A Clinically Oriented antimicrobial Resistance surveillance Network (ACORN): pilot implementation in three countries in Southeast Asia, 2019-2020

Background: Case-based surveillance of antimicrobial resistance (AMR) provides more actionable data than isolate- or sample-based surveillance. We developed A Clinically Oriented antimicrobial Resistance surveillance Network (ACORN) as a lightweight but comprehensive platform, in which we combine clinical data collection with diagnostic stewardship, microbiological data collection and visualisation of the linked clinical-microbiology dataset. Data are compatible with WHO GLASS surveillance and can be stratified by syndrome and other metadata. Summary metrics can be visualised and fed back directly for clinical decision-making and to inform local treatment guidelines and national policy. Methods: An ACORN pilot was implemented in three hospitals in Southeast Asia (1 paediatric, 2 general) to collect clinical and microbiological data from patients with community- or hospital-acquired pneumonia, sepsis, or meningitis. The implementation package included tools to capture site and laboratory capacity information, guidelines on diagnostic stewardship, and a web-based data visualisation and analysis platform. Results: Between December 2019 and October 2020, 2294 patients were enrolled with 2464 discrete infection episodes (1786 community-acquired, 518 healthcare-associated and 160 hospital-acquired). Overall, 28-day mortality was 8.7%. Third generation cephalosporin resistance was identified in 54.2% (39/72) of E. coli and 38.7% (12/31) of K. pneumoniae isolates. Almost a quarter of S. aureus isolates were methicillin resistant (23.0%, 14/61). 290/2464 episodes could be linked to a pathogen, highlighting the level of enrolment required to achieve an acceptable volume of isolate data. However, the combination with clinical metadata allowed for more nuanced interpretation and immediate feedback of results. Conclusions: ACORN was technically feasible to implement and acceptable at site level. With minor changes from lessons learned during the pilot ACORN is now being scaled up and implemented in 15 hospitals in 9 low- and middle-income countries to generate sufficient case-based data to determine incidence, outcomes, and susceptibility of target pathogens among patients with infectious syndromes

ACORN (A Clinically-Oriented Antimicrobial Resistance Surveillance Network) II: protocol for case based antimicrobial resistance surveillance

Background: Antimicrobial resistance surveillance is essential for empiric antibiotic prescribing, infection prevention and control policies and to drive novel antibiotic discovery. However, most existing surveillance systems are isolate-based without supporting patient-based clinical data, and not widely implemented especially in low- and middle-income countries (LMICs). Methods: A Clinically-Oriented Antimicrobial Resistance Surveillance Network (ACORN) II is a large-scale multicentre protocol which builds on the WHO Global Antimicrobial Resistance and Use Surveillance System to estimate syndromic and pathogen outcomes along with associated health economic costs. ACORN-healthcare associated infection (ACORN-HAI) is an extension study which focuses on healthcare-associated bloodstream infections and ventilator-associated pneumonia. Our main aim is to implement an efficient clinically-oriented antimicrobial resistance surveillance system, which can be incorporated as part of routine workflow in hospitals in LMICs. These surveillance systems include hospitalised patients of any age with clinically compatible acute community-acquired or healthcare-associated bacterial infection syndromes, and who were prescribed parenteral antibiotics. Diagnostic stewardship activities will be implemented to optimise microbiology culture specimen collection practices. Basic patient characteristics, clinician diagnosis, empiric treatment, infection severity and risk factors for HAI are recorded on enrolment and during 28-day follow-up. An R Shiny application can be used offline and online for merging clinical and microbiology data, and generating collated reports to inform local antibiotic stewardship and infection control policies. Discussion: ACORN II is a comprehensive antimicrobial resistance surveillance activity which advocates pragmatic implementation and prioritises improving local diagnostic and antibiotic prescribing practices through patient-centred data collection. These data can be rapidly communicated to local physicians and infection prevention and control teams. Relative ease of data collection promotes sustainability and maximises participation and scalability. With ACORN-HAI as an example, ACORN II has the capacity to accommodate extensions to investigate further specific questions of interest

ACORN (A Clinically-Oriented Antimicrobial Resistance Surveillance Network) II: protocol for case based antimicrobial resistance surveillance

Background: Antimicrobial resistance surveillance is essential for empiric antibiotic prescribing, infection prevention and control policies and to drive novel antibiotic discovery. However, most existing surveillance systems are isolate-based without supporting patient-based clinical data, and not widely implemented especially in low- and middle-income countries (LMICs). Methods: A Clinically-Oriented Antimicrobial Resistance Surveillance Network (ACORN) II is a large-scale multicentre protocol which builds on the WHO Global Antimicrobial Resistance and Use Surveillance System to estimate syndromic and pathogen outcomes along with associated health economic costs. ACORN-healthcare associated infection (ACORN-HAI) is an extension study which focuses on healthcare-associated bloodstream infections and ventilator-associated pneumonia. Our main aim is to implement an efficient clinically-oriented antimicrobial resistance surveillance system, which can be incorporated as part of routine workflow in hospitals in LMICs. These surveillance systems include hospitalised patients of any age with clinically compatible acute community-acquired or healthcare-associated bacterial infection syndromes, and who were prescribed parenteral antibiotics. Diagnostic stewardship activities will be implemented to optimise microbiology culture specimen collection practices. Basic patient characteristics, clinician diagnosis, empiric treatment, infection severity and risk factors for HAI are recorded on enrolment and during 28-day follow-up. An R Shiny application can be used offline and online for merging clinical and microbiology data, and generating collated reports to inform local antibiotic stewardship and infection control policies. Discussion: ACORN II is a comprehensive antimicrobial resistance surveillance activity which advocates pragmatic implementation and prioritises improving local diagnostic and antibiotic prescribing practices through patient-centred data collection. These data can be rapidly communicated to local physicians and infection prevention and control teams. Relative ease of data collection promotes sustainability and maximises participation and scalability. With ACORN-HAI as an example, ACORN II has the capacity to accommodate extensions to investigate further specific questions of interest

<b>AutoMated tool for Antimicrobial resistance Surveillance System version 3.0 (AMASSv3.0)</b>

Release date: 24 April 2024Below is a list of features that we have incorporated in AMASSv3.0Main analysisWe have added “Annex C: Cluster signals”. This annex shows potential clusters of patients with AMR infection identified using the SaTScan (www.satscan.org).We have left processed (i.e. de-duplicated and/or merged) data files in the folder “Report_with_patient_identifiers” so that users can use the processed data files (e.g. deduplicated and merged data files for each AMR pathogen) for any further analysis and internal use after using the AMASS.;Enterococcus faecalis and E. faecium have been explicitly included in the pathogens under the survey (while Enterococcus spp. are used in the AMASS version 2.0);We have added a few antibiotics in the list of antibiotics for a few pathogens under the survey;Technical aspectsWe have added a configuration for “Annex C: Cluster signals” in Configuration.xlsx;We have improved the algorithm to support more several date formats;We have improved the algorithm to translate data files;We have improved Data_verification_logfile report to present local languages of the variable names and values (according to how they were recorded in the data files) in the report;We have improved Annex B: Data indicators to support a larger data set;We have used only Python rather than R + Python (as used in the AMASSv2.0);We have set a default config for infection origin stratification by allowing a specimen collected two calendar days before the hospital admission date and one day after the hospital discharge date into consideration. This config supports the real-world setting that several hospitals in LMICs (particularly Thailand) have patients stay in the hospital (e.g. at ER) due to many limitations before official admissions can be made. The one days after an admission record but the specimen has already taken for pathogenic culturation. The one day after the hospital discharge date supports the real-word setting that some laboratories record specimen-arrival-to-the-laboratory date in their data set rather the specimen-collection-from-the-patient date as specimen date. Therefore, some specimens that were collected on the hospital discharge date (either discharge alive or died) have specimen dates one day after the hospital discharge.We have improved the algorithm to include patients who were not yet discharged from the hospital (i.e. having no discharge dates yet) in the analysis by truncating at the last date of specimen date in the whole data set (usually at the end of the year [e.g. 31 Dec] or the survey period). The change allows us to improve the ‘bed-days at risk’ and ‘bed-days at risk for hospital-origin infection’ to be precisely estimated based on their duration in the hospital. For example, a patient who was admitted on 30 Dec 2023, had blood specimen collected for culture on 31 Dec 2023, and were still in the hospital on 10 Jan 2024. Then, the microbiology data file and hospital admission date file were exported on 10 Jan 2024 for the data of year 2023 [specimen dates and hospital admission dates from 1 Jan 2023 to 31 Jan 2023]. This patient would be included in the analysis using AMASSv3.0, assuming that the days at risk of BSI was from 30 Dec to 31 Dec 2023, and the specimen collected on 31 Dec 2023 would be included in the analysis. This patient would not be included in the analysis of the “Section 6: Mortality” as discharge outcome is still missing. In the AMASSv2.0, this patient would not be included in the analysis due to the missing discharge date and missing discharge outcomes).We have revised text and fixing bugs/typos.AutoMated tool for Antimicrobial resistance Surveillance System (AMASS) was developed as an offline, open−access and easy−to−use application that allows a hospital to perform data analysis independently and generate antimicrobial resistance (AMR) surveillance reports stratified by infection origin from routinely collected electronic databases. The application was built in Python, which is a free software environment. The application has been placed within a user−friendly interface that only requires the user to double−click on the application icon.AMASS performs data analysis and generates reports automatically. The raw data files required are hospital admission and microbiology databases. Firstly, the application translates and de-duplicated the microbiology data file, and produces the AMR surveillance reports without stratification by infection origin (Sections 1 and 2 in the report). Secondly, the application then merges the microbiology and hospital admission data files, analyzes the merged data, and produces the AMR surveillance reports with stratification by infection origin (Sections 3, 4 and 5 in the report). Finally, the application then performs a statistical analysis to estimate all−cause mortality of patients following AMR infection (Section 6 in the report). AMASS uses a tier-based approach. For example, in cases that only the microbiology data file with the results of culture positive samples is available, only the AMR surveillance report without stratification by infection origin will be generated.Further details on how to use the application can be found at https://www.amass.website.</p

Input and output screen for the simplified interface of the three-tests in one-population model (Walter and Irwig model) provided on the website (http://mice.tropmedres.ac).

<p>See text for details.</p

Input and output screen for the simplified interface of the two-tests in two-population model (Hui and Walter model) provided on the website (http://mice.tropmedres.ac).

<p>See text for details. </p

Schematic diagram of the web-based application (http://mice.tropmedres.ac).

<p>(A) Users input the data set and settings into a table provided on the webpage, (B) The central web server invisibly transforms the data set and settings inputted into multiple text files suitable for the statistical software, and automatically runs the Bayesian latent class models (LCM) using the R and WinBUGS programs. (C) The results estimated by Bayesian LCM are provided on the webpage within few minutes. </p

ACORN (A Clinically-Oriented Antimicrobial Resistance Surveillance Network): a pilot protocol for case based antimicrobial resistance surveillance

Background: Antimicrobial resistance (AMR) / drug resistant infections (DRIs) are a major global health priority. Surveillance data is critical to inform infection treatment guidelines, monitor trends, and to assess interventions. However, most existing AMR / DRI surveillance systems are passive and pathogen-based with many potential biases. Addition of clinical and patient outcome data would provide considerable added value to pathogen-based surveillance. Methods: The aim of the ACORN project is to develop an efficient clinically-oriented AMR surveillance system, implemented alongside routine clinical care in hospitals in low- and middle-income country settings. In an initial pilot phase, clinical and microbiology data will be collected from patients presenting with clinically suspected meningitis, pneumonia, or sepsis. Community-acquired infections will be identified by daily review of new admissions, and hospital-acquired infections will be enrolled during weekly point prevalence surveys, on surveillance wards. Clinical variables will be collected at enrolment, hospital discharge, and at day 28 post-enrolment using an electronic questionnaire on a mobile device. These data will be merged with laboratory data onsite using a flexible automated computer script. Specific target pathogens will be Streptococcus pneumoniae, Staphylococcus aureus, Salmonella spp., Klebsiella pneumoniae, Escherichia coli, and Acinetobacter baumannii. A bespoke browser-based app will provide sites with fully interactive data visualisation, analysis, and reporting tools. Discussion: ACORN will generate data on the burden of DRI which can be used to inform local treatment guidelines / national policy and serve as indicators to measure the impact of interventions. Following development, testing and iteration of the surveillance tools during an initial six-month pilot phase, a wider rollout is planned

Knowledge about risk factors for common infectious diseases in Thailand (n = 4,203).

<p>Knowledge about risk factors for common infectious diseases in Thailand (n = 4,203).</p

Characteristics of 4,203 adult participants who completed the questionnaire about awareness and knowledge of common infectious diseases in Thailand.

<p>* Characteristics of participants were compared with the national data in 2010 reported by the National Statistical Office Thailand [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121311#pone.0121311.ref016" target="_blank">16</a>].</p><p>** 169 participants did not answer the question about education.</p><p>Characteristics of 4,203 adult participants who completed the questionnaire about awareness and knowledge of common infectious diseases in Thailand.</p