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

Study Overview
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Brief Summary
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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.

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

Conditions
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Covid19

Intervention / Treatment
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* Other: Machine learning
* Other: Machine based evaluation


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Training cohort<br>Randomly selection of 80% of the study population. The machine learning algorithm is trained on this dataset | Other: Machine learning<br>* Machine learning on vital parameters, clinical symptoms and underlying diseases<br>|
| Validation cohort<br>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<br>* Quantification of the prediction power and identification of the most relevant predictive parameters<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Machine learning, Artificial intelligence, Clinical course