Antimicrobial Resistance and Infection Control最新文献

Background: Effective infection prevention and control programs can positively influence quality of care, increase patient safety, and protect health care providers. Chlorine, a widely used and effective chemical disinfectant, is recommended for infection prevention and control in health care settings. However, lack of consistent chlorine availability limits its use. Electrolytic chlorine generators can address limited chlorine supply and stockouts by enabling onsite production of readily usable, high-quality chlorine cost-effectively. We report the feasibility (i.e., performance, acceptability, chlorine availability, and cost) of the electrolytic STREAM Disinfectant Generator (Aqua Research, New Mexico, USA) device for infection prevention and control in primary health care facilities in Uganda.

Methods: We installed STREAM devices in 10 primary health care facilities in central and western Uganda. Commercial chlorine inventory records (stock cards) were reviewed in each facility to calculate average liters of chlorine received and used per month. These values were compared with actual STREAM chlorine production volumes over the study period to determine its impact on chlorine availability. We collected acceptability data from a purposive sample of device users (n = 16), hospital administrators (n = 10), and district health officers (n = 6) who had been directly involved in the operation or supervision of the STREAM device. We descriptively analyzed the acceptability data by user group and evaluated qualitative responses manually using a thematic approach. Cost data were normalized and modeled to determine a break-even and cost-savings analysis across a five-year period (the minimum expected lifespan of the STREAM device).

Results: Chlorine was consistently available without any reported stockouts during the evaluation period. STREAM chlorine production resulted in a 36.9 percent cost-savings over a five-year period compared to commercial chlorine. User acceptability of the STREAM device was high among STREAM operators, hospital administrators, and district health officers, with all respondents reporting that STREAM moderately or significantly improved infection prevention and control practices in the health facility. Overall, 88 percent of device users and 100 percent of hospital administrators wished to continue using the STREAM device instead of commercial chlorine products.

Conclusion: The STREAM device has demonstrated significant potential to strengthen infection prevention and control practices in health care facilities in Uganda. Based on the preliminary results, the STREAM device should be considered a promising tool for district hospitals and large health centers facing infection prevention and control challenges in Uganda and elsewhere, provided water and electricity are available. Going forward, implementation of the STREAM device could also be c

Background: Inappropriate or overuse of antibiotic prescribing in primary care highlights an opportunity for antimicrobial stewardship (AMS) programs aimed at reducing unnecessary use of antimicrobials through education, policies and practice audits that optimize antibiotic prescribing. Evidence from the early part of the pandemic indicates a high rate of prescribing of antibiotics for patients with COVID-19. It is crucial to surveil antibiotic prescribing by primary care providers from the start of the pandemic and into its endemic stage to understand the effects of the pandemic and better target effective AMS programs.

Methods: This was a matched pair population-based cohort study that used electronic medical record (EMR) data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Participants included all patients that visited their primary care provider and met the inclusion criteria for COVID-19, respiratory tract infection (RTI), or non-respiratory or influenza-like-illness (negative). Four outcomes were evaluated (a) receipt of an antibiotic prescription; (b) receipt of a non-antibiotic prescription; (c) a subsequent primary care visit (for any reason); and (d) a subsequent primary care visit with a bacterial infection diagnosis. Conditional logistic regression was used to evaluate the association between COVID-19 and each of the four outcomes. Each model was adjusted for location (rural or urban), material and social deprivation, smoking status, alcohol use, obesity, pregnancy, HIV, cancer and number of chronic conditions.

Results: The odds of a COVID-19 patient receiving an antibiotic within 30 days of their visit is much lower than for patients visiting for RTI or for a non-respiratory or influenza-like-illnesses (AOR = 0.08, 95% CI[0.07, 0.09] compared to RTI, and AOR = 0.43, 95% CI[0.38, 0.48] compared to negatives). It was found that a patient visit for COVID-19 was much less likely to have a subsequent visit for a bacterial infection at all time points.

Conclusions: Encouragingly, COVID-19 patients were much less likely to receive an antibiotic prescription than patients with an RTI. However, this highlights an opportunity to leverage the education and attitude change brought about by the public health messaging during the COVID-19 pandemic (that antibiotics cannot treat a viral infection), to reduce the prescribing of antibiotics for other viral RTIs and improve antibiotic stewardship.

The implementation of isolation precautions for patients with suspected Coronavirus Disease 2019 (COVID-19) and pending test results is resource intensive. Due to the limited availability of single-bed rooms at our institution, we isolated patients with suspected COVID-19 together with patients without suspected COVID-19 on-site in multiple-bed rooms until SARS-CoV-2-test results were available. We evaluated the likelihood of SARS-CoV-2 transmission to individuals sharing the room with patients isolated on-site. This observational study was performed at the University Hospital Basel, Switzerland, from 03/20 - 11/20. Secondary attack rates were compared between patients hospitalized in multiple-bed rooms and exposed to individuals subjected to on-site isolation precautions (on-site isolation group), and patients exposed to individuals initially not identified as having COVID-19, and not placed under isolation precautions until the diagnosis was suspected (control group). Transmission events were confirmed by whole-genome sequencing. Among 1,218 patients with suspected COVID-19, 67 (5.5%) tested positive for SARS-CoV-2. Of these, 21 were isolated on-site potentially exposing 27 patients sharing the same room. Median contact time was 12 h (interquartile range 7-18 h). SARS-CoV-2 transmission was identified in none of the patients in the on-site isolation group vs. 10/63 (15.9%) in the control group (p = 0.03). Isolation on-site of suspected COVID-19-patients in multiple-bed rooms avoided single-room occupancy and subsequent in-hospital relocation for many patients without confirmed SARS-CoV-2-infection. The absence of secondary transmission among the exposed patients in the on-site isolation group allows for assessment of the risk/benefit ratio of this strategy given the limitation of a small sample size.

Background: Before the COVID-19 pandemic there has been a constant increase in antimicrobial resistance (AMR) of Escherichia coli, the most common cause of urinary tract infections and bloodstream infections. The aim of this study was to investigate the impact of the COVID-19 pandemic on extended-spectrum β-lactamase (ESBL) production in urine and blood E. coli isolates in Finland to improve our understanding on the source attribution of this major multidrug-resistant pathogen.

Methods: Susceptibility test results of 564,233 urine (88.3% from females) and 23,860 blood E. coli isolates (58.8% from females) were obtained from the nationwide surveillance database of Finnish clinical microbiology laboratories. Susceptibility testing was performed according to EUCAST guidelines. We compared ESBL-producing E. coli proportions and incidence before (2018-2019), during (2020-2021), and after (2022) the pandemic and stratified these by age groups and sex.

Results: The annual number of urine E. coli isolates tested for antimicrobial susceptibility decreased 23.3% during 2018-2022 whereas the number of blood E. coli isolates increased 1.1%. The annual proportion of ESBL-producing E. coli in urine E. coli isolates decreased 28.7% among males, from 6.9% (average during 2018-2019) to 4.9% in 2022, and 28.7% among females, from 3.0 to 2.1%. In blood E. coli isolates, the proportion decreased 32.9% among males, from 9.3 to 6.2%, and 26.6% among females, from 6.2 to 4.6%. A significant decreasing trend was also observed in most age groups, but risk remained highest among persons aged ≥ 60 years.

Conclusions: The reduction in the proportions of ESBL-producing E. coli was comprehensive, covering both specimen types, both sexes, and all age groups, showing that the continuously increasing trends could be reversed. Decrease in international travel and antimicrobial use were likely behind this reduction, suggesting that informing travellers about the risk of multidrug-resistant bacteria, hygiene measures, and appropriate antimicrobial use is crucial in prevention. Evaluation of infection control measures in healthcare settings could be beneficial, especially in long-term care.

Background: Multidrug-resistant organisms (MDRO) pose a significant threat to public health. Intensive Care Units (ICU), characterized by the extensive use of antimicrobial agents and a high prevalence of bacterial resistance, are hotspots for MDRO proliferation. Timely identification of patients at high risk for MDRO can aid in curbing transmission, enhancing patient outcomes, and maintaining the cleanliness of the ICU environment. This study focused on developing a machine learning (ML) model to identify patients at risk of MDRO during the initial phase of their ICU stay.

Methods: Utilizing patient data from the First Medical Center of the People's Liberation Army General Hospital (PLAGH-ICU) and the Medical Information Mart for Intensive Care (MIMIC-IV), the study analyzed variables within 24 h of ICU admission. Machine learning algorithms were applied to these datasets, emphasizing the early detection of MDRO colonization or infection. Model efficacy was evaluated by the area under the receiver operating characteristics curve (AUROC), alongside internal and external validation sets.

Results: The study evaluated 3,536 patients in PLAGH-ICU and 34,923 in MIMIC-IV, revealing MDRO prevalence of 11.96% and 8.81%, respectively. Significant differences in ICU and hospital stays, along with mortality rates, were observed between MDRO positive and negative patients. In the temporal validation, the PLAGH-ICU model achieved an AUROC of 0.786 [0.748, 0.825], while the MIMIC-IV model reached 0.744 [0.723, 0.766]. External validation demonstrated reduced model performance across different datasets. Key predictors included biochemical markers and the duration of pre-ICU hospital stay.

Conclusions: The ML models developed in this study demonstrated their capability in early identification of MDRO risks in ICU patients. Continuous refinement and validation in varied clinical contexts remain essential for future applications.

The use of antimicrobials in Thailand has been reported as one of the highest in the world in human and animal sectors. Our engagement project aimed to improve our understanding of the issue of antimicrobial use and antimicrobial resistance (AMR) among adult Thai communities, and co-create locally relevant solutions to AMR, especially those focusing on raising awareness to improve related policies in Thailand.We conducted a series of online and in-person 'conversations' according to Wellcome's 'Responsive Dialogues' engagement approach, designed to bring together different voices to understand complex AMR problems and find potential solutions. This approach enabled key AMR stakeholders and policy makers to hear directly from communities and members of the public, and vice versa. Conversations events took place between 25 November 2020 and 8 July 2022, and we engaged 179 AMR key stakeholders and members of the public across Thailand.The issues found were: there were quite a lot of misunderstandings around antimicrobials and AMR; participants felt that communications and engagement around antimicrobial resistance had limited reach and impact; asking for and taking antibiotics for self-limiting ailments is a social norm in Thailand; and there appeared to be a wide availability of cheap antimicrobials. To mitigate the spread of AMR, participants suggested that the messages around AMR should be tailored to the target audience, there should be more initiatives to increase general health literacy, there should be increased availability of AMR related information at the local level and there should be increased local leadership of AMR mitigation efforts.Trial registration Thaiclinicaltrials.org registration: TCTR20210528003 (28/05/2021).

Objectives: Genomic surveillance of Klebsiella pneumoniae carbapenemase-producing Klebsiella pneumoniae (KPC-Kp) is crucial for virulence, drug-resistance monitoring, and outbreak containment.

Methods: Genomic analysis on 87 KPC-Kp strains isolated from 3 Northern Italy hospitals in 2019-2021 was performed by whole genome sequencing (WGS), to characterize resistome, virulome, and mobilome, and to assess potential associations with phenotype resistance and clinical presentation. Maximum Likelihood and Minimum Spanning Trees were used to determine strain correlations and identify potential transmission clusters.

Results: Overall, 15 different STs were found; the predominant ones included ST307 (35, 40.2%), ST512/1519 (15, 17.2%), ST20 (12, 13.8%), and ST101 (7, 8.1%). 33 (37.9%) KPC-Kp strains were noticed to be in five transmission clusters (median number of isolates in each cluster: 5 [3-10]), four of them characterized by intra-hospital transmission. All 87 strains harbored Tn4401a transposon, carrying bla_KPC-3 (48, 55.2%), bla_KPC-2 (38, 43.7%), and in one case (1.2%) bla_KPC-33, the latter gene conferred resistance to ceftazidime/avibactam (CZA). Thirty strains (34.5%) harbored porin mutations; of them, 7 (8.1%) carried multiple Tn4401a copies. These strains were characterized by significantly higher CZA minimum inhibitory concentration compared with strains with no porin mutations or single Tn4401a copy, respectively, even if they did not overcome the resistance breakpoint of 8 ug/mL. Median 2 (IQR:1-2) virulence factors per strain were detected. The lowest number was observed in ST20 compared to the other STs (p<0.001). While ST307 was associated with infection events, a trend associated with colonization events could be observed for ST20.

Conclusions: Integration of genomic, resistance score, and clinical data allowed us to define a relative diversification of KPC-Kp in Northern Italy between 2019 and 2021, characterized by few large transmission chains and rare inter-hospital transmission. Our results also provided initial evidence of correlation between KPC-Kp genomic signatures and higher MIC levels to some antimicrobial agents or colonization/infection status, once again underlining WGS's importance in bacterial surveillance.

Background: Detection of pathogen-related clusters within a hospital is key to early intervention to prevent onward transmission. Various automated surveillance methods for outbreak detection have been implemented in hospital settings. However, direct comparison is difficult due to heterogenicity of data sources and methodologies. In the hospital setting, we assess the performance of three different methods for identifying microbiological clusters when applied to various pathogens with distinct occurrence patterns.

Methods: In this retrospective cohort study we use WHONET-SaTScan, CLAR (CLuster AleRt system) and our currently used percentile-based system (P75) for the means of cluster detection. The three methods are applied to the same data curated from 1st January 2014 to 31st December 2021 from a tertiary care hospital. We show the results for the following case studies: the introduction of a new pathogen with subsequent endemicity, an endemic species, rising levels of an endemic organism, and a sporadically occurring species.

Results: All three cluster detection methods showed congruence only in endemic organisms. However, there was a paucity of alerts from WHONET-SaTScan (n = 9) compared to CLAR (n = 319) and the P75 system (n = 472). WHONET-SaTScan did not pick up smaller variations in baseline numbers of endemic organisms as well as sporadic organisms as compared to CLAR and the P75 system. CLAR and the P75 system revealed congruence in alerts for both endemic and sporadic organisms.

Conclusions: Use of statistically based automated cluster alert systems (such as CLAR and WHONET-Satscan) are comparable to rule-based alert systems only for endemic pathogens. For sporadic pathogens WHONET-SaTScan returned fewer alerts compared to rule-based alert systems. Further work is required regarding clinical relevance, timelines of cluster alerts and implementation.

Background: Methicillin-resistant Staphylococcus aureus (MRSA) is one of the leading causes of mortality due to bacterial antimicrobial resistance. While S. aureus is common in skin and soft tissue infections (SSTI) in Africa, data on MRSA rates are scarce and reports vary widely across the continent (5%-80%). In this study, we describe the proportion of MRSA causing SSTI in Lambaréné, Gabon, over an 11-year period.

Methods: We retrospectively analyzed data from 953 bacterial specimens collected from inpatients and outpatients with SSTI at the Albert Schweitzer Hospital, Lambaréné, Gabon, between 2009 and 2019. We determined temporal changes in the prevalence of MRSA and identified risk factors for SSTI with MRSA.

Results: 68% of all specimens with bacterial growth yielded S. aureus (n = 499/731), of which 7% (36/497) with antimicrobial susceptibility testing were identified as MRSA. Age above 18 years, admission to the surgical ward, and deep-seated infections were significantly associated with MRSA as the causative agent. After an initial decline from 7% in 2009, there was a marked increase in the proportion of MRSA among all S. aureus from SSTI from 3 to 20% between 2012 and 2019. The resistance rate to erythromycin was significantly higher in MRSA than in methicillin-susceptible S. aureus (73% vs. 10%), and clindamycin resistance was detected exclusively in MRSA isolates (8%).

Conclusion: The increasing proportion of MRSA causing SSTI over the 11-year period contrasts with many European countries where MRSA is on decline. Continuous surveillance of MRSA lineages in the hospital and community along with antibiotic stewardship programs could address the increasing trend of MRSA.

Background: The digitalization of information systems allows automatic measurement of antimicrobial consumption (AMC), helping address antibiotic resistance from inappropriate drug use without compromising patient safety.

Objectives: Describe and characterize a new automated AMC surveillance service for intensive care units (ICUs), with data stratified by referral clinic and linked with individual patient risk factors, disease severity, and mortality.

Methods: An automated service collecting data from the electronic medical record was developed, implemented, and validated in a healthcare region in northern Sweden. We performed an observational study from January 1, 2018, to December 31, 2021, encompassing general ICU care for all ≥18-years-olds in a catchment population of 270000 in secondary care and 900000 in tertiary care. We used descriptive analyses to associate ICU population characteristics with AMC outcomes over time, including days of therapy (DOT), length of therapy, defined daily doses, and mortality.

Results: There were 5608 admissions among 5190 patients with a median age of 65 (IQR 48-75) years, 41.2% females. The 30-day mortality was 18.3%. Total AMC was 1177 DOTs in secondary and 1261 DOTs per 1000 patient days and tertiary care. AMC varied significantly among referral clinics, with the highest total among 810 general surgery admissions in tertiary care at 1486 DOTs per 1000 patient days. Case-mix effects on the AMC were apparent during COVID-19 waves highlighting the need to account for case-mix. Patients exposed to more than three antimicrobial drug classes (N = 242) had a 30-day mortality rate of 40.6%, with significant variability in their expected rates based on admission scores.

Conclusion: We introduce a new service and instructions for automating local ICU-AMC data collection. The versatile long-term ICU-AMC metrics presented, covering patient factors, referral clinics and mortality outcomes, are expected to be beneficial in refining antimicrobial drug use.