CClinicalTrials.gg
Status unknownNCT05629793DICOPERIAUpdated Nov 29, 2022

Differential Diagnosis of Persistent COVID-19 by Artificial Intelligence

An interventional study of Experimental tests in COVID-19, Fatigue and Distress Respiratory Syndrome, sponsored by Fundacin Biomedica Galicia Sur. Status unknown at 7 sites in Spain. Open to participants aged 18 Years to 70 Years. Per ClinicalTrials.gov, last updated 2022-11-29.

Sponsored by Fundacin Biomedica Galicia Sur · Not applicable, Interventional, and Treatment

The sponsor has not verified this record recently (last verified Nov 2022), so the status shown — last known as Not yet recruiting — may be out of date.
Phase
Not applicable
Study type
Interventional
Enrollment
136
Allocation
Non-randomized
Ages
18 Years to 70 Years
Sex
All
01

Study 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.

Read the 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.

02

Conditions studied

  • 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
03

In context

COVID-19

7,640 studies on the registry are indexed under COVID-19; 488 are open to participants now.

This study's planned enrollment of 136 is above the median of 100 across 4,099 interventional studies indexed under COVID-19.

Browse COVID-19 studies →

Lead sponsor

Fundacin Biomedica Galicia Sur is the lead sponsor of 27 studies on the registry; 7 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years to 70 Years
Sexes eligible
All
Accepts healthy volunteers
No

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.
05

Study design

Phase
Not applicable
Primary purpose
Treatment
Allocation
Non-randomized
Intervention model
Parallel assignment
Masking
Single (Investigator)
Enrollment
136 participants (estimated)

Study arms

  • Experimental
    Persistent COVID group

    Patients with persistent COVID will be recruited by the physicians of the Post COVID-19 Multidisciplinary Clinic of the Complexo Hospitalario Universitario de Ourense.

    Other: Experimental tests

  • Experimental
    Recovered COVID group

    The controls will be recruited in a matched manner with the clinical sample in age, sex, epidemic wave and vaccination status, from among previously COVID-positive patients cured without sequelae and attended in Primary Care in the Health Centers of A Cuña, Valle Inclán and Novoa Santos in Ourense.

    Other: Experimental tests

Interventions

  • OtherExperimental tests

    Walking for 6 minutes, sitting down and getting up from a chair for 1 minute and finally the cold test (Cold pressor) where the hand is introduced for 1 minute in water at 4ºC. The patient will be monitored by means of a Polar H10 chest strap, as used in sports, continuously and 02 saturation, TA and voice (exhalation while saying /a/ and dry cough) will be collected before and after the tests. Finally, skin conductance will be monitored by performing baseline tracing and then control while performing the cold test.

06

What researchers measure

Primary outcomes

  1. 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.

    Through an algorithm model created by Machine Learning that will be trained using cardic variability (HRV), skin conductance and acoustic analysis data.

    Time frame: 8 weeks

Secondary outcomes

  1. Cardiac variability

    Number of times a contraction of the heart occurs in one minute, expressed in beats per minute, by means of a Polar chest strap, model H10. A baseline recording of 5 minutes duration will be taken, with the patient in a seated position. At the end of each test, recording is continued for 2 minutes to demonstrate the speed and degree of recovery after stress.

    Time frame: 8 weeks

  2. Voice recording

    Sounds produced by the patient at rest and after having performed the stress tests. The patient will be asked to take a deep breath and then pronounce the vowel /a/ in a sustained manner, in a comfortable tone and volume (3 times).

    Time frame: 8 weeks

  3. Skin conductance

    micro Siemens \[µS\]. By means of the Bitalino electrodermal activity recording system. It will be recorded at rest, during the Cold Pressor test and once it is finished, for at least two minutes, to assess the normalization of the conductivity curve.

    Time frame: 8 weeks

  4. 6MWT

    metres/min. The patient will walk the maximum distance they can in 6 minutes.

    Time frame: 8 weeks

  5. 1minSTST

    Number of repetitions performed after sitting down and getting up from a chair without supporting the hands as many times as possible for 1 minute..

    Time frame: 8 weeks

  6. Cold Pressor test

    One hand is inserted into a container with water at 4-5ºC for 1 minute. Before and after the test, HRV, BP, and thermal conductance are recorded for 5 and 2 minutes -respectively- while lying supine.

    Time frame: 8 weeks

Other outcomes

  1. Age

    Years

    Time frame: 8 weeks

  2. Sex

    Male, Female

    Time frame: 8 weeks

  3. Current treatment

    Treatment taken by the patient at the time of the study.

    Time frame: 8 weeks

  4. Date of PCR + SARS-CoV-2

    DD-MMM-YYYY

    Time frame: 8 weeks

  5. Epidemic wave

    Of the 7 waves of COVID-19 that have occurred in Spain, a description will be given of the wave to which the infection of each patient included belonged. First, second, third, fourth, fifth, sixth, seventh, eighth, ninth or tenth wave.

    Time frame: 8 weeks

  6. Vaccination status at the time of infection

    Number of vaccines doses at the time of infection

    Time frame: 8 weeks

  7. FVC

    Is the maximum volume of air exhaled, with the maximum possible effort, starting from a maximum inspiration in ml.

    Time frame: 8 weeks

  8. FEV1

    The volume of air expelled during the first second of forced expiration in ml.

    Time frame: 8 weeks

  9. FEV1/ FVC

    Expressed as a percentage (%), it indicates the proportion of the FVC that is expelled during the first second of the forced expiratory maneuver.

    Time frame: 8 weeks

  10. CO diffusion test

    To evaluate the transfer of oxygen from the alveolar space to the hemoglobin of the erythrocytes contained in the pulmonary capillaries. Effective alveolar-capillary area available for gas transfer in the lung. (%)

    Time frame: 8 weeks

07

Study locations

7 sites
  • Complexo Hospitalario Universitario de Ourense
    Ourense, 32002, Spain
    • Alejandro García Caballero, MD · Contact · alejandro.alberto.garcia.caballero@sergas.es
    • Alejandro García Caballero, MD · Principal investigator
    • María Bustillo Casado, MD · Sub investigator
    • María Dolores Díaz López, MD · Sub investigator
    • Pablo López Mato, MD · Sub investigator
    • Luis Docasar Bertolo, MD · Sub investigator
    • Beatriz Gómez Gómez, MD · Sub investigator
  • Health Center Novoa Santos
    Ourense, 32003, Spain
  • Health Center Valle Inclán
    Ourense, 32004, Spain
  • S.S. Computer Engineering (University of Vigo)
    Ourense, 32004, Spain
  • Health Center A Cuña
    Ourense, 32005, Spain
    • Carlos Menéndez Villalva · Contact
  • Galicia Sur Health Research Institute (IISGS) - Hospital Álvaro Cunqueiro
    Vigo, 36213, Spain
  • School of Telecommunication Engineering (University of Vigo)
    Vigo, 36310, Spain
08

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Nov 29, 2022, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
09

Registry details

Key details

Study ID
NCT05629793
Lead sponsor
Fundacin Biomedica Galicia Sur
Collaborators
University of Vigo, Galician South Health Research Institute
Responsible party
Sponsor
First posted
Nov 29, 2022
Start date
Dec 14, 2022 (estimated)
Primary completion
Nov 2023 (estimated)
Completion
Nov 2023 (estimated)
Last update
Nov 29, 2022

Study contacts

Alejandro García Caballero, MD
Contact
alejandro.alberto.garcia.caballero@sergas.es
988 38 55 00

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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