Multi-centre EuRopean study of MAjor Infectious Disease Syndromes (MERMAIDS) - Acute Respiratory Infections

Based on historic and recent pandemics or pandemic threats, as well as on
knowledge of transmissibility and epidemiology, emerging or (re-)emerging
pathogens causing acute respiratory infections (ARI) are considered the most
likely candidates to cause the next pandemic.
There is a substantial body of knowledge on clinical risk factors for disease
severity and outcome of ARIs. Validated clinical risk stratification tools,
such as the Pneumonia Severity Index (PSI) and CURB-65 score predict outcomes
and help to guide clinical decision making. Patient groups at risk for
developing severe disease are well known, such as the elderly, patients with
chronic pulmonary, cardiovascular or metabolic disease or immunocompromised
patients. In national guidelines for prevention and treatment of ARI these risk
groups are often combined.
However, the underlying pathophysiologic processes that determine the severity
of ARI across the diversity of risk groups and pathogens likely differ and are
not well understood. This means that within broad phenotypic *risk groups* it
is currently not possible to predict who is at increased risk of becoming
severely ill. Consequently, there are no opportunities to tailor preventive and
therapeutic interventions to individual risk. Personalised or precision
medicine is becoming a reality in some areas of oncology, but has yet to enter
infectious diseases. In patients that do become moderately or severely ill,
there is an assumption that the underlying pathophysiological processes are the
same, hence all patients will benefit equally from the same intervention, such
as antivirals and immunomodulators. Increased insight into differences in
underlying pathophysiological processes across clinical backgrounds and
pathogens will open avenues for the development of predictive algorithms that
anticipate severity at individual patient levels and direct strategies for
individualized therapeutic interventions, hence improving clinical outcomes.
Additionally, there may be commonalities in host- and pathogen-related factors
that determine outcome which could be used in generic diagnostic (e.g.
distinguishing viral versus bacterial infection) and therapeutic approaches.
This could be of relevance to the next emerging novel respiratory pathogen,
hence contributing to clinical preparedness for a new epidemic threat.
Analysis of the host gene expression profile (transcriptome) provides a
high-resolution insight into host responses and pathophysiology, providing
opportunities to understand the specific contributions of risk factors,
comorbidities and pathogen-specific traits. As an example, human challenge
studies using influenza virus, respiratory syncytial virus (RSV) and human
rhinovirus (HRV) have indicated that peripheral blood gene expression profiles
distinguish between specific viral aetiologies, and symptomatic and
asymptomatic influenza virus infection, thus providing valuable insight into
the development of clinical disease as well as avenues towards host-targeted
approaches of aetiological diagnostics.

The primary aim of this study is to identify host and pathogen related
determinants of disease severity of ARI. In this study we will include a
prospectively defined and sufficiently sized patient cohort that will allow
investigations of relative contributions of host and pathogen factors in
development of mild, moderate and severe ARI. The sample size of this study is
determined to provide adequate power to identify differences in RNA expression
profiles between groups of patients with ARI stratified into groups by major
categories of comorbidity (chronic pulmonary/cardiovascular/metabolic disease),
and the major causative pathogens (Influenza A, HRV, RSV and Streptococcus
pneumoniae). Setting a high bar with regards to sample size, by basing our
power calculation on the ability to detect differentially expressed genes
between these predefined groups, will ensure the groups will be large enough to
also compare less complex determinants of ARI severity, e.g. local and systemic
innate and adaptive immune markers, and pathogen markers (e.g. pathogen load).
Our analysis will identify differentially expressed genes between patients with
different comorbidities within and across ARI aetiologies and severities by
combining a fold change assessment (>2-fold nominal change expression levels)
combined with a p<0.05 correcting for multiple comparisons using the
Benjamini-Hochberg False Discovery Rate (FDR) < 0.05 in accordance with
guidelines from the MicroArray Quality Control (MAQC) group.

International, multicentre, prospective observational study.

This is an observational study with sampling with negligible risks. Nose swabs
and venous blood sampling may lead to some discomfort. Blood sampling may cause
some pain and bruising. The total amount of blood draw per sampling day is
15ml, with a maximum of 4 sampling days in a period of 28 days. This is
acceptable in adults.