Clinical trial NCT05629793: Differential Diagnosis of Persistent COVID-19 by Artificial Intelligence
Official title: Differential Diagnosis of Persistent COVID-19 by Artificial Intelligence
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: COVID-19
Fatigue
Distress Respiratory Syndrome
Cognitive Dysfunction
COVID-19 Recurrent
SARS CoV 2 Infection
Keywords: Machine learning
Stress test
Cardiac variability
Voice recording
Skin conductance
Lead sponsor: Fundacin Biomedica Galicia Sur
Sponsor class: OTHER
Collaborators: name: University of Vigo
name: Galician South Health Research Institute
First posted: 2022-11-29
Enrollment: 136
Sex: ALL
Minimum age (years): 18
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Persistent COVID group vs. Recovered COVID group
Primary purpose: TREATMENT

Brief summary: The pandemic caused by SARS-CoV-2 infection has resulted, in addition to the well-known acute symptoms, in the emergence of persistent, diffuse and heterogeneous symptoms referred to as persistent COVID.

Common symptoms include fatigue, shortness of breath, and cognitive dysfunction, among others, and result in an impact on daily functioning. Symptoms may be new onset, appear after initial recovery from an acute episode of COVID-19, or persist after the initial illness. Cardiac variability (HRV) was initially used in COVID-19 to predict mortality in the acute setting. Dysautonomia which partly evaluates HRV is frequent in patients with persistent COVID. Several groups have used voice or other respiratory noise analysis for the diagnosis of acute COVID.

Patients in the persistent COVID cohort will be able to be differentiated from an age, sex and vaccination status matched cohort of recovered COVID patients without sequelae by means of a model created by Machine Learning that will be trained using cardiac variability (HRV), skin conductance and acoustic analysis data. The primary objetive will be to obtain a classification algorithm by Machine Learning to differentiate the group of patients with persistent COVID diagnosis from the paired group of recovered COVID patients without sequelae.

Detailed description: This is a validation study of a Machine Learning algorithm for the diagnosis of persistent COVID using clinical diagnosis as the "gold standard". The sample will be composed of post-COVID patients, one group of which developed persistent COVID and another paired with the previous one with cured COVID without sequelae.

Eligibility criteria: Persistent COVID group:

Inclusion Criteria:

* Age ≥18 and ≤70 years of age
* Confirmed infection (PCR) with SARS- CoV-2 until 03/28/2022 and thereafter date.
* Symptoms include: fatigue, respiratory distress or cognitive dysfunction, among others.
* Symptoms persist or appear more than 3 months after onset of infection.
* Symptoms last longer than 2 months and are not better explained by another diagnosis.
* Symptoms appeared after initial recovery or persisted since disease debut.
* Symptoms may fluctuate or remit over time.
* Patients have capacity to consent and agree to participate in the study.

Exclusion Criteria:

* Active COVID-19 infection.
* Cardiac arrhythmia, pacemaker carrier.
* Other pathologies with dysautonomia.
* Raynaud's phenomenon.
* Other diseases that may affect exercise capacity or be aggravated by exercise shall also be excluded, such as: Uncontrolled heart failure, severe or symptomatic aortic stenosis, pulmonary edema, acute respiratory failure, recent pulmonary thromboembolism, lower limb thrombosis, infections, thyrotoxicosis, or orthopedic inability to walk.

Recovery COVID group

Inclusion Criteria:

* Age ≥18 and ≤70 years of age
* Confirmed infection (PCR) with SARS- CoV-2 until 03/28/2022 and thereafter date.
* Full functional recovery.
* Follow-up by Primary Care.
* They have not presented three months after the onset of the disease: fatigue, respiratory distress or cognitive dysfunction, among others.
* Patients have capacity to consent and agree to participate in the study.

Exclusion Criteria:

* Active COVID-19 infection.
* Cardiac arrhythmia, pacemaker carrier.
* Other pathologies with dysautonomia.
* Raynaud's phenomenon.
* Other diseases that may affect exercise capacity or be aggravated by exercise such as: uncontrolled heart failure, severe or symptomatic aortic stenosis, pulmonary edema, acute respiratory failure, recent pulmonary thromboembolism, lower limb thrombosis, infections, thyrotoxicosis, or orthopedic inability to walk shall also be excluded.

Primary outcomes:
measure: Differences of the group of patients with a persistent diagnosis of COVID from the age-matched group, sex and vaccination status of patients recovered from COVID without sequelae.
timeFrame: 8 weeks
Secondary outcomes:
measure: Cardiac variability
timeFrame: 8 weeks
measure: Voice recording
timeFrame: 8 weeks
measure: Skin conductance
timeFrame: 8 weeks
measure: 6MWT
timeFrame: 8 weeks
measure: 1minSTST
timeFrame: 8 weeks
measure: Cold Pressor test
timeFrame: 8 weeks
Other outcomes:
measure: Age
timeFrame: 8 weeks
measure: Sex
timeFrame: 8 weeks
measure: Current treatment
timeFrame: 8 weeks
measure: Date of PCR + SARS-CoV-2
timeFrame: 8 weeks
measure: Epidemic wave
timeFrame: 8 weeks
measure: Vaccination status at the time of infection
timeFrame: 8 weeks
measure: FVC
timeFrame: 8 weeks
measure: FEV1
timeFrame: 8 weeks
measure: FEV1/ FVC
timeFrame: 8 weeks
measure: CO diffusion test
timeFrame: 8 weeks
Locations:
city: Ourense
country: Spain
facility: Complexo Hospitalario Universitario de Ourense
city: Ourense
country: Spain
facility: Health Center Novoa Santos
city: Ourense
country: Spain
facility: Health Center Valle Inclán
city: Ourense
country: Spain
facility: S.S. Computer Engineering (University of Vigo)
city: Ourense
country: Spain
facility: Health Center A Cuña
city: Vigo
country: Spain
facility: Galicia Sur Health Research Institute (IISGS) - Hospital Álvaro Cunqueiro
city: Vigo
country: Spain
facility: School of Telecommunication Engineering (University of Vigo)
Study officials:
References: