Clinical Trial Details
— Status: Terminated
Administrative data
NCT number |
NCT04664075 |
Other study ID # |
20SM6170 |
Secondary ID |
|
Status |
Terminated |
Phase |
|
First received |
|
Last updated |
|
Start date |
January 25, 2021 |
Est. completion date |
April 30, 2022 |
Study information
Verified date |
April 2024 |
Source |
Imperial College London |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
Respiratory infections such as colds, flu and pneumonia affect millions of people around the
world every year. Most cases are mild, but some people become very unwell. Influenza ('flu')
is one of the most common causes of lung infection. Seasonal flu affects between 10% and 46%
of the population each year and causes around 12 deaths in every 100,000 people infected. In
addition, both influenza and coronaviruses have caused pandemics in recent years, leading to
severe disease in many people. Although flu vaccines are available, these need to change
every year to overcome rapid changes in the virus and are not completely protective.
This study aims to find and develop predictive tests to better understand how and when
flu-like illness progresses to more severe disease. This may help to decide which people need
to be admitted to hospital, and how their treatment needs to be increased or decreased during
infection.
The aim is to recruit 100 patients admitted to hospital due to a respiratory infection. It is
voluntary to take part and participants can choose to withdraw at any time. The study will
involve some blood and nose samples. This will be done on Day 0, Day 2 and Discharge from
hospital, and an out-patient follow-up visit on Day 28. The data will be used to develop
novel diagnostic tools to assist in rational treatment decisions that will benefit both
individual patients and resource allocation. It will also establish research preparedness for
upcoming pandemics.
Description:
Despite clinical advances and decades of research, the ability to reliably predict the course
of respiratory viral diseases such as influenza and coronavirus infections remains poor. The
aim of this project is to develop a platform for identifying and developing predictive tests
by combining physiological data and correlates of severity in influenza-like infections so
that progression to severe pulmonary involvement can be anticipated during respiratory viral
infection. This would then permit safe discharge of patients with self-limiting disease or
more rapid intensification of treatment as appropriate.
Respiratory infections are among the most important causes of severe disease worldwide, with
the major respiratory viruses responsible for overwhelming pressure on health services each
winter due to annual surges in incidence. The two most common viral causes of severe lung
disease, influenza and respiratory syncytial virus (RSV), are responsible for ~50% of
hospital admissions in children and 22% in adults, with mortality greatest in older people.
As the population ages, this burden of disease is steadily increasing. Furthermore, the
continual risk of newly emergent pandemic influenza strains that arise unpredictably is
universally considered one of the most critical threats to global health and socioeconomic
stability. This has been demonstrated by the recent COVID-19 pandemic.
Risk factors for severe influenza have been investigated extensively in clinical cohorts,
with older age, co-morbidities, obesity and pregnancy all increasing the likelihood of severe
disease. However, accurate prognostic markers remain elusive and the dynamics of the response
to respiratory viral infection has not been explored in naturally-infected patients.
Furthermore, biomarker discovery has been limited by heterogeneity in virus strain and dose;
delays in timing of presentation; and patient-level confounders. To address these issues, the
investigators have conducted controlled human infections with influenza and RSV since 2010,
to investigate mechanisms of immunopathogenesis with a particular focus on disease in the
human respiratory tract. Recent preliminary data from a cohort of volunteers infected with
the influenza A(H1N1)2009 strain showed that rapid changes in the transcriptome of whole
blood occurred within 2 days of virus exposure. During the 2009 influenza pandemic, similar
studies were also performed with hospitalised patients. There, transcriptomic analysis of
blood showed similar antiviral signatures in less severely unwell individuals but divergent
signatures associated with poor clinical outcomes.
The aim of this project is to identify and test predictors of disease progression and
clinical deterioration in patients with influenza-like illness, in order to develop novel
methods to more accurately determine the need for hospital admission and treatment
intensification during respiratory viral infection. To further develop and test these
biomarkers in an independent cohort of naturally-infected patients, hospitalised adults with
influenza-like illness will be recruited within 24 hours of admission and samples obtained
from blood and nose at 3 subsequent time-points.
Using these data, predictive transcriptomic signatures will be identified. Longitudinal
samples and clinical data will then be used to test, validate and refine them in affected
local populations. These findings will then be translated into novel diagnostic tools and a
biobank established for further investigation of the virology and immunopathogenesis of
severe respiratory viral infections.