Clinical trial NCT04828915: Early Detection of Clinical Deterioration in Patients With COVID-19 Using Machine Learning
Official title: Early Detection of Clinical Deterioration in Patients With COVID-19 Using Machine Learning
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Covid19
Keywords: Machine learning
Artificial intelligence
Clinical course
Lead sponsor: University Hospital Tuebingen
Sponsor class: OTHER
Collaborators: name: Max-Planck-Institute Tuebingen
First posted: 2021-04-02
Enrollment: 1000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients with detection of SARS-CoV2

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.


Eligibility criteria: 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

Primary outcomes:
measure: Probability of Participants for Hospitalisation or Fatal Outcome
timeFrame: Detection of severe acute respiratory syndrome- Corona Virus 2 (SARS-CoV2) to recovery, hospitalisation or fatal outcome up to 5 weeks
Secondary outcomes:
measure: Probability of Participants for Intensive Care Unit Admission
timeFrame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
measure: Probability of Participants for Fatal Outcome
timeFrame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
measure: Prediction of persisting health impairment by using standardized questionnaires
timeFrame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
measure: Detection of symptoms, vital parameters and comorbidities predicting clinical course
timeFrame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
measure: Influence of size of training data set
timeFrame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
measure: Influence of viral load on the course of disease/ clinical outcome
timeFrame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
measure: Influence of different virus variants on the course of disease/ clinical outcome
timeFrame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
measure: Influence of SARS-CoV2 vaccination (yes/no) on the course of disease/ clinical outcome
timeFrame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
measure: Evaluation of parameters (symptoms, vital parameters, comorbidities) according to their potential of clinical course predictions
timeFrame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
measure: Probability of Participants for hospitalisation
timeFrame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
measure: Influence of different SARS-CoV2 vaccines on the course of disease/ clinical outcome
timeFrame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Other outcomes:
Locations:
city: Tuebingen
country: Germany
facility: University Hospital of Tuebingen
Study officials:
affiliation: Max-Planck-Institute, Tuebingen, Germany
name: Bernhard Schoelkopf, PhD
role: STUDY_CHAIR
affiliation: University Hospital of Tuebingen, Tuebingen, Germany
name: Juergen Hetzel, MD
role: PRINCIPAL_INVESTIGATOR
References: