sources of infection within the hospital. However, the majority of XDR isolates belonged to a single cluster of drug-resistant bacterial infections. These findings indicate that Metallo-{beta}-lactamase (MBL) mediated resistance to carbapenem in P. aeruginosa poses a serious threat to the spread of infections among hospitalized patients.

BackgroundAntimicrobial resistance (AMR) in E. coli is a global problem associated with substantial morbidity and mortality. AMR-associated genes are typically annotated based on similarity to a variants in a curated reference database with an implicit assumption that uncatalogued genetic variation within these is phenotypically unimportant. In this study we evaluated the potential for discovering new AMR-associated gene families and characterising variation within existing ones to improve genotype-to-susceptibility-phenotype prediction in E. coli. MethodsWe assembled a global dataset of 9001 E. coli sequences of which 8586 had linked antibiotic susceptibility data. Raw reads were assembled using Shovill and AMR genes extracted using the NCBI AMRFinder tool. Mash was used to calculate the similarity between extracted genes using Jaccard distances. We empirically reclustered extracted gene sequences into AMR-associated gene families (70% match) and alleles (ARGs, 100% match). ResultsThe performance of the AMRFinder database for genotype-to-phenotype predictions using strict 100% identity and coverage thresholds did not meet FDA thresholds for any of the eight antibiotics evaluated. Relaxing filters to default settings improved sensitivity with a specificity cost. For all antibiotics, a small number of genes explained most resistance although a proportion could not be explained by known ARGs; this ranged from 75.1% for co-amoxiclav to 3.4% for ciprofloxacin. Only 17,177/36,637 (47%) of ARGs detected had a 100% identity and coverage match in the AMRFinder database. After empirically reclassifying genes at 100% nucleotide sequence identity, we identified 1292 unique ARGs of which 158 (12%) were present [&ge;]10 times, 374 (29%) were present 2-9 times and 760 (59%) only once. Simulated accumulation curves revealed that discovery of new (100%-match) ARGs present more than once in the dataset plateaued relatively quickly whereas new singleton ARGs were discovered even after many thousands of isolates had been included. We identified a strong correlation (Spearman coefficient 0.76 (95% CI 0.72-0.79, p<0.001)) between the number of times an ARG was observed in Oxfordshire and the number of times it was seen internationally, with ARGs that were observed 7 times in Oxfordshire always being found elsewhere. Finally, using the example of blaTEM-1, we demonstrated that uncatalogued variation, including synonymous variation, is associated with potentially important phenotypic differences (e.g. two common, uncatalogued blaTEM-1 alleles with only synonymous mutations compared to the known reference were associated with reduced resistance to co-amoxiclav [aOR 0.57, 95%CI 0.34-0.93, p=0.03] and piperacillin-tazobactam [aOR 0.54, 95%CI 0.32-0.87, p=0.01]). ConclusionsOverall we highlight substantial uncatalogued genetic variation with respect to known ARGs, although a relatively small proportion of these alleles are repeatedly observed in a large international dataset suggesting strong selection pressures. The current approach of using fuzzy matching for ARG detection, ignoring the unknown effects of uncatalogued variation, is unlikely to be acceptable for future clinical deployment. The association of synonymous mutations with potentially important phenotypic differences suggests that relying solely on amino acid-based gene detection to predict resistance is unlikely to be sufficient. Finally, the inability to explain all resistance using existing knowledge highlights the importance of new target gene discovery.

BackgroundAntimicrobial drug resistance is of public health importance due to its potential to reduce treatment options and increase healthcare expenditure. There is, however, a paucity of studies that have examined antimicrobial resistance in countries with poor to moderate income. The present study examined the patterns and predictors of antimicrobial resistance in Staphylococcus isolates collected from humans at diagnostic laboratories in South Africa between 2012 and 2017. Method and materialsA cross-sectional study design using retrospective data of 404 217 diagnostic laboratory records of staphylococcal isolates collected between 2012 and 2017 was adopted in this study. Isolates were assessed for antimicrobial drug resistance against 24 antimicrobials. Descriptive statistics, and binary logistic regression models were used to analyse the data. Significance was assessed at < 0.05. ResultsThe highest resistance was observed against Cloxacillin (70.3%), while the lowest resistance was against Colistin (0.1%). A significant (p < 0.05) decreasing trend in AMR was observed over the study period, while a significant increasing temporal trend (p < 0.05) was observed for MDR over the same period. A Significant (p < 0.05) association was observed between specimen type, species of organism, and year of isolation with AMR outcome. Significant (p < 0.05) associations were observed between specimen type and season, with MDR. Discussion and recommendationsThe observed high levels of AMR and the increasing temporal trend in MDR is of public health concern. Clinicians should consider these findings when deciding on therapeutic options. Continued monitoring of AMR among Staphylococcus spp. and judicious use of antimicrobials in human medicine should be promoted.

BackgroundSurveillance and prediction of antibiotic resistance in Escherichia coli relies on curated databases of genes and mutations. Such databases currently lack quantitative data estimating the effect on MIC caused by the acquisition of any given element for a particular antibiotic-species combination. MethodsUsing a collection of 2875 E. coli isolates with linked whole genome sequencing and MIC data, we used multivariable interval regression models to estimate the change in MIC for specific antibiotics associated with the acquisition of genes and mutations in the AMRFinder database with and without an adjustment for population structure. We then tested the ability of these models to predict MIC and binary resistance/susceptibility using leave-one-out cross validation. FindingsWe provide quantitative estimates (with confidence intervals) of the change in MIC associated with the acquisition of genes/mutations in the NCBI-AMRFinder database. Whilst the majority of genes and mutations (89/111 (80.2%) were associated with an increased MIC, a much smaller number (27/111, 24.3%) were found to be putatively independently resistance conferring (i.e. associated with an MIC above the EUCAST breakpoint) when acquired in isolation. We found evidence of differential effects of acquired genes and mutations between different generations of cephalosporin antibiotics and demonstrated that sub-breakpoint variation in MIC can be linked to genetic mechanisms of resistance. 20,697/24,858 (83.3%, range 52.9-97.7 across all antibiotics) of MICs were correctly exactly predicted and 23,677/24,858 (95.2%, range 87.3-97.7) to within +/-1 doubling dilution. InterpretationQuantitative estimates of the independent effect on MIC of the acquisition of antibiotic resistance genes add to the interpretability and utility of existing databases. Using these estimates to predict antibiotic resistance phenotype demonstrates performance that is comparable to or better than approaches utilising machine learning models and crucially more readily interpretable. The methods outlined here could be readily applied to other antibiotic/pathogen combinations. FundingThis work was funded by the NIHR and the MRC. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed from inception to 05/04/2024 using the terms ((Escherichia coli OR E. coli) AND ((MIC) OR (minimum inhibitory concentration))) AND (predict*) AND (whole genome sequencing). Of the 56 articles identified by these search terms, eight were of direct relevance to this study. These studies generally focused on single antibiotics (3 studies), had relatively small datasets (6 studies {inverted exclamation}1000 isolates) or used machine learning approaches on pan-genomes to predict binary (i.e. susceptible/resistant) phenotypes (2 studies). Only one study attempted to predict ciprofloxacin MICs in 704 E. coli isolates using a machine learning approach with known resistance conferring genes/mutations as features. To our knowledge, there are no studies estimating the independent effect (as opposed to the total effect of all elements present) of the acquisition of specific antibiotic resistance genes (ARGs) or resistance-associated mutations on MICs of different antibiotics in E. coli more generally. What this study addsIn this study we estimate the change in MIC for particular antibiotics associated with the acquisition of specific ARGs or resistance-associated mutations, adjusting for the presence of other relevant genes and population structure. In doing so we provide an approach to greatly enhance the information provided by existing ARG databases and approaches based on predicting binary susceptible/resistant phenotypes, for example by demonstrating differential effects of ARGs on resistance to antibiotics of the same class, enriching our understanding of the relationship between genotype and phenotype in a way that is easily interpretable. Using more "parsimonious" models for prediction, we demonstrate high overall accuracy comparable to or better, and crucially more readily interpretable, than recent machine learning models. We also demonstrate a genetic basis behind sub-breakpoint variation in MIC for some antibiotics, demonstrating the value of non-dichotomised phenotypes for identifying wildtype isolates (i.e. those carrying no ARGs) with greater confidence. Implications of all available evidenceWhole genome sequencing data can be used to predict MICs for most commonly used antibiotics for managing E. coli infections with accuracy approaching that of conventional phenotyping techniques, though very major error rates remain too high for deployment in routine clinical practice. Further studies focusing on genotypes with high phenotypic heterogeneity should investigate the phenotypic replicability, genetic heritability and clinical outcomes associated with these isolates.

BackgroundThe World Health Organization (WHO), Centre for disease Control and Prevention (CDC) gives broad guidance on how to establish, implement and evaluate AMSP. However, specific action plans for effective AMSP especially in LMICs is needed, the action plan in India is not uniformly implemented across hospitals because of prevailing issues specific to diverse hospital settings. These include-non-availability of all classes of antimicrobial agents (AMA) in the hospital, lack of in-house antibiotic policy which may lead to irrational prescription and lack of skilled manpower such as clinical pharmacists who are the pillars to prescription audits. Crucial to all this is how clinical teams are constantly engaged in informed AMA prescribing. As per the NAP-AMR strategy, hospitals have been trying to implement AMSP inspite of resource constraints. Ensuring only right drugs are used at the right time, is challenging because, engaging clinical teams has been an important bottleneck. ObjectivesTo explore the key promoters, constraints, and operational feasibility of an integrated m-health intervention program on antimicrobial consumption and AMSP in five tertiary hospitals in south India; To evaluate the effectiveness of an integrated m-health intervention program on antimicrobial consumption and AMSP in five tertiary hospitals in south India; To assess the impact of an integrated AMSP m-health intervention on the incidence of multidrug-resistant organisms in five tertiary hospitals in south India MethodologyStudy will be conducted in four tertiary hospitals across south India. Pre-intervention-A baseline data collection will be done before the delivery of the intervention. Intervention: This includes capacity building of clinicians on AMSP and provision of mobile application for them to use during patient care. Implementation of intervention-A stepped wedge trail will be conducted in the selected units various departments included in the study. This will be done over 24 months. All units receiving the intervention will be followed up for the next eight months periodically. OutcomesOutcome indicators such as consumption of antimicrobial agents, incidence of multi drug resistance organisms and healthcare associated infections will be captured during the follow ups.

BackgroundPromoting the responsible use of antimicrobials is essential in tackling antimicrobial resistance. However, data on consumption and usage of antibiotics in Sub-Saharan Africa are still limited. MethodsThis was a prospective cross-sectional time series study conducted to investigate the consumption and use of third-and fourth-generation cephalosporins and fluoroquinolones in North Western Tanzania. We collect stock records from outpatient pharmacies in the hospital and conducted exist interviews each month from April to September 2021.We did descriptive analysis in Stata and Ms Excel. ResultsA total of 982586.2 DDD were consumed with a daily consumption of 1198.5 DDD per 1000 inhabitants per day over the six months. Five classes of antibiotics accounted for 70% of consumption. Beta lactams penicillins (J01C) are the most consumed at 329.25 DDD followed tetracyclines(J01A) 243.85 DDD. By WHO AWaRe Access antibiotics constituted 75%. Of the 253 interviews conducted 131 (51.8%) of the patients were male, 192 (75.9%) patients had bacterial infection as an indication. Ceftriaxone, was the most used cephalosporin and was used mostly to treat pneumonia while ciprofloxacin was the most used fluoroquinolone and was widely but mostly used for UTI and gastrointestinal infections. Up to 44% of prescriptions do not adhere to treatment guidelines. ConclusionWe report that antibiotic consumption is in concordance WHO recommendation to have >60% of antibiotics consumed from the access group. However, there is relatively high consumption of ciprofloxacin and ceftriaxone in this hospital. Additionally, there, was significant non-adherence to treatment guidelines which underscores the need to establish functioning and robust antimicrobial stewardship programs. Posted historyNONE

IntroductionDisease surveillance is an essential element of an effective response to antimicrobial resistance (AMR). Associations between AMR cases and area-level drivers such as remoteness and socio-economic disadvantage have been observed, but spatial associations when modelling routinely collected surveillance data that are often imperfect or missing have not been previously possible. AimWe aimed to use spatial modelling to adjust for area-level variables and to enhance AMR surveillance for missing or sparse data, in an effort to provide clinicians and policy makers with more actionable epidemiological information. MethodsWe used monthly antimicrobial susceptibility data for methicillin-resistant Staphylococcus aureus (MRSA) from a surveillance system in Australia. MRSA was assessed for the effects of age, sex, socio-economic and access to healthcare services indices by fitting Bayesian spatial models. ResultsWe analysed data for 77, 760 MRSA isolates between 2016 and 2022. We observed significant spatial heterogeneity in MRSA and found significant associations with age, sex and remoteness, but not socio-economic status. MRSA infections were highest in adult females aged 16-60 living in very remote regions and lowest in senior males aged 60+ years living in inner regional areas.. ConclusionCurrent disease surveillance approaches for antimicrobial resistant infections have limited spatial comparability, are not timely, and at risk of sampling bias. Bayesian spatial models borrow information from neighbouring regions to adjust for unbalanced geographical information and can fill information gaps of current MRSA surveillance. Assessment of disease spatial variation is especially critical in settings which have diverse geography, dispersed populations or in regions with limited microbiological capacity.

BackgroundHospital-acquired infections (HAIs) remain a global public-health concern, particularly in low- and middle-income countries where infection-prevention resources are limited. Surgical-site infections (SSIs) and urinary-tract infections (UTIs) are among the most frequent HAIs and contribute to increased morbidity and healthcare costs. Genomic surveillance provides insights into the diversity, antimicrobial resistance (AMR), and virulence potential of causative bacteria. MethodsFour bacterial isolates collected from Tanzanian healthcare facilities were analysed: Pseudomonas aeruginosa SS01 and SS89 (from SSIs), Alcaligenes faecalis UP17 (from a UTI), and Lysinibacillus sphaericus SS48 (from an SSI). Genomic DNA was extracted and sequenced on the Illumina platform. Reads were quality-filtered and assembled de novo using SPAdes. Genomes were annotated with Prokka. AMR genes were identified using AMRFinderPlus, CARD-RGI, and ResFinder. Virulence determinants were detected using VFDB. P. aeruginosa isolates were typed by multilocus sequence typing (MLST). Phylogenetic analysis based on single-nucleotide polymorphisms (SNPs) was performed using Snippy and IQ-TREE, and trees were visualised with iTOL. ResultsGenome sizes ranged between approximately 6.0 and 6.7 Mb with GC contents consistent with species references. MLST revealed two distinct P. aeruginosa sequence types: SS01 was closest to ST2317 (incomplete ppsA locus) and SS89 matched ST4714, indicating non-clonal origins. AMR screening detected {beta}-lactamase, aminoglycoside-modifying enzyme, and efflux-pump genes in P. aeruginosa, multidrug-efflux genes in A. faecalis, and intrinsic resistance determinants in L. sphaericus. Virulence-factor profiling identified type III-secretion, quorum-sensing, and biofilm-formation genes in P. aeruginosa; adhesion and stress-tolerance genes in A. faecalis; and sporulation and surface-adhesion genes in L. sphaericus. Phylogenetic analysis positioned the Tanzanian isolates as unique local lineages distinct from global references. ConclusionsThis study demonstrates the genomic diversity and complex AMR mechanisms of clinically important bacteria in Tanzania. The coexistence of resistance and virulence determinants underscores the need for routine genomic surveillance and strengthened antimicrobial-stewardship programs.