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

NCT ID: NCT07290608

Last Updated: 2025-12-18

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

59 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-01-20

Study Completion Date

2025-10-10

Brief Summary

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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.

Detailed Description

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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.

Conditions

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

Keywords

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anticoagulant algorithm thrombosis

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

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.
Primary Study Purpose

SUPPORTIVE_CARE

Blinding Strategy

SINGLE

Investigators
Single-blind. Investigator was blinded to the intervention assignment.

Study Groups

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DOACT algorithm

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

Group Type EXPERIMENTAL

DOACT algorithm

Intervention Type OTHER

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).

No algorithm

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

Group Type PLACEBO_COMPARATOR

No algorithm

Intervention Type OTHER

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).

LLM-based tools

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

Group Type ACTIVE_COMPARATOR

LLM-based tools

Intervention Type OTHER

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).

Interventions

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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).

Intervention Type 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).

Intervention Type 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).

Intervention Type OTHER

Eligibility Criteria

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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.

* 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

* 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)


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

18 Years

Maximum Eligible Age

89 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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ITALO EUGENIO SOUZA GADELHA DE ABREU

OTHER

Sponsor Role lead

Responsible Party

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ITALO EUGENIO SOUZA GADELHA DE ABREU

Principal Investigator

Responsibility Role SPONSOR_INVESTIGATOR

Locations

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Irmandade da Santa Casa de Misericórdia de São Paulo

São Paulo, São Paulo, Brazil

Site Status

Countries

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Brazil

References

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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.

Reference Type BACKGROUND
PMID: 31168834 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 29973392 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 24444650 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 33334670 (View on PubMed)

Other Identifiers

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DOACT-AI-VASC Study

Identifier Type: -

Identifier Source: org_study_id