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

Clinical Performance of the DOACT Algorithm Versus AI-Based Decision Models in Oral Anticoagulant Therapy for Vascular Patients

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.

Study Overview

• Status: Completed
• Conditions: Deep Vein Thrombosis; Artificial Intelligence; Clinical Decision Support Systems; Pulmonary Thromboembolisms; Superficial Thrombophlebitis
• Intervention / Treatment: Other: DOACT algorithm; Other: No algorithm; Other: LLM-based tools

Detailed 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.

• Study Type: Interventional
• Enrollment (Actual): 59
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Brazil
  • São Paulo
    • São Paulo, São Paulo, Brazil, 01.223-001
      • Irmandade Da Santa Casa De Misericordia De Sao Paulo

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Supportive Care
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Single

Number of Arms

3

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: DOACT algorithm; Use of DOACT algorithm (Dose-Oriented Anticoagulant Calculator for Evidence-Based Decision Tool) to recommend appropriate oral anticoagulant regimens.	Other: DOACT algorithm; Vascular and non-vascular physicians using DOACT (Dose-Oriented Anticoagulant Calculator for Evidence-Based Decision Tool) to recommend appropriate oral anticoagulant regimens-dose selection and duration responding 15 standardized clinical case vignettes representing patients with vascular diseases such as deep vein thrombosis (DVT), superficial thrombophlebitis, and pulmonary thromboembolism (PTE).
Placebo Comparator: No algorithm; Standard clinical decision-making to recommend appropriate oral anticoagulant regimens.	Other: No algorithm; Vascular and non-vascular physicians using standard clinical decision-making (no use of algorithm) to recommend appropriate oral anticoagulant regimens-dose selection and duration responding 15 standardized clinical case vignettes representing patients with vascular diseases such as deep vein thrombosis (DVT), superficial thrombophlebitis, and pulmonary thromboembolism (PTE).
Active Comparator: LLM-based tools; Use of large language model (LLM)-based tools to recommend appropriate oral anticoagulant regimens.	Other: LLM-based tools; Vascular and non-vascular physicians using large language model (LLM)-based tools to recommend appropriate oral anticoagulant regimens-dose selection and duration responding 15 standardized clinical case vignettes representing patients with vascular diseases such as deep vein thrombosis (DVT), superficial thrombophlebitis, and pulmonary thromboembolism (PTE).

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of the DOACT Algorithm in Guiding Oral Anticoagulant Therapy	Accuracy of anticoagulation recommendations Description: Proportion of correct responses generated by the four evaluated LLMs, vascular surgeons, and non-vascular physicians, with and without access to the DOACT algorithm, using standardized clinical vignettes.	Day 1
Accuracy of anticoagulation recommendations	Proportion of correct responses generated by LLMs, vascular surgeons, and non-vascular physicians with and without access to the DOACT algorithm. All LLM outputs will be generated using the same standardized prompt, following methodological guidance recommended by IBM for evaluating large language models.	Day 1

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
1. Identification of key clinical elements 2.Response time	Correct reporting of dosing adjustments, renal criteria, bleeding risks, reversal agents, and contraindications. Description: Time (seconds) from prompt submission to full answer generation for LLMs, and time to completion for physicians.	Day 1

Collaborators and Investigators

• Sponsor: ITALO EUGENIO SOUZA GADELHA DE ABREU

Publications and helpful links

General Publications

• Gee E. The National VTE Exemplar Centres Network response to implementation of updated NICE guidance: venous thromboembolism in over 16s: reducing the risk of hospital-acquired deep vein thrombosis or pulmonary embolism (NG89). Br J Haematol. 2019 Sep;186(5):792-793. doi: 10.1111/bjh.16010. Epub 2019 Jun 5. No abstract available.
• Vinogradova Y, Coupland C, Hill T, Hippisley-Cox J. Risks and benefits of direct oral anticoagulants versus warfarin in a real world setting: cohort study in primary care. BMJ. 2018 Jul 4;362:k2505. doi: 10.1136/bmj.k2505.
• Nielsen PB, Lundbye-Christensen S, Rasmussen LH, Larsen TB. Improvement of anticoagulant treatment using a dynamic decision support algorithm: a Danish Cohort study. Thromb Res. 2014 Mar;133(3):375-9. doi: 10.1016/j.thromres.2013.12.042. Epub 2014 Jan 7.
• Kakkos SK, Gohel M, Baekgaard N, Bauersachs R, Bellmunt-Montoya S, Black SA, Ten Cate-Hoek AJ, Elalamy I, Enzmann FK, Geroulakos G, Gottsater A, Hunt BJ, Mansilha A, Nicolaides AN, Sandset PM, Stansby G, Esvs Guidelines Committee, de Borst GJ, Bastos Goncalves F, Chakfe N, Hinchliffe R, Kolh P, Koncar I, Lindholt JS, Tulamo R, Twine CP, Vermassen F, Wanhainen A, Document Reviewers, De Maeseneer MG, Comerota AJ, Gloviczki P, Kruip MJHA, Monreal M, Prandoni P, Vega de Ceniga M. Editor's Choice - European Society for Vascular Surgery (ESVS) 2021 Clinical Practice Guidelines on the Management of Venous Thrombosis. Eur J Vasc Endovasc Surg. 2021 Jan;61(1):9-82. doi: 10.1016/j.ejvs.2020.09.023. Epub 2020 Dec 15. No abstract available.

Study record dates

Study Major Dates

• Study Start (Actual): January 20, 2025
• Primary Completion (Actual): October 10, 2025
• Study Completion (Actual): October 10, 2025

Study Registration Dates

• First Submitted: November 24, 2025
• First Submitted That Met QC Criteria: December 16, 2025
• First Posted (Actual): December 18, 2025

Study Record Updates

• Last Update Posted (Actual): December 18, 2025
• Last Update Submitted That Met QC Criteria: December 16, 2025
• Last Verified: December 1, 2025

More Information

Terms related to this study

• Keywords: anticoagulant; algorithm; thrombosis
• Additional Relevant MeSH Terms: Vascular Diseases; Cardiovascular Diseases; Respiratory Tract Diseases; Lung Diseases; Embolism and Thrombosis; Embolism; Pulmonary Embolism; Thrombosis; Venous Thrombosis
• Other Study ID Numbers: DOACT-AI-VASC Study

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No