Background

Current antimicrobial resistance (AMR) surveillance systems are typically passive, and pathogen-focused, based on routine antimicrobial susceptibility testing (AST) results alone generated by clinical microbiology laboratories. These systems need more relevant patient-level metadata and clinical syndromic denominators to inform treatment guidelines and decision-making appropriately, and, especially in low- and middle-income countries (LMIC), suffer from various biases due to a lack of diagnostic stewardship and underutilisation of diagnostic microbiology resources.

Collecting samples for microbiologic testing is often not part of a standard diagnostic work-up for many clinical syndromes. This issue can be due to many factors, including a need for more trust between clinicians and the microbiology laboratory and (national) insurance systems that do not reimburse microbiological diagnostics. Therefore, samples are more likely to be collected only in more severe cases or in cases of treatment failure. This limits direct assessment and subsequent modelling of the clinically relevant impacts and burden of drug-resistant infections (DRI).

Microbiologists often do not receive clinical information important for interpreting laboratory results and surveillance data, e.g. whether an infection is community- or hospital-acquired. In addition, patients have access to over-the-counter antibiotics in the community and are often already taking these when admitted to the hospital. These biases favour an overrepresentation of results from DRI among surveillance data. Therefore, if one used the current surveillance network results and resistance proportions to inform clinical guidelines, there is a risk of contributing to the problem of AMR rather than the solution and advocating the use of broader spectrum antibiotic regimens that would be justified if data were more representative.

The utility of integrated patient and laboratory-based surveillance, i.e. case-based surveillance, has been highlighted recently. In addition to the bias-related problems noted above, several key patient-level questions may not be adequately answered by passive pathogen-focussed AMR surveillance:

• What is the impact and cost of DRI at the patient level?
• What are the patient-level risk factors for DRI in a particular setting?
• Which AMR-syndrome combinations are associated with the poorest outcomes in particular patient groups?

High-quality patient-level surveillance data from LMICs are necessary to inform models to determine the impact of AMR, using big datasets with key patient-level variables and to identify opportunities for intervention. The concept of ACORN is operationally efficient case-based AMR surveillance that can be deployed in low-resource settings to add value to existing laboratory capacity-building efforts.

Aims

This multi-country project aims to implement clinical antimicrobial resistance (AMR) surveillance of hospitalised patients with suspected acute bacterial infections at up to 15 sites in 9 African and Asian countries. Secondary Objectives are to characterise drug-resistant infections (DRI) by clinical syndrome, place of acquisition (CAI, HAI, HCAI), patient group (adult, paediatric, neonatal), and location (site, country, region); determine the attributable mortality for extended-spectrum beta-lactamase-producing Escherichia coli and methicillin-resistant Staphylococcus aureus bloodstream infection; determine the major indications for prescribing parenteral antibiotics by patient group (adult, paediatric, neonatal), the timing of prescription (day of admission versus >2 days after admission), and location (site, country, region); determine the major empiric antibiotics used by clinical syndrome, place of acquisition (CAI, HAI, HCAI), patient group (adult, paediatric, neonatal), and location (site, country, region).