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

NCT04828915 | Unknown

• 1 other identifier: TEDDI
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Covid19

Keywords

Machine learning; Artificial intelligence; Clinical course

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (11)

• 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

Study Arms (2)

Training cohort

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

Other: Machine learning

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

Interventions

Machine learning OTHER

Machine learning on vital parameters, clinical symptoms and underlying diseases

Training cohort

Machine based evaluation OTHER

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

Validation cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients with detection of SARS-CoV2

You may qualify if:

• Written informed consent
• Age \>= 18 years
• Detection of SARS-CoV2 within the past 5 days

You may not qualify if:

• Inability to measure vital parameters and document symptoms

Sponsors & Collaborators

• University Hospital Tuebingen: lead
• Max-Planck-Institute Tuebingen: collaborator

Study Sites (1)

University Hospital of Tuebingen

Tübingen, 72076, Germany

RECRUITING

MeSH Terms

Conditions

COVID-19; Disease Progression

Interventions

Machine Learning

Condition Hierarchy (Ancestors)

Pneumonia, Viral; Pneumonia; Respiratory Tract Infections; Infections; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Lung Diseases; Respiratory Tract Diseases; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Artificial Intelligence; Algorithms; Mathematical Concepts

Study Officials

• Bernhard Schoelkopf, PhD

  Max-Planck-Institute, Tuebingen, Germany

  STUDY CHAIR

• Juergen Hetzel, MD

  University Hospital of Tuebingen, Tuebingen, Germany

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Annika Buchholz, PhD

CONTACT

+49 151 51819576; annika.buchholz@tuebingen.mpg.de

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 6 Months
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 24, 2021
• First Posted: April 2, 2021
• Study Start: February 1, 2021
• Primary Completion: July 31, 2021
• Study Completion: December 31, 2021
• Last Updated: April 2, 2021

Record last verified: 2021-01

Data Sharing

• IPD Sharing: Will not share

Locations

DE (1)