COVID-19NCT06052527
Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) and Influenza Treatment System With Machine Learning
An observational study of COVID-19 and Post-COVID-19 Syndrome, run by Lizora LLC at 1 site in China.
Trial summary
This is an open-tabled, one-arm observatory trial to assess the effectiveness and safety of the Autonomous Treatment System Based on Machine Learning in patients with Covid-19, Post-Acute Sequelae of SARS-CoV-2 infection and influenza. Read more about this study
- Who is it for?
- Ages 14 to 95 years
- Conditions the study is for
- COVID-19, Post-COVID-19 Syndrome, Influenza
- What is being tested?
- Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine Learning
- How many people
- 27 participants
- Where
- 1 siteChina
- Study dates
- 16 Jun 2023to 1 Oct 2023
- How long will I take part?
- 28 Days
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 14 to 95 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients with active SARS-Cov-2 infection within 30 days patients with Post-Acute Sequelae of SARS-CoV-2 infection and patients with influenza.
Who may be able to join
- Either male or female (14 years or older), and their COVID-19 vaccination status was not a factor for inclusion.
- Subjects with any high-risk conditions
- Subjects with positive sars-cov-2 rapid antigen results in 30 days
- Subjects with post Covid-19 syndrome
Who cannot join
- pregnant individuals
- subjects with known histories of allergic reactions to medical herbs commonly used in Traditional Chinese Medicine (TCMs)
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Autonomous Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) and Influenza Treatment System With Machine Learning in Outpatient Settings
This is an open-tabled, one-arm observatory trial to assess the effectiveness and safety of the Autonomous Treatment System Based on Machine Learning in patients with Covid-19, Post-Acute Sequelae of SARS-CoV-2 infection and influenza.
Read the detailed description
This study has enrolled 27 patients diagnosed with Covid-19, Post-Acute Sequelae of SARS-CoV-2 infection, and influenza. Of these patients, 26 are outpatients, and 1 is hospitalized. After screening based on the inclusion and exclusion criteria, eligible patients will receive prescriptions recommended by the Autonomous Treatment System Based on Machine Learning in this observational trial.
The objectives of this study are:
- To compare the classifications made by our machine learning system with those by physicians to assess the model's reliability and accuracy;
- To evaluate Covid-19-related hospitalizations or deaths from any cause through day 28;
- To determine if the machine learning system's recommended prescription alleviates symptoms of Covid-19, Post-Acute Sequelae of SARS-CoV-2 infection, and influenza;
- To monitor participants who tested positive for the Covid-19 for 28 days after initiating treatment, looking for potential rebound cases.
Participants will use an online application to receive the recommended prescription results and will forward these results to a physician for verification. Patients are instructed to complete the online analysis every 3 days or whenever their symptoms change, whichever comes first. They are also asked to adhere to the prescribed medication regimen. Research physicians will conduct follow-ups with patients every 3 days via phone calls. The potential treatments patients may receive include any of the following Traditional Chinese Medicine formulas: LizCovidCure-1, LizCovidCure-2, LizCovidCure-3, LizCovidCure-4, and LizCovid-5.
- Conditions
- COVID-19, Post-COVID-19 Syndrome, Influenza
- Being tested
- Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine Learning
- Medical subject headings
- COVID-19, Post-Acute COVID-19 Syndrome, Influenza, Human
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine LearningOther
A novel treatment recommendation system for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza, which is based on machine learning
| Study group | Role | What they receive |
|---|---|---|
| Active Covid-19 Infection | Patients with positive SARS-CoV-2 rapid antigen test results within 60 days before the start of the study will be administered pre-defined TCM prescriptions recommended by the Autonomous Treatment System based on machine learning Receives: Other: Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine Learning | |
| Post-Covid-19 Syndrome | Patients with positive Covid-19 antigen test results obtained more than 60 days before the start of the study will be administered pre-defined TCM prescriptions recommended by the Autonomous Treatment System based on machine learning. Receives: Other: Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine Learning | |
| Influenza | Patients with negative SARS-CoV-2 rapid antigen test results and who are diagnosed with influenza will be administered pre-defined TCM prescriptions recommended by the Autonomous Treatment System based on machine learning. Receives: Other: Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine Learning |
What is being measured
The primary outcome is the main question the study is designed to answer.
Classification Accuracy
compare the classifications made by our machine learning system with those by physicians, to assess the model's reliability
Measured: 1 Day
Secondary outcomes (1)
Hospitalization Rate and Death
we assess Covid-19-related hospitalization or death from any cause through day 28
Measured: 28 Days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Sep 2023
- Study start16 Jun 2023
- Primary completion30 Sep 2023
- Study completion1 Oct 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for COVID-19Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 27 participants (actual)
- Target duration
- 28 Days
Sponsor and investigators
- Lead sponsor
- Lizora LLC
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Sheng'ai Traditional Chinese Medicine Hospital
- Responsible party
- Sponsor
- Investigators
- jiale xian, MHA, Study chair, Lizora LLC
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06052527
- Sponsor's protocol ID
- Liz2023Covid19
- Status last verified
- Dec 2023
- First submitted
- 21 Sep 2023
- Last updated
- 29 Dec 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: No plan to share the individual participant data (IPD).
Study at a glance
From the registry record, last updated by the sponsor on 29 Dec 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Autonomous Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) and Influenza Treatment System With Machine Learning in Outpatient Settings
- ClinicalTrials.gov ID
- NCT06052527
- Acronym / sponsor ID
- Liz2023Covid19
- Conditions
- COVID-19, Post-COVID-19 Syndrome, Influenza
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 27 participants (actual)
- Lead sponsor
- Lizora LLCIndustry (pharma, biotech, devices)
- Collaborators
- Sheng'ai Traditional Chinese Medicine Hospital
- Study start
- 16 Jun 2023 (actual)
- Primary completion
- 30 Sep 2023 (actual)
- Study completion
- 1 Oct 2023 (actual)
- First posted
- 25 Sep 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This is an open-tabled, one-arm observatory trial to assess the effectiveness and safety of the Autonomous Treatment System Based on Machine Learning in patients with Covid-19, Post-Acute Sequelae of SARS-CoV-2 infection and influenza.
Detailed description
This study has enrolled 27 patients diagnosed with Covid-19, Post-Acute Sequelae of SARS-CoV-2 infection, and influenza. Of these patients, 26 are outpatients, and 1 is hospitalized. After screening based on the inclusion and exclusion criteria, eligible patients will receive prescriptions recommended by the Autonomous Treatment System Based on Machine Learning in this observational trial.
The objectives of this study are:
- To compare the classifications made by our machine learning system with those by physicians to assess the model's reliability and accuracy;
- To evaluate Covid-19-related hospitalizations or deaths from any cause through day 28;
- To determine if the machine learning system's recommended prescription alleviates symptoms of Covid-19, Post-Acute Sequelae of SARS-CoV-2 infection, and influenza;
- To monitor participants who tested positive for the Covid-19 for 28 days after initiating treatment, looking for potential rebound cases.
Participants will use an online application to receive the recommended prescription results and will forward these results to a physician for verification. Patients are instructed to complete the online analysis every 3 days or whenever their symptoms change, whichever comes first. They are also asked to adhere to the prescribed medication regimen. Research physicians will conduct follow-ups with patients every 3 days via phone calls. The potential treatments patients may receive include any of the following Traditional Chinese Medicine formulas: LizCovidCure-1, LizCovidCure-2, LizCovidCure-3, LizCovidCure-4, and LizCovid-5.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 28 Days
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Active Covid-19 Infection | – | Other: Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine LearningPatients with positive SARS-CoV-2 rapid antigen test results within 60 days before the start of the study will be administered pre-defined TCM prescriptions recommended by the Autonomous Treatment System based on machine learning |
| Post-Covid-19 Syndrome | – | Other: Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine LearningPatients with positive Covid-19 antigen test results obtained more than 60 days before the start of the study will be administered pre-defined TCM prescriptions recommended by the Autonomous Treatment System based on machine learning. |
| Influenza | – | Other: Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine LearningPatients with negative SARS-CoV-2 rapid antigen test results and who are diagnosed with influenza will be administered pre-defined TCM prescriptions recommended by the Autonomous Treatment System based on machine learning. |
Who may qualify
- Ages
- 14 to 95 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with active SARS-Cov-2 infection within 30 days patients with Post-Acute Sequelae of SARS-CoV-2 infection and patients with influenza.
Key inclusion criteria (4 listed)
- Either male or female (14 years or older), and their COVID-19 vaccination status was not a factor for inclusion.
- Subjects with any high-risk conditions
- Subjects with positive sars-cov-2 rapid antigen results in 30 days
- Subjects with post Covid-19 syndrome
Key exclusion criteria (2 listed)
- pregnant individuals
- subjects with known histories of allergic reactions to medical herbs commonly used in Traditional Chinese Medicine (TCMs)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine LearningA novel treatment recommendation system for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza, which is based on machine learning | Other | Active Covid-19 Infection, Influenza, Post-Covid-19 Syndrome |
Primary outcome
Classification Accuracy
Time frame: 1 Day
compare the classifications made by our machine learning system with those by physicians, to assess the model's reliability
1 secondary outcome
Hospitalization Rate and Death
Time frame: 28 Days
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Dec 2023.
- Individual participant dataSharing planned: No. No plan to share the individual participant data (IPD).
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
jiale xian, MHA study chair
Lizora LLC
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of COVID-19 and Post-COVID-19 Syndrome, run by Lizora LLC at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 29 Dec 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Classification Accuracy (time frame: 1 Day). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Dec 2023.