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

Sponsor

University Hospital Tuebingen (Other)

Overall Status

Recruiting

CT.gov ID

NCT04828915

Collaborator

Max-Planck-Institute Tuebingen (Other)

1,000

Enrollment

Location

10.9

Anticipated Duration (Months)

91.4

Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Covid19	Other: Machine learning Other: Machine based evaluation

Study Design

Study Type:

Observational [Patient Registry]

Anticipated Enrollment :

1000 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

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

Actual Study Start Date :

Feb 1, 2021

Anticipated Primary Completion Date :

Jul 31, 2021

Anticipated Study Completion Date :

Dec 31, 2021

Arms and Interventions

Arm	Intervention/Treatment
Training cohort Randomly selection of 80% of the study population. The machine learning algorithm is trained on this dataset	Other: Machine learning Machine learning on vital parameters, clinical symptoms and underlying diseases
Validation cohort Randomly selection of 20% of the study population. The machine learning algorithm which was trained on the basis of the training data cohort is validated on the validation cohort.	Other: Machine based evaluation Quantification of the prediction power and identification of the most relevant predictive parameters

Arm

Intervention/Treatment

Training cohort

Randomly selection of 80% of the study population. The machine learning algorithm is trained on this dataset

Other: Machine learning

Machine learning on vital parameters, clinical symptoms and underlying diseases

Validation cohort

Randomly selection of 20% of the study population. The machine learning algorithm which was trained on the basis of the training data cohort is validated on the validation cohort.

Other: Machine based evaluation

Quantification of the prediction power and identification of the most relevant predictive parameters

Outcome Measures

Primary Outcome Measures

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

Probability of Participants for Intensive Care Unit Admission [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]
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 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]
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]
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]
Probability of Participants for hospitalisation [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]

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

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

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	University Hospital of Tuebingen	Tuebingen		Germany	72076

Sponsors and Collaborators

University Hospital Tuebingen
Max-Planck-Institute Tuebingen

Investigators

Study Chair: Bernhard Schoelkopf, PhD, Max-Planck-Institute, Tuebingen, Germany
Principal Investigator: Juergen Hetzel, MD, University Hospital of Tuebingen, Tuebingen, Germany