CRiPSI/CRiPSI-X: Covid-19 Risk Prediction in Swiss ICUs-Trial/Extension

Automatically Closed · 2020 until 2022

Clinical Studies
Multicentric, KSSG as main centre
Automatically Closed
Start Date
End Date
Study Design
All adult patients with laboratory confirmed Covid-19 will be included und prospectively evaluated who will be hospitalized in the ICU (KSSG, University Geneva, Clinica Luganese Moncucco). Clinical and biological samples (blood, urine, nasopharyngeal swabs, endotracheal specimens, bronchoalveolar lavage, stool) are collected on admission to the ICU and weekly until ICU discharge. For patients initially hospitalized at the KSSG, there are also follow-up appointments at 6 (-9) and 12 (±1) months at outpatient clinics of the KSSG for collection of biological specimens. We will assess pulmonary function, exercise tolerance, sleep-disordered breathing, chest CT imaging and perform specific neuropsychological testing using a battery of questionnaires. Nutritional intake will be evaluated with a food questionnaire.
covid; covid-19; corona; risk prediction; ICU; epidemiologic
Brief description/objective

Outcome of patients with critical Covid-19 varies. We study risk of and predictors for poor outcome in adult patients with Covid-19 admitted to ICU in a multicenter study. In addition, we assess the prevalence of, risk factors for and mechanisms involved in the prolonged symptoms of surviving patients. We hypothesize that poor outcome and death can be predicted by I) clinical and epidemiologic risk factors including comorbidities, medications, laboratory parameters, radiological signs and risk scores; II) candidate genes or certain host genetic polymorphisms determined with genome-wide association analysis (GWAS); III) host response of all biological pathways that may contribute to disease phenotypes using untargeted transcriptomics and metabolomics; IV) diversity, composition and total burden of the respiratory microbiome, with an abundance of butyrate-producers in the gut and respiratory microbiome being protective. In addition, we hypothesize that initial assessment including clinical severity and multi-omics analysis can predict long-term outcomes including quality of life and neuropsychological outcomes in survivors. Using machine learning algorithms, we will obtain a final comprehensive prediction model integrating all identified predictors.