Clinical trial NCT04366765: COVID-19 Survival - The COVIVA Study
Official title: Coronavirus Disease 19 Survival - The COVIVA Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: COVID-19
SARS-CoV 2
Keywords: precision medicine
risk prediction
pathomechanisms
renin-angiotensin-aldosterone system (RAAS)
biomarkers
biobank
serum
resource allocation
artificial intelligence
big data
Lead sponsor: University Hospital, Basel, Switzerland
Sponsor class: OTHER
First posted: 2020-04-29
Enrollment: 1325
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All patients with clinical suspicion of COVID-19 or confirmed SARS-CoV2 infection triaged to the Emergency Department (ED) of the University Hospital Basel, Switzerland, are potentially eligible for enrolment in this study. An upstream pre-triage located outside the ED (in a nearby church) routinely performs clinical risk assessment in all patients with clinical suspicion of SARS-CoV-2 infection irrespective of symptoms severity and, if clinically indicated, nasopharyngeal swab testing. While patients with mild symptoms and in good clinical condition are assigned towards outpatient management, patients in poorer clinical conditions and higher likelihood of need for in-patient management are assigned to the ED, where they will be screened for potential COVIVA inclusion.

Brief summary: The COVID-19 pandemic poses a major and imminent challenge for health care systems regarding patient triage and allocation of limited resources worldwide. The involved pathogenetic mechanisms as well as the clinical value of established and emerging biomarkers for early risk prediction are largely unknown.

To fill these gaps in knowledge, investigators designed the prospective, interdisciplinary, observational, case-control "COronaVIrus surviVAl (COVIVA)" study platform, aiming to deliver an open-source platform to i) perform extensive clinical and biomarker phenotyping in COVID-19 suspects presenting to the emergency department (ED) as well as admitted to the intensive care unit, ii) compare clinical and biomarker profiles of COVID-19 patients with a control group, iii) derive and validate personalized risk prediction models for early clinical decision support, and iv) explore pathophysiological mechanisms including but not limited to inflammatory, immunological and cardiovascular pathways.

Blood samples (serum) are routinely collected for bio banking both in cases and controls. Patients are followed 30 days after discharge. Personalized risk prediction models will be derived and validated based on advanced statistical models including machine-based learning incorporating a variety of clinical parameters and biomarker signatures (including digitally stored in-hospital data, e.g. imaging, ECG, ventilation parameters). Close cooperation with multiple other national and international COVID-19 cohorts is endorsed.

The personalized risk prediction models from the COVIVA study will support clinicians in the most challenging process of limited resource allocation in a timely fashion. In addition, pathophysiological mechanisms and differences in mild and severe variants of COVID-19 as well as in the control group can be extensively studied in a multidisciplinary approach.

Detailed description: Background: The current COVID-19 pandemic poses a major and imminent challenge for health care systems regarding patient triage and allocation of limited resources worldwide, but also in Switzerland. Data from severly affected countries impressively demonstrate that COVID-19 fatality rates rapidly increase in times of overloaded health care services. Cardiovascular comorbidity seems to be associated with impaired outcome, e.g. with admission to intensive care unit (ICU) or death. However, a direct causal relation is questionable and pathophysiological mechanisms of the cardiovascular involvement such as the renin-angiotensin-aldosterone system are poorly understood. The clinical value of established and emerging biomarkers is largely unknown. Accordingly, early and reliable personalized risk prediction represents a major unmet clinical need, as it may allow evidence-based clinical decision aid for most effective resource allocation in the common fight against the COVID-19 pandemic.

Aims: To fill these gaps in knowledge, investigators designed the "COronaVIrus surviVAl (COVIVA)" study. With this study, investigators aim to deliver an open-source platform to i) perform extensive clinical and biomarker phenotyping in COVID-19 suspects presenting to the emergency department (ED) and in COVID-19 patients with subsequent ICU admission, ii) compare clinical and biomarker profiles of COVID-19 patients with a control group, iii) derive and validate personalized risk prediction models for early clinical decision support, and iv) explore pathophysiological mechanisms including inflammatory and cardiovascular pathways.

Methodology: The COVIVA study is an ongoing, prospective, interdisciplinary, observational, case-control study with active enrolment of consecutive patients with clinical suspicion of COVID-19 triaged to the Emergency Department (ED) of the University Hospital in Basel, Switzerland. Patients with a positive nasopharyngeal swab test for severe acute respiratory syndrome (SARS)-CoV-2 will serve as cases while the remainders will serve as controls. Detailed clinical patient's phenotyping (e.g. comorbidities, medications, symptoms, vitals, ECG and imaging data), extended laboratory analyses and blood sampling for bio banking are performed once in all patients (cases and control) at time of ED presentation and serially thereafter in the subset of COVID-19 patients with subsequent need for ICU admission. Primary outcome measure is in-hospital mortality; secondary outcome measures include the need for ICU admission, invasive mechanical ventilation, hemodynamic support, 30-day post-discharge mortality, length of hospital and ICU stay, resource use and quality of life 30 days after discharge and its composites. Personalized risk prediction models will be derived and validated based on advanced statistical models including machine-based learning incorporating a variety of clinical parameters and biomarker signatures (including digitally stored in-hospital data, e.g. imaging, ECG, ventilation parameters). Close cooperation with multiple other national and international COVID-19 cohorts is endorsed.

Potential significance: The personalized risk prediction models from the COVIVA study will support clinicians in the most challenging process of limited resource allocation in a timely fashion. In addition, pathophysiological mechanisms and differences in mild and severe variants of COVID-19 as well as in the control group can be extensively studied in a multidisciplinary approach.

Eligibility criteria: Inclusion Criteria:

* Clinically suspected or confirmed SARS-CoV-2 infection triaged to the ED
* SARS-CoV-2 swab test performed (result may be pending at time of study enrolment)
* Age ≥18 years
* Patient or legally authorized representative is willing to sign local General consent form

Exclusion Criteria:

* Patient or legally authorized representative is unable or unwilling to participate in the study.

Primary outcomes:
measure: short-term prognosis
timeFrame: at 30 days
Secondary outcomes:
measure: Admission to the intensive care unit (ICU)
timeFrame: at 30 days
measure: Invasive ventilation (Intubation)
timeFrame: at 30 days
measure: Need for extracorporal membrane oxygenation (ECMO)
timeFrame: at 30 days
measure: Hemodynamic support
timeFrame: at 30 days
measure: Length of ICU stay
timeFrame: at 30 days
measure: Acute respiratory distress Syndrome (ARDS)
timeFrame: at 30 days
measure: Myocardial injury
timeFrame: at 30 days
measure: ST-segment elevation myocardial infarction
timeFrame: at 30 days
measure: In-hospital resource use
timeFrame: at 30 days
measure: EQ-5D questionnaire
timeFrame: at 30 days
Other outcomes:
Locations:
city: Basel
country: Switzerland
facility: University Hospital Basel
Study officials:
affiliation: Department of Cardiology and Cardiodiovascular Research Institute Basel (CRIB)
name: Raphael Twerenbold, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: Department of Cardiology, University Hospital Basel
name: Gabriela Kuster Pfister, MD
role: PRINCIPAL_INVESTIGATOR
References: