COVID-19 Survival - The COVIVA Study
Study Details
Study Description
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.
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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.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| COVID-19 suspects Patients presenting with suspected COVID-19 to the emergency department of the University Hospital Basel. |
Outcome Measures
Primary Outcome Measures
- short-term prognosis [at 30 days]
incidence of death during index hospital stay
Secondary Outcome Measures
- Admission to the intensive care unit (ICU) [at 30 days]
Incidence of subsequent ICU admission
- Invasive ventilation (Intubation) [at 30 days]
Incidence of subsequent intubation
- Need for extracorporal membrane oxygenation (ECMO) [at 30 days]
Incidence of subsequent ECMO
- Hemodynamic support [at 30 days]
Incidence of subsequent need for hemodynamic support
- Length of ICU stay [at 30 days]
Number of days (overnight-stays) spent on the ICU
- Acute respiratory distress Syndrome (ARDS) [at 30 days]
Incidence o ARDS
- Myocardial injury [at 30 days]
Incidence of myocardial injury
- ST-segment elevation myocardial infarction [at 30 days]
Incidence of myocardial infarction
- In-hospital resource use [at 30 days]
Types and numbers of resources used
- EQ-5D questionnaire [at 30 days]
Quality of life assessed using the EQ-5D questionnaire resulting in an indexed score ranging from 0 to 1 with higher numbers indicating higher quality of life.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Clinically suspected or confirmed SARS-CoV-2 infection triaged to the ED
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SARS-CoV-2 swab test performed (result may be pending at time of study enrolment)
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Age ≥18 years
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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.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | University Hospital Basel | Basel | Switzerland | 4031 |
Sponsors and Collaborators
- University Hospital, Basel, Switzerland
Investigators
- Principal Investigator: Raphael Twerenbold, MD, Department of Cardiology and Cardiodiovascular Research Institute Basel (CRIB)
- Principal Investigator: Gabriela Kuster Pfister, MD, Department of Cardiology, University Hospital Basel
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- UHospital Switz