Early Detection of Clinical Deterioration in Patients With COVID-19 Using Machine Learning
- Conditions
- Covid19
- Registration Number
- NCT04828915
- Lead Sponsor
- University Hospital Tuebingen
- Brief Summary
The aim of this study is to use artificial intelligence in the form of machine learning analysing vital signs as well as symptoms of patients suffering from Covid19 to identify predictors of disease progression and severe course of disease.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- UNKNOWN
- Sex
- All
- Target Recruitment
- 1000
Inclusion Criteria
- Written informed consent
- Age >= 18 years
- Detection of SARS-CoV2 within the past 5 days
Exclusion Criteria
- Inability to measure vital parameters and document symptoms
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Probability of Participants for Hospitalisation or Fatal Outcome Detection of severe acute respiratory syndrome- Corona Virus 2 (SARS-CoV2) to recovery, hospitalisation or fatal outcome up to 5 weeks
- Secondary Outcome Measures
Name Time Method Probability of Participants for Intensive Care Unit Admission Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks Prediction of persisting health impairment by using standardized questionnaires Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks Detection of symptoms, vital parameters and comorbidities predicting clinical course Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks Influence of different virus variants on the course of disease/ clinical outcome Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks Influence of SARS-CoV2 vaccination (yes/no) on the course of disease/ clinical outcome Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks Probability of Participants for hospitalisation Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks Probability of Participants for Fatal Outcome Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks Evaluation of parameters (symptoms, vital parameters, comorbidities) according to their potential of clinical course predictions Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks Influence of different SARS-CoV2 vaccines on the course of disease/ clinical outcome Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks Influence of size of training data set Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks Influence of viral load on the course of disease/ clinical outcome Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Related Research Topics
Explore scientific publications, clinical data analysis, treatment approaches, and expert-compiled information related to the mechanisms and outcomes of this trial. Click any topic for comprehensive research insights.
What machine learning models predict clinical deterioration in NCT04828915?
How do vital sign biomarkers correlate with severe disease progression in NCT04828915?
What molecular mechanisms underlie AI-identified predictors of severe COVID-19?
Are machine learning approaches more effective than standard-of-care for early detection in NCT04828915?
What adverse events are associated with machine learning-based monitoring in NCT04828915?
Trial Locations
- Locations (1)
University Hospital of Tuebingen
🇩🇪Tuebingen, Germany
University Hospital of Tuebingen🇩🇪Tuebingen, GermanyAnnika Buchholz, Ph.D.Contact+4915151819576annika.buchholz@tuebingen.mpg.deJuergen Hetzel, M.D.Principal InvestigatorBijoy N Atique, M.D.Sub InvestigatorMaik Haentschel, M.D.Sub Investigator