Early Detection of Clinical Deterioration in Patients With COVID-19 Using Machine Learning

Covid19 Clinical Trial

— COVID-19  

Official title:

Early Detection of Clinical Deterioration in Patients With COVID-19 Using Machine Learning

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04828915
• Other study ID #: TEDDI
• Status: Recruiting
• Start date: February 1, 2021
• Est. completion date: December 31, 2021

Study information

• Verified date: January 2021
• Source: University Hospital Tuebingen
• Contact: Annika Buchholz, PhD
• Phone: +49 151 51819576
• Email: annika.buchholz[@]tuebingen.mpg.de
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1000
• Est. completion date: December 31, 2021
• Est. primary completion date: July 31, 2021
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Clinical Deterioration
• Covid19

Intervention

Other:
• Machine learning
Machine learning on vital parameters, clinical symptoms and underlying diseases
• Machine based evaluation
Quantification of the prediction power and identification of the most relevant predictive parameters

Locations

Country	Name	City	State
Germany	University Hospital of Tuebingen	Tuebingen

Sponsors (2)

Lead Sponsor	Collaborator
University Hospital Tuebingen	Max-Planck-Institute Tuebingen

Country where clinical trial is conducted

Germany, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	Probability of Participants for Intensive Care Unit Admission		Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Secondary	Probability of Participants for Fatal Outcome		Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Secondary	Prediction of persisting health impairment by using standardized questionnaires		Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Secondary	Detection of symptoms, vital parameters and comorbidities predicting clinical course		Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Secondary	Influence of size of training data set		Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Secondary	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
Secondary	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
Secondary	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
Secondary	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
Secondary	Probability of Participants for hospitalisation		Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Secondary	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