AI-driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism: Study Protocol for the VTE-AI Randomized Trial. (VTE-AI RCT)

February 10, 2026 updated by: Colin G. Walsh, Vanderbilt University Medical Center

AI-driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism

Hospital-acquired blood clots (HA-VTE) are the leading cause of death in hospitalized patients in the US. Each year, about 900,000 people get blood clots, costing between $7 and $10 billion in medical expenses. HA-VTE is the second leading cause of long-term disability and causes significant health issues and deaths in both adults and children. About 1 in 3 people who get blood clots experience long-term complications. Reducing HA-VTE is a major challenge.

This study will test a new AI method to predict and prevent HA-VTE. The goal is to see if this AI tool can reduce the number of HA-VTE cases in the Vanderbilt Health System, which includes both urban and rural hospitals.

The AI tool, called VTE-AI, calculates a risk score without needing input from doctors. It will suggest reconsidering blood clot prevention measures for patients who don't have them ordered and have no reasons to avoid them. This suggestion will be made after admission and daily during the hospital stay.

Currently, doctors manually calculate a risk score and choose a prevention option. This study will compare the effectiveness of the AI tool against the current manual method in reducing HA-VTE cases. The study will randomly assign half of the patients to use the AI tool and the other half to the standard manual method.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Background Hospital Acquired Venous Thromboembolism (HA-VTE) remains the leading cause of death in hospitalized patients in the US. Approximately 900,000 people experience VTE each year, with incidence-based medical costs estimated between $7 and $10 billion per year. The second leading cause of disability-adjusted life-years, HA-VTE causes significant morbidity and mortality in adult and pediatric patients. Roughly 1 in 3 people experience long-term complications (i.e., post-thrombotic syndrome) following VTE. Reducing HA-VTE presents a major diagnostic challenge.

Despite numerous published prognostic models of HA-VTE, no single model outperforms the rest. And HA-VTE affects groups inequitably, which means models might reflect or worsen healthcare disparities if they are not deployed in the context of responsible, algorithmovigilant systems. Integrating scalable AI for HA-VTE prevention into effective clinical decision support (CDS) might effectively reduce HA-VTE incidence while aiding the realization of the potential for AI in high-value clinical practice. Recently, a Vanderbilt team of clinicians and biostatisticians validated a regression risk score called "VTE-AI" to prognosticate risk of HA-VTE on admission.

The urban-rural divide has long caused healthcare disparities in morbidity and mortality. These differences might not result from rurality itself, but from "the effects of socio-economic disadvantage, ethnicity, poorer service availability, higher levels of personal risk and more hazardous environmental, occupational and transportation conditions." AI implementation will be no different without close attention to differences in both deployment settings. Studying multiple simultaneous implementations of AI in both urban and rural setting with adult and pediatric patients will yield unprecedented insights for AI-driven CDS.

Rationale and Specific Aims This study will rigorously study a novel AI approach to predicting risk of HA-VTE and guiding prevention. This trial will produce strong evidence for the pragmatic use of novel AI-CDS to prevent HA-VTE across diverse sites and populations.

The goal of this study is to evaluate the effectiveness of AI-driven CDS to reduce the incidence of HA-VTE across the Vanderbilt Health System including urban and rural sites: Vanderbilt Adult Hospital (VUH) and the Vanderbilt Regional Health System (VRHS) including Vanderbilt Tullahoma Harton Hospital (VTHH), Vanderbilt Bedford County Hospital (VBCH), and Vanderbilt Wilson County Hospital (VWCH).

The investigators will implement a validated risk score, VTE-AI,5 which does not require clinician input to calculate, to prompt CDS suggesting reconsideration of DVT prophylaxis in those who 1) do not have active prophylaxis ordered and 2) have no contraindication to pharmacologic prophylaxis. This CDS "nudge" will occur after admission orders have been submitted for hospital admissions and on each subsequent day of an inpatient encounter.

The current standard of care is an order set requiring manual calculation of the Padua risk tool and selection of a prophylaxis option or documentation of a temporary or permanent exception. The investigators will evaluate the effectiveness of the VTE-AI-driven clinical decision support (CDS) against the standard DVT/VTE prophylaxis order set to reduce HA-VTE incidence across urban/rural dimensions. The investigators will conduct a pragmatic RCT of VTE-AI-driven CDS randomizing half the eligible encounters to CDS and half to standard of care.

Objectives Primary Objective The investigators hypothesize CDS will reduce incidence of HA-VTE in those i) predicted at high risk by VTE-AI and ii) without evidence of pharmacologic prophylaxis in half, from baseline 4.3% incidence (562/12,946 events) to 2.2% incidence, which will require 2,236 encounters. Sample size calculation indicates at least 1,118 patient encounters are needed in each arm to achieve 80% power with 5% probability of type I error. Using historical data from 2023-2024, there were 150-230 encounters per month meeting these criteria. The investigators will therefore conduct the RCT for one (1) year to meet required sample size.

Secondary Objective The investigators hypothesize the VTE-AI CDS will not increase bleeding risk, readmission rates, or lengths of stay (LOS) between the intervention and non-intervention arms.

Trial design Two-arm RCT with randomization at encounter-level.

Study Type

Interventional

Enrollment (Estimated)

2236

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • United States
    • Tennessee
      • Nashville, Tennessee, United States, 37203
        • Recruiting
        • Vanderbilt University Medical Center
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Inpatient admission to Vanderbilt Adult Hospital, Vanderbilt Tullahoma Harton Hospital, Vanderbilt Bedford County Hospital, or Vanderbilt Wilson County Hospital

Exclusion Criteria:

  • None

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Prevention
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Interventional
Hospitalizations randomized to receive risk model-driven CDS
Other: Risk model-driven CDS
The CDS intervention will use an automated risk model called "VTE-AI" to add EHR-based prompts in the form of alerts targeting those encounters on which 1) VTE-AI risk is above 5% predicted risk (found to be high risk in prior analyses), 2) no active DVT prophylaxis pharmacologic order is present, 3) no contraindication has been documented in the current admission
No Intervention: Standard of Care
Hospitalizations randomized to receive Standard of Care in a given clinical setting

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Incidence of Hospital Acquired VTE
Time Frame: Baseline to discharge from hospital, approximately 2 to 5 days
Percentage of admissions in which patients were diagnosed with VTE more than 48 hours after admission, defined as "Hospital Acquired" in prior literature
Baseline to discharge from hospital, approximately 2 to 5 days

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Thirty-day hospital readmissions
Time Frame: Day 30 following hospital discharge
Rate of hospitalizations followed by another unplanned hospitalization within thirty-days of discharge
Day 30 following hospital discharge
Bleeding events
Time Frame: Baseline to discharge from hospital, approximately 2 to 5 days
Rates of bleeding events for hospitalized patients. An uncommon risk of VTE prophylaxis is an increased risk of bleeding
Baseline to discharge from hospital, approximately 2 to 5 days
Length of Stay
Time Frame: Date of admission to date of discharge from hospital, approximately 2 to 5 days
Number of days from admission to discharge for each hospitalization
Date of admission to date of discharge from hospital, approximately 2 to 5 days

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Vanderbilt University Medical Center

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 17, 2025

Primary Completion (Estimated)

December 16, 2026

Study Completion (Estimated)

March 31, 2027

Study Registration Dates

First Submitted

April 15, 2025

First Submitted That Met QC Criteria

April 15, 2025

First Posted (Actual)

April 23, 2025

Study Record Updates

Last Update Posted (Actual)

February 13, 2026

Last Update Submitted That Met QC Criteria

February 10, 2026

Last Verified

February 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 241978

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Protected Health Information

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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