SENSING-AI in Patients With Long COVID (SENSING-AI)

NCT06247332 | Completed

• 1 other identifier: SEN-0121
• Study Type: observational
• Target: 103
• Locations: 1 country
• Sites: 1

Brief Summary

The retrospective study will be used to develop an artificial intelligence model of risk stratification of physiological and psychological complications arising from the information available in the electronic medical record and first consultation report to support patients and healthcare professionals in better managing the healthcare process for patients diagnosed with long COVID.

Conditions

Post-acute COVID-19 Syndrome

Keywords

Long COVID-19; Retrospective study; Artificial Intelligence; Mental Health

Outcome Measures

Primary Outcomes (1)

• Retrospective SENSING-AI cohort

  The retrospective SENSING-AI cohort will be fed from clinical information of 100 cases of patients with long COVID-19.

  1 month

Study Arms (1)

Retrospective Long COVID cases

The target population will be as balanced as possible between subjects who needed specialized care due to long COVID-19 complications (either specialized care consultations or any non-planned hospital admission) at 1 month, 3 months, 6 months and 1 year from the long COVID-19 diagnose and those who did not require such specialized care.

Other: Review of available clinical data sources related to use cases

Interventions

Review of available clinical data sources related to use cases OTHER

There will be a review of available clinical data sources related to use cases. In addition, this information will be complemented by a cohort of anonymized retrospective data of 100 cases obtained from the clinical information resulting from the assistance to COVID-19 patients managed by the Primary Care Health District of Sevilla Norte and the Infectious Diseases Department of the Virgen Macarena University Hospital

Retrospective Long COVID cases

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The sample size for the retrospective study enough to generate a first version of the risk stratification models will be around 100 cases. The target population will be as balanced as possible between subjects who needed specialized care due to long COVID-19 complications (either specialized care consultations or any non-planned hospital admission) at 1 month, 3 months, 6 months and 1 year from the long COVID-19 diagnose and those who did not require such specialized care.

You may qualify if:

• Legal adult
• Diagnosed of long COVID-19 in the last year
• With the presence of any of these symptoms:
• Asthenia (Tiredness)
• Dyspnea
• Shortness of breath
• Anxiety
• Stress
• Depression
• Sleep disorder

You may not qualify if:

• Attended to specialized care consultation
• Was admitted in hospital in the last year due to a problem not related to the COVID complications

Sponsors & Collaborators

• Adhera Health, Inc.: lead
• Hospital Universitario Virgen Macarena: collaborator

Study Sites (1)

Virgen Macarena University Hospital

Seville, Seville, 41009, Spain

Location

MeSH Terms

Conditions

Post-Acute COVID-19 Syndrome; Psychological Well-Being

Condition Hierarchy (Ancestors)

COVID-19; Pneumonia, Viral; Pneumonia; Respiratory Tract Infections; Infections; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Lung Diseases; Respiratory Tract Diseases; Post-Infectious Disorders; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Personal Satisfaction; Behavior

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 31, 2024
• First Posted: February 7, 2024
• Study Start: January 18, 2022
• Primary Completion: February 25, 2022
• Study Completion: February 25, 2022
• Last Updated: February 18, 2025

Record last verified: 2024-02

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)