DOACT Algorithm Versus AI-Based Decision Models in Oral Anticoagulant Therapy for Vascular Patients

ITALO EUGENIO SOUZA GADELHA DE ABREU

Status: Completed

Conditions: Artificial Intelligence; Superficial Thrombophlebitis; Clinical Decision Support Systems; Deep Vein Thrombosis; Pulmonary Thromboembolisms

Treatments: Other: LLM-based tools; Other: No algorithm; Other: DOACT algorithm

Study type: Interventional

Funder types: Other

Identifiers

NCT07290608

DOACT-AI-VASC Study

Details and patient eligibility

About

Study using a decision algorithm for the application of an oral anticoagulant calculator in vascular diseases, aimed at validating a clinical decision-support tool for conditions such as deep vein thrombosis, superficial thrombophlebitis, and pulmonary thromboembolism.

Full description

Cross-sectional, three-arm comparative validation study evaluating the accuracy and clinical utility of the DOACT algorithm versus standard clinical decision-making and large language model (LLM)-based decision tools.

Enrollment: 59 patients

Sex: All

Ages: 18 to 89 years old

Volunteers: Accepts Healthy Volunteers

Inclusion and exclusion criteria

Inclusion Criteria

• Physicians with residency training in Vascular Surgery or official Board Certification in Vascular Surgery.
• Currently practicing clinical and/or surgical vascular care in Brazil.
• Completed the informed consent process (TCLE) and voluntarily agreed to participate.

Exclusion Criteria

• Physicians without formal Vascular Surgery residency and without Board Certification.
• Physicians not performing vascular clinical or surgical care (e.g., exclusively administrative, academic, or non-assistance roles).
• Less than 1 year of professional experience after medical school graduation.
• Did not sign or did not fully complete the TCLE.

Large Language Models (LLMs)

• Inclusion Criteria
• Free-access LLMs available to the public at the time of data collection.
• All responses generated using the same standardized prompt.
• Capable of producing complete, text-based clinical answers relevant to vascular surgery decision-making.

Exclusion Criteria

• Paid or subscription-based LLMs.
• LLMs requiring institutional licenses, restricted access, or proprietary tokens.
• Models unable to generate full responses to the standardized prompt.

Trial design

Primary purpose: Supportive Care

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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59 participants in 3 patient groups, including a placebo group

DOACT algorithm

Experimental group

Description:

Use of DOACT algorithm (Dose-Oriented Anticoagulant Calculator for Evidence-Based Decision Tool) to recommend appropriate oral anticoagulant regimens.

Treatment:

Other: DOACT algorithm

No algorithm

Placebo Comparator group

Description:

Standard clinical decision-making to recommend appropriate oral anticoagulant regimens.

Treatment:

Other: No algorithm

LLM-based tools

Active Comparator group

Description:

Use of large language model (LLM)-based tools to recommend appropriate oral anticoagulant regimens.

Treatment:

Other: LLM-based tools