COVID-19 Survival - The COVIVA Study

Overview

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.

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.