Clinical trial NCT04363008: Investigating the Role of Biomarkers in Predicting Outcome for COVID 19
Official title: COVID-19 Inflammatory Blood Biomarkers for Clinical Management, Prognosis and Evaluation of Interventions
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: COVID 19
Lead sponsor: University of British Columbia
Sponsor class: OTHER
First posted: 2020-04-27
Enrollment: 150
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients admitted to ICU with COVID 19.

Brief summary: Emergent experimental and anecdotal evidence has indicated that critically ill COVID-19 patients demonstrate two patient sub-types (called phenotypes). In one group the disease progresses slowly and patients have a low potential of developing mild respiratory failure, but in the other group, an exaggerated immune response (hyper-inflammation/cytokine storm) may be linked to the onset of precipitous respiratory failure, termed acute respiratory distress syndrome. This syndrome is responsible for a large portion of COVID-19 associated mortality. Thus, determining links between hyper-inflammation and acute respiratory distress syndrome in COVID-19 patients is of immediate importance. Blood samples will undergo a number of analyses to help us to understand as much as possible about COVID-19. We will also study any differences in physiologic and cytokine levels before and after patients are treated with immunomodulatory therapies as part of clinical care in COVID-19 patients.

Detailed description: PURPOSES

1. Determine the prevalence of a COVID-19 hyper-inflammatory "cytokine storm" phenotype in patients at Vancouver General Hospital
2. Determine any potential links between cytokine storm and ARDS in COVID-19 patients
3. Profile patients with mild and severe disease in an attempt to identify biomarkers that could be developed into a rapid test for triaging patients who require urgent care from those that will recover on their own
4. Elucidate the impact of genetic variation on clinical outcomes from COVID-19
5. Identify SARS-CoV-2 relevant RNAs
6. To determine differences in physiologic and cytokine levels before and after patients are treated with immunomodulatory therapies as part of clinical care in COVID-19 patients.

HYPOTHESES

1. Primary: Based off of previous reports, approximately 50% of COVID-19 patients will demonstrate a cytokine storm phenotype
2. Secondary: a)Patients exhibiting cytokine storm will demonstrate a higher incidence of ARDS b)We will identify biomarkers for rapid testing c)Host gene variation will influence the clinical outcome from COVID-19 infection

JUSTIFICATION

The 2019 novel human coronavirus, originally named 2019-nCoV, and now referred to as the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), originated in Wuhan of the Hubei Province in China late December 2019. This virus causes the disease now known as COVID-19. Following its discovery, SARS-CoV-2 has since had its genome sequenced and cell entry receptor determined. Its rapid global spread and evidence of community transmission indicate a likely protracted influence on our health care system and economy. This necessitates a coordinated response from health care workers and scientists to discern strategies for the treatment, and therapeutic management of COVID-19.

Outside of vaccine and anti-viral development efforts, the focus on novel therapeutic interventions has been comparatively minimal, with few clinical trial reports to date, which has elicited a call to action to explore host-directed therapies in addition to other therapeutic strategies. Recently published clinical descriptions of this disease have indicated that many patients suffer from both a hyper-inflammatory state, referred to as a "cytokine storm" with subsequent death often attributable to the development of acute respiratory distress syndrome (ARDS). However, while cytokine storm appears associated with disease severity, key links between the presence of cytokine storm and development of ARDS, which is currently responsible for the largest proportion of COVID-19 deaths, has yet to be established. Delineation of such a link and clinical establishment of a hyper-inflammatory (i.e. cytokine storm) phenotype would provide precedence for specific anti-inflammatory treatments to reduce the incidence of COVID-19 associated ARDS and mortality as this remains debated currently.

CyTOF analyses: is a single cell proteomic tool that will be applied using a custom panel of 31 monoclonal antibodies we developed to the widest possible array of blood lineage cells. McNagny developed this technology as a platform for the AllerGEN NCE (Associate Scientific Director of this NCE). It identifies, in unprecedented detail, rare subsets of mature blood and progenitor cells. It includes markers of innate lymphoid cells (ILCs) that play potent roles in priming immune responses and also bone marrow blood cell progenitors. Chronic inflammation and severe acute inflammation both lead to a rapid ramping up of inflammatory cell production and premature release of bone marrow progenitors. The panel has been internally validated and shown utility in identifying adult patients with chronic allergic disease, a population which appears to be more severely impacted by respiratory infections. There will be a goal to profile patients with mild and severe disease in an attempt to identify biomarkers that could be developed into a rapid test for triaging patients who require urgent care from those that will recover on their own.

Genetic Testing: Authors have shown that host genetic differences have a significant impact on the clinical outcomes of sepsis. For instance, we have shown that a single nucleotide variant in the CETP gene more than doubles the risk of 28-day mortality from sepsis. The hypothesis is that similar to other forms of critical illness, host gene variation will also influence the clinical outcome from COVID-19 infection. This work is designed to elucidate the impact of genetic variation on clinical outcomes from COVID-19.

RNA Testing: Circulating cell-free RNA species are typically encapsulated in extracellular vesicles (EVs) that shield them from degradation in body fluids. A wide variety of circulating RNA biotypes are present, with micro RNAs (miRNAs) being the most studied class. These and other circulating RNAs can participate in cell-to-cell communication, can be differentially expressed in patients compared to controls, and can be associated with disease progression, dissemination, or drug responses in a variety of acute and critical care indications including sepsis, neurotrauma, and cardiovascular disease.

Conditions are optimized to obtain reliable and complete circulating RNA data from minimal input volumes of human plasma or serum. Initial studies using 200 ul serum yielded \~15ng RNA, clearly exceeding the minimal input of 100pg to generate libraries for next generation sequencing. Pilot analysis from 3 healthy human volunteer donors passed quality criteria exceeding 10M reads/sample and confirmed that 98% of exosomal miRNAs were detected in total signatures of an average of 359 miRNAs. Importantly, this method also yields data on the entire transcriptional landscape of potentially informative circulating RNAs from sample volumes very feasible for clinical studies. Further optimization studies using both serum and plasma from a single human donor showed that good quality RNA data could be obtained from as little as 50µl input volume and that the expected RNA biotype distributions were found for both serum and plasma

OBJECTIVES

Better understand 1)the links between cytokine storm and ARDS in COVID-19 patients 2)the biomarker profile of COVID-19 patients with mild and severe disease 3)the impact of genetic variation on clinical outcomes in COVID-19 patients

RESEARCH DESIGN

Overview

Patients admitted to the Intensive Care Unit (ICU) and High Acuity Unit (HAU) at Vancouver General Hospital (VGH) or Surrey Memorial Hospital (SMH) with suspected COVID-19 will be quarantined in an isolation unit. Here, specimens will be collected for laboratory confirmation of SARS-CoV-2. Clinical care will commence, and be guided by the discretion of attending physicians in accordance with VGH/VCH's current operating procedure for the management of COVID-19.

Once clinical care has commenced, a blood sample will be collected on days 1-7, 10, 14, and 21 following ICU/HAU admission by clinical staff in the ICU/HAU into vacutainer serum separator tubes (SST, Vacutainer®, Becton \& Dickinson). These samples will be analyzed for a panel of cytokines, inflammatory markers, CyTOF, RNA and genetic testing. Analyses will be performed at 1)Dr Cheryl Wellington's lab at the David Mowafaghian Centre for Brain Health (UBC), 2)Dr Kelly McNagny's lab, DMCBH, UBC., 3)Dr. Liam Brunham's lab at the Centre for Heart Lung Innovation (HLI), St Paul's Hospital.

A nested matched cohort study will be conducted to examine the changes in the changes in ventilator free days, PaO2/FiO2 and inflammatory cytokines before after administration of immunomodulatory therapies which are administration as part of routine clinical care for cytokine storm syndrome.

STATISTICAL ANALYSIS

The recency of this disease outbreak makes robust statistical power testing difficult. However by digitizing data from Huang and colleague's recent work, which is one of several reports demonstrating cytokine storm in critically ill COVID-patients, a sample calculation based off of their IL-1b, IL-2, IL-10, and TNF-α data is provided. The below presented power calculations are for independent samples non-parametric Mann-Whitney-U test and were conducted in G\*power (V3.1.9.1):

IL-1: This was not significantly different in the report by Huang and colleagues (Huang et al., 2020).

IL-2: With the estimated mean±SD derived from Huang and Colleagues of 9.5±4.3 and 6.7±4.3 pg/mL of IL-2 in critically ill versus non-critically ill COVID-19 patients,\~40 patients per group (80 total) would be needed to detect a difference (power = 0.80, alpha = 0.05)

IL-6: This was not measured by Huang and colleagues (Huang et al., 2020), however, with a sample size of 150 patients total, Ruan and colleagues were able to detect significant differences in IL-6 levels (Ruan et al., 2020).

IL-10: With the estimated mean±SD derived from Huang and Colleagues of 19.5±39.6 and 5.3±4.3 pg/mL of IL-10 in critically ill versus non-critically ill COVID-19 patients, \~60 patients per group (120 total) would be needed to detect a difference (power = 0.80, alpha = 0.05).

TNF-α: With the estimated mean±SD derived from Huang and Colleagues of 93.2±20.8 and 71.7±21.4 pg/mL of TNF-α in critically ill versus non-critically ill COVID-19 patients, \~17 patients per group (34 total) would be needed to detect a difference (power = 0.80, alpha = 0.05)

Given the above presented sample size estimates, that a marked difference in IL-6 was also reported by Ruan and colleagues, and the anticipated admission rate of COVID-19 patients over the next several months, we aim to recruit up to 150 patients.

A log2 fold change \>1 and an adjusted p value of \<0.05 will be used as the threshold of significance for differentially regulated RNAs. Ingenuity Pathway Analysis will be used to Identify causal networks, with further analysis set at a threshold of a negative log p value \>20. We will use the Antimicrobial Response, Inflammatory Response and Infectious Disease filters to identify SARS-CoV-2 relevant RNAs. Heat maps will be generated by comparison analysis.

Analysis Plan

Primary: We will compare the levels of cytokines between patients that develop ARDS and those that do not. We will use independent samples t-tests in the data is normal (determined via Shapiro Wilks test), or Mann-Whitney-U tests if the data is not normally distributed.

A priori analysis: We will assess the effects of immunosuppresant or anti-viral miedcations given as part of clinical care out physiological outcomes and the changes in the serum biomarkers.

Secondary: In exploratory analyses aimed at fulfilling our secondary aim, we will utilize our daily cytokine measures to determine if there are cytokine thresholds that are associated with the development of ARDS in COVID-19 patients.

Nested matched cohort study:

1. Examine the differences in the ventilator free days in the tocilizumab (administered as part of clinical care) and supportive groups using an independent samples two-tailed t-test.
2. Examine the differences in the cardiorespiratory physiologic outcomes (PaO2/FiO2, mean arterial pressure and heart rate) in the tocilizumab (administered as part of clinical care) and supportive groups using independent samples two-tailed t-tests.
3. Examine the differences in serum inflammatory cytokines in the tocilizumab (administered as part of clinical care) and supportive groups using independent samples two-tailed t-tests.

Eligibility criteria: Inclusion Criteria:

* Patients admitted to VGH or SMH with confirmed COVID-19
* Admitted to the High Acuity Unit or Intensive Care Unit at VGH or SMH
* An arterial line is in place as part of clinical care. If arterial line is on longer insitu the sample will be collected to coincide with usual care blood collection. This will negate the need for additional venipuncture

Exclusion Criteria:

* Those who do not meet inclusion criteria

Primary outcomes:
measure: Inflammation
timeFrame: 24 hours
measure: Oxygenation
timeFrame: 24 hours
Secondary outcomes:
measure: Chronic Pulmonary outcomes
timeFrame: 8 to 12 weeks after discharge
measure: Pulmonary artery pressure using transthoracic echocardiography
timeFrame: 8 to 12 weeks after discharge
measure: Exertion
timeFrame: 8 to 12 weeks after discharge
measure: Quality of life assessment
timeFrame: 8 to 12 weeks after discharge
Other outcomes:
measure: Duration of mechanical ventilation
timeFrame: 30 days
Locations:
city: Vancouver
country: Canada
facility: Vancouver General Hospital
state: British Columbia
Study officials:
affiliation: University of British Columbia
name: Mypinder Sekhon, MD
role: PRINCIPAL_INVESTIGATOR
References: