ACORN (A Clinically-Oriented Antimicrobial Resistance Surveillance Network): a pilot protocol for case based antimicrobial resistance surveillance.
Paul TurnerElizabeth A AshleyOlivier J CelhayAnousone DouangnouvongRaph Leonardus HamersClare L LingYoel LubellThyl MiliyaTamalee RobertsChansovannara SoputhyPham Ngoc ThachManivanh VongsouvathNaomi WaithiraPrapass WannapinijHindrik Rogier van DoornPublished in: Wellcome open research (2020)
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.
Keyphrases
- antimicrobial resistance
- public health
- drug resistant
- escherichia coli
- multidrug resistant
- electronic health record
- acinetobacter baumannii
- healthcare
- big data
- klebsiella pneumoniae
- staphylococcus aureus
- global health
- mental health
- clinical trial
- randomized controlled trial
- clinical practice
- health insurance
- palliative care
- deep learning
- climate change
- emergency department
- data analysis
- study protocol
- artificial intelligence
- chronic pain
- cystic fibrosis
- risk factors
- pain management
- candida albicans
- cross sectional
- pulmonary embolism
- drug induced
- network analysis
- smoking cessation