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

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04828915
Recruitment Status : Recruiting
First Posted : April 2, 2021
Last Update Posted : April 2, 2021
Sponsor:
Collaborator:
Max-Planck-Institute Tuebingen
Information provided by (Responsible Party):
University Hospital Tuebingen

Study Description
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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
Covid19 Other: Machine learning Other: Machine based evaluation

Study Design
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Study Type : Observational [Patient Registry]
Estimated Enrollment : 1000 participants
Observational Model: Cohort
Time Perspective: Prospective
Target Follow-Up Duration: 6 Months
Official Title: Early Detection of Clinical Deterioration in Patients With COVID-19 Using Machine Learning
Actual Study Start Date : February 1, 2021
Estimated Primary Completion Date : July 31, 2021
Estimated Study Completion Date : December 31, 2021

Resource links provided by the National Library of Medicine


Groups and Cohorts
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Group/Cohort 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
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Primary Outcome Measures :
  1. Probability of Participants for Hospitalisation or Fatal Outcome [ Time Frame: Detection of severe acute respiratory syndrome- Corona Virus 2 (SARS-CoV2) to recovery, hospitalisation or fatal outcome up to 5 weeks ]

Secondary Outcome Measures :
  1. Probability of Participants for Intensive Care Unit Admission [ Time Frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks ]
  2. Probability of Participants for Fatal Outcome [ Time Frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks ]
  3. Prediction of persisting health impairment by using standardized questionnaires [ Time Frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks ]
  4. Detection of symptoms, vital parameters and comorbidities predicting clinical course [ Time Frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks ]
  5. Influence of size of training data set [ Time Frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks ]
  6. Influence of viral load on the course of disease/ clinical outcome [ Time Frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks ]
  7. Influence of different virus variants on the course of disease/ clinical outcome [ Time Frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks ]
  8. Influence of SARS-CoV2 vaccination (yes/no) on the course of disease/ clinical outcome [ Time Frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks ]
  9. Evaluation of parameters (symptoms, vital parameters, comorbidities) according to their potential of clinical course predictions [ Time Frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks ]
  10. Probability of Participants for hospitalisation [ Time Frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks ]
  11. Influence of different SARS-CoV2 vaccines on the course of disease/ clinical outcome [ Time Frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks ]

Eligibility Criteria
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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Sampling Method:   Probability Sample
Study Population
Patients with detection of SARS-CoV2
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
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04828915


Contacts
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Contact: Annika Buchholz, PhD +49 151 51819576 annika.buchholz@tuebingen.mpg.de

Locations
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Germany
University Hospital of Tuebingen Recruiting
Tuebingen, Germany, 72076
Contact: Annika Buchholz, Ph.D.    +4915151819576    annika.buchholz@tuebingen.mpg.de   
Contact: Juergen Hetzel, M.D.    +491622919339    juergen.hetzel@med.uni-tuebingen.de   
Sub-Investigator: Bijoy N Atique, M.D.         
Principal Investigator: Juergen Hetzel, M.D.         
Sub-Investigator: Maik Haentschel, M.D.         
Sponsors and Collaborators
University Hospital Tuebingen
Max-Planck-Institute Tuebingen
Investigators
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Study Chair: Bernhard Schoelkopf, PhD Max-Planck-Institute, Tuebingen, Germany
Principal Investigator: Juergen Hetzel, MD University Hospital of Tuebingen, Tuebingen, Germany
More Information
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Responsible Party: University Hospital Tuebingen
ClinicalTrials.gov Identifier: NCT04828915    
Other Study ID Numbers: TEDDI
First Posted: April 2, 2021    Key Record Dates
Last Update Posted: April 2, 2021
Last Verified: January 2021
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by University Hospital Tuebingen:
Machine learning
Artificial intelligence
Clinical course
Additional relevant MeSH terms:
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COVID-19
Clinical Deterioration
Respiratory Tract Infections
Infections
Pneumonia, Viral
Pneumonia
Virus Diseases
Coronavirus Infections
 Coronaviridae Infections
Nidovirales Infections
RNA Virus Infections
Lung Diseases
Respiratory Tract Diseases
Disease Progression
Disease Attributes
Pathologic Processes