Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) and Influenza Treatment System With Machine Learning

Lizora

Status: Completed

Conditions: COVID-19; Influenza; Post-COVID-19 Syndrome

Treatments: Other: Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine Learning

Study type: Observational

Funder types: Industry; Other

Identifiers

NCT06052527

Liz2023Covid19

Details and patient eligibility

About

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.

Full 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:

1. To compare the classifications made by our machine learning system with those by physicians to assess the model's reliability and accuracy;
2. To evaluate Covid-19-related hospitalizations or deaths from any cause through day 28;
3. 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;
4. 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.

Enrollment: 27 patients

Sex: All

Ages: 14 to 95 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• 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

Exclusion criteria

• pregnant individuals
• subjects with known histories of allergic reactions to medical herbs commonly used in Traditional Chinese Medicine (TCMs)

Trial design

27 participants in 3 patient groups

Active Covid-19 Infection

Description:

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

Treatment:

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

Description:

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.

Treatment:

Other: Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine Learning

Influenza

Description:

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.

Treatment:

Other: Autonomous Treatment System for Covid-19, Post-Acute Sequelae of SARS-CoV-2 Infection and Influenza Based on Machine Learning