NCT04825301

Brief Summary

The aim of the study is to develop a prognostic prediction model based on machine learning algorithms in patients affected by coronavirus disease 2019 (COVID-19), the prediction model will be capable to recognize patient with favorable prognosis or patient with poor prognosis by intelligent systems data analysis.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
779

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Feb 2020

Typical duration for all trials

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

February 27, 2020

Completed
1.1 years until next milestone

First Submitted

Initial submission to the registry

March 29, 2021

Completed
3 days until next milestone

First Posted

Study publicly available on registry

April 1, 2021

Completed
12 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

March 30, 2022

Completed
1 month until next milestone

Study Completion

Last participant's last visit for all outcomes

April 30, 2022

Completed
Last Updated

February 24, 2022

Status Verified

September 1, 2021

Enrollment Period

2.1 years

First QC Date

March 29, 2021

Last Update Submit

February 7, 2022

Conditions

Keywords

Covid19machine learningprediction modelconvolutional neural network

Outcome Measures

Primary Outcomes (1)

  • COVID-19 clinical course

    Data about sex, age, symptoms start date, symptoms, comorbidity, vital parameters, hematochemical blood tests, therapy, oxygen support, radiology, clinical disease progression will be collected. The collected data will be analyzed through a machine learning based approach to predict the prognosis of patients affected by COVID-19.

    2 months

Secondary Outcomes (1)

  • Application of machine learning algorithms on data of patients affected by COVID-19

    2 months

Study Arms (2)

training cohort

data collection

validation cohort

data collection

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodProbability Sample
Study Population

Italian caucasian patients aged over 18 years old SARS-CoV-2 infection confirmed by PCR

You may qualify if:

  • patients aged over 18 positive for COVID-19 by polymerase chain reaction assay for rhino-pharyngeal swab

You may not qualify if:

  • Under 18 aged

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

University of L'Aquila

L’Aquila, 67100, Italy

RECRUITING

MeSH Terms

Conditions

COVID-19

Condition Hierarchy (Ancestors)

Pneumonia, ViralPneumoniaRespiratory Tract InfectionsInfectionsVirus DiseasesCoronavirus InfectionsCoronaviridae InfectionsNidovirales InfectionsRNA Virus InfectionsLung DiseasesRespiratory Tract Diseases

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Full Professor

Study Record Dates

First Submitted

March 29, 2021

First Posted

April 1, 2021

Study Start

February 27, 2020

Primary Completion

March 30, 2022

Study Completion

April 30, 2022

Last Updated

February 24, 2022

Record last verified: 2021-09

Locations