Predicting Severity and Disease Progression in Influenza-like Illness (Including COVID-19) (PREDICT-ILI)
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| ClinicalTrials.gov Identifier: NCT04664075 |
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Recruitment Status :
Not yet recruiting
First Posted : December 11, 2020
Last Update Posted : January 5, 2021
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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.
| Condition or disease | Intervention/treatment |
|---|---|
| Influenza SARS (Severe Acute Respiratory Syndrome) Respiratory Viral Infection Respiratory Tract Infections Infection, Bacterial Infection Viral Covid19 RNA Virus Infections | Biological: Respiratory infections |
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.
| Study Type : | Observational |
| Estimated Enrollment : | 100 participants |
| Observational Model: | Case-Only |
| Time Perspective: | Cross-Sectional |
| Official Title: | Predicting Severity and Disease Progression in Influenza-like Illness |
| Estimated Study Start Date : | January 2021 |
| Estimated Primary Completion Date : | April 30, 2021 |
| Estimated Study Completion Date : | June 30, 2021 |
- Biological: Respiratory infections
With biological samples and longitudinal observations, the aim is 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.
- Describe the aetiology of influenza-like illness in hospitalised adults [ Time Frame: Day 0 to Day 28 ]The identity of pathological organisms associated with influenza-like illness (including respiratory viruses and bacteria) will be obtained from the patient's medical record
- Describe the clinical outcomes of influenza-like illness in hospitalised adults [ Time Frame: Day 0 to Day 28 ]The following data will be collected from the patient's medical record. At enrolment, data will consist of: past medical history, clinical signs and symptoms relating to this admission, vital signs (pulse rate, blood pressure, temperature, oxygen saturation), demographics, drug history, laboratory results including diagnostic microbiological tests and interventions. Data collection on Day 28 will consist of clinical diagnosis at discharge, any febrile illness in the 7 days preceding the visit, mortality and complications between Day 0 and 28.
- Identify changes in cytokine levels during influenza-like illness in hospitalised adults [ Time Frame: Day 0 to Day 28 ]Cytokine levels (in pg/mL) will be measured in plasma and nasal lining fluid samples by MesoScale Discovery
Biospecimen Retention: Samples With DNA
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| Ages Eligible for Study: | 18 Years and older (Adult, Older Adult) |
| Sexes Eligible for Study: | All |
| Accepts Healthy Volunteers: | No |
| Sampling Method: | Probability Sample |
Inclusion Criteria:
- Healthy persons aged ≥ 18, and able to give informed consent
- Patient is admitted to hospital
- Primary reason for hospital admission is clinical suspicion of a new episode of ARI
- Onset of the following symptoms within the last 7 days: i. Sudden onset of self-reported fever OR temperature of ≥ 38°C at presentation AND ii. At least one respiratory symptom (cough, sore throat, runny or congested nose, dyspnoea) AND iii. At least one systemic symptom (headache, muscle ache, sweats or chills or tiredness).
Exclusion Criteria:
- Patient lacks capacity to provide informed consent
- Patient has been transferred from another hospital
- Patient has been previously enrolled in the study
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04664075
| Contact: Christopher Chiu, PhD | +44(0)20 383 2301 | c.chiu@imperial.ac.uk | |
| Contact: Emma Bergstrom, BSc | +44(0)7872 850 212 | e.bergstrom@imperial.ac.uk |
| United Kingdom | |
| Imperial College London | |
| London, United Kingdom | |
| Contact: Christopher Chiu, PhD +44(0)20 383 2301 c.chiu@imperial.ac.uk | |
| Contact: Emma Bergstrom, BSc +44(0)7872 850 212 e.bergstrom@imperial.ac.uk | |
| Principal Investigator: | Christopher Chiu, PhD | Imperial College London |
| Responsible Party: | Imperial College London |
| ClinicalTrials.gov Identifier: | NCT04664075 |
| Other Study ID Numbers: |
20SM6170 |
| First Posted: | December 11, 2020 Key Record Dates |
| Last Update Posted: | January 5, 2021 |
| Last Verified: | January 2021 |
| Individual Participant Data (IPD) Sharing Statement: | |
| Plan to Share IPD: | Yes |
| Plan Description: | The expectation is that after analysis the data from this study will be widely distributed in the medical and scientific community. Facilitated with presentations at local, national and international meetings, the hope is to publish widely in the medical literature. In addition there is an excellent media department at Imperial College that will publicise research that has public interest when it is published. All data will be anonymised and aggregated or pseudonymised; no identifying participant information will be published. |
| Supporting Materials: |
Study Protocol Clinical Study Report (CSR) |
| Time Frame: | Data will become available approximately 12 months from the last patient's last visit and remain available indefinitely |
| Access Criteria: | According to study protocol |
| Studies a U.S. FDA-regulated Drug Product: | No |
| Studies a U.S. FDA-regulated Device Product: | No |
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Flu Influenza SARS-CoV-2 Covid19 |
Respiratory Predict Infection RNA |
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Infection Communicable Diseases Respiratory Tract Infections Bacterial Infections Influenza, Human Virus Diseases Severe Acute Respiratory Syndrome Coronavirus Infections |
RNA Virus Infections Disease Progression Orthomyxoviridae Infections Respiratory Tract Diseases Disease Attributes Pathologic Processes Coronaviridae Infections Nidovirales Infections |