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Establishment of clinical prediction models using machine learning techniques and predictive factors to predict adverse drug events in patients treated with dolutegravir-based anti-retroviral agents

Establishment of clinical prediction models using machine learning techniques and predictive factors to predict adverse drug events in patients treated with dolutegravir-based anti-retroviral agents

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
TCTR
Registry ID
TCTR20230904001
Enrollment
840
Registered
2023-09-04
Start date
2023-05-15
Completion date
Unknown
Last updated
2026-08-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

HIV-infected patients under a DTG-based regimen adverse drug events, dolutegravir, machine learning, prediction model, HIVs

Interventions

patient having ADR,patient without ADR
Treatment,Treatment
ADRs group,No-ADRs group

Sponsors

Pakchongnana Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.HIV-infected patients under a DTG-based regimen. 2. Age more than 18. 3. Thai patient

Exclusion criteria

Exclusion criteria: patient with head trauma with residual effects or severe cognitive impairment.

Design outcomes

Primary

MeasureTime frame
Adverse drug events each 3 month specific for each ADRs—

Secondary

MeasureTime frame
Efficacy of antiretroviral 1 year CD4 and viral load—

Countries

Thailand

Contacts

Public ContactSutthipun Suriya

Mahidol University

Suriya.sutthipun@gmail.com0909328189

Outcome results

None listed

Source: TCTR (via WHO ICTRP) · Data processed: Aug 9, 2026