Covid19COVID-19NCT04828915
Early Detection of Clinical Deterioration in Patients With COVID-19 Using Machine Learning
An observational study of Covid19, run by University Hospital Tuebingen at 1 site in Germany.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Covid19
- What is being tested?
- Machine learning and Machine based evaluation
- How many people
- 1,000 participants
- Where
- 1 siteGermany
- Study dates
- 1 Feb 2021to 31 Dec 2021
- How long will I take part?
- 6 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
Study population: Patients with detection of SARS-CoV2
Who may be able to join
- Written informed consent
- Age >= 18 years
- Detection of SARS-CoV2 within the past 5 days
Who cannot join
- Inability to measure vital parameters and document symptoms
Trial locations and contacts
1 site in 1 country.
Germany1 site, 1 recruiting
- Recruiting
University Hospital of Tuebingen
Tübingen, 72076
Annika Buchholz, Ph.D., +4915151819576, annika.buchholz@tuebingen.mpg.de
Juergen Hetzel, M.D., +491622919339, juergen.hetzel@med.uni-tuebingen.de
About this study
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
- Being tested
- Machine learning, Machine based evaluation
- Keywords
- Machine learning, Artificial intelligence, Clinical course
- Medical subject headings
- COVID-19, Disease Progression
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine learningOther
Machine learning on vital parameters, clinical symptoms and underlying diseases
Machine based evaluationOther
Quantification of the prediction power and identification of the most relevant predictive parameters
| Study group | Role | What they receive |
|---|---|---|
| Training cohort | Randomly selection of 80% of the study population. The machine learning algorithm is trained on this dataset Receives: 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. Receives: Other: Machine based evaluation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Probability of Participants for Hospitalisation or Fatal Outcome
Measured: 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
Measured: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Probability of Participants for Fatal Outcome
Measured: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Prediction of persisting health impairment by using standardized questionnaires
Measured: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Detection of symptoms, vital parameters and comorbidities predicting clinical course
Measured: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Influence of size of training data set
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Probability of Participants for hospitalisation
Measured: 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
Measured: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Apr 2021
- Study start1 Feb 2021
- Primary completion31 Jul 2021 (estimated)
- Study completion31 Dec 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Covid19Studies of the same condition, open and under way.
- More trials run by University Hospital TuebingenThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
- Target duration
- 6 Months
Sponsor and investigators
- Lead sponsor
- University Hospital Tuebingen
- Sponsor type
- University, hospital or other
- Collaborators
- Max-Planck-Institute Tuebingen
- Responsible party
- Sponsor
- Investigators
- Bernhard Schoelkopf, PhD, Study chair, Max-Planck-Institute, Tuebingen, GermanyJuergen Hetzel, MD, Principal investigator, University Hospital of Tuebingen, Tuebingen, Germany
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04828915
- Sponsor's protocol ID
- TEDDI
- Acronym
- COVID-19
- Status last verified
- Jan 2021
- First submitted
- 24 Mar 2021
- Last updated
- 2 Apr 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 2 Apr 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Early Detection of Clinical Deterioration in Patients With COVID-19 Using Machine Learning
- ClinicalTrials.gov ID
- NCT04828915
- Acronym / sponsor ID
- COVID-19 / TEDDI
- Conditions
- Covid19
- Keywords
- Machine learning, Artificial intelligence, Clinical course
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- University Hospital TuebingenUniversity, hospital or other
- Collaborators
- Max-Planck-Institute Tuebingen
- Study start
- 1 Feb 2021 (actual)
- Primary completion
- 31 Jul 2021 (estimated)
- Study completion
- 31 Dec 2021 (estimated)
- First posted
- 2 Apr 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 6 Months
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Training cohort | – | Other: Machine learningRandomly selection of 80% of the study population. The machine learning algorithm is trained on this dataset |
| Validation cohort | – | Other: Machine based evaluationRandomly 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients with detection of SARS-CoV2
Key inclusion criteria (3 listed)
- Written informed consent
- Age >= 18 years
- Detection of SARS-CoV2 within the past 5 days
Key exclusion criteria (1 listed)
- Inability to measure vital parameters and document symptoms
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Machine learningMachine learning on vital parameters, clinical symptoms and underlying diseases | Other | Training cohort |
| Machine based evaluationQuantification of the prediction power and identification of the most relevant predictive parameters | Other | Validation cohort |
Primary outcome
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
11 secondary outcomes
Probability of Participants for Intensive Care Unit Admission
Time frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Probability of Participants for Fatal Outcome
Time frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
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
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
Influence of size of training data set
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Probability of Participants for hospitalisation
Time frame: 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
Time frame: Detection of SARS-CoV2 to recovery, hospitalisation or fatal outcome up to 5 weeks
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Apr 2021.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Bernhard Schoelkopf, PhD study chair
Max-Planck-Institute, Tuebingen, Germany
Juergen Hetzel, MD principal investigator
University Hospital of Tuebingen, Tuebingen, Germany
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Covid19, run by University Hospital Tuebingen at 1 site in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 2 Apr 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Annika Buchholz, PhD (+49 151 51819576, annika.buchholz@tuebingen.mpg.de), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Apr 2021.