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

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04828915

Acronym

COVID-19

Enrollment

1000

Registered

2021-04-02

Start date

2021-02-01

Completion date

2021-12-31

Last updated

2021-04-02

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Covid19

Keywords

Machine learning, Artificial intelligence, Clinical course

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.

Interventions

OTHERMachine learning

Machine learning on vital parameters, clinical symptoms and underlying diseases

OTHERMachine based evaluation

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

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

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

Design outcomes

Primary

Measure	Time frame
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

Measure	Time frame
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
Probability of Participants for Intensive Care Unit Admission	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
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

Countries

Germany

Contacts

Primary ContactAnnika Buchholz, PhD

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

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026