Introduction: Infectious diseases have a major impact on morbidity and mortality in hospital. Microbial diagnosis remains elusive for most cases of suspected infection which impacts on the use of antibiotics. Rapid advances in genomic technologies combined with high-quality phenotypic data have great potential to improve the diagnosis, management and clinical outcomes of infectious diseases. The aim of the Bioresource in Adult Infectious Diseases (BioAID) is to provide a platform for biomarker discovery, trials and clinical service developments in the field of infectious diseases, by establishing a registry linking clinical phenotype to microbial and biological samples in adult patients who attend hospital with suspected infection.
Methods and analysis: BioAID is a cohort study which employs deferred consent to obtain an additional 2.5mL RNA blood sample from patients who attend the Emergency Department (ED) with suspected infection when they undergo peripheral blood culture sampling. Clinical data and additional biological samples including DNA, serum and microbial isolates are obtained from BioAID participants during hospital admission. Participants are also asked to consent to be recalled for future studies. BioAID aims to recruit 10,000 patients from 5-8 sites across England. Since February 2014 >4000 individuals have been recruited to the study. The final cohort will be characterised using descriptive statistics including information on the number of cases that can be linked to biological and microbial samples to support future research studies. Ethical approval and section 251 exemption have been obtained for BioAID researchers to seek deferred consent from patients from whom a RNA specimen has been collected. Samples and meta-data obtained through BioAID will be made available to researchers worldwide following submission of an application form and research protocol.
Conclusions: BioAID will support a range of study designs spanning discovery science, biomarker validation, disease pathogenesis and epidemiological analyses of clinical infection syndromes.