Ignite Creation Date:
2025-12-24 @ 7:25 PM
Ignite Modification Date:
2025-12-29 @ 1:53 PM
Study NCT ID:
NCT06939803
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-12-10
First Post:
2025-04-15
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI-driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism: Study Protocol for the VTE-AI Randomized Trial.
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'PREVENTION', 'interventionModel': 'PARALLEL', 'interventionModelDescription': 'Randomized Controlled Clinical Trial'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 2236}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-12-17', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-07', 'completionDateStruct': {'date': '2027-03-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-12-09', 'studyFirstSubmitDate': '2025-04-15', 'studyFirstSubmitQcDate': '2025-04-15', 'lastUpdatePostDateStruct': {'date': '2025-12-10', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-04-23', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-09-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Incidence of Hospital Acquired VTE', 'timeFrame': 'Baseline to discharge from hospital, approximately 2 to 5 days', 'description': 'Percentage of admissions in which patients were diagnosed with VTE more than 48 hours after admission, defined as "Hospital Acquired" in prior literature'}], 'secondaryOutcomes': [{'measure': 'Thirty-day hospital readmissions', 'timeFrame': 'Day 30 following hospital discharge', 'description': 'Rate of hospitalizations followed by another unplanned hospitalization within thirty-days of discharge'}, {'measure': 'Bleeding events', 'timeFrame': 'Baseline to discharge from hospital, approximately 2 to 5 days', 'description': 'Rates of bleeding events for hospitalized patients. An uncommon risk of VTE prophylaxis is an increased risk of bleeding'}, {'measure': 'Length of Stay', 'timeFrame': 'Date of admission to date of discharge from hospital, approximately 2 to 5 days', 'description': 'Number of days from admission to discharge for each hospitalization'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Clinical decision support', 'Hospital-acquired venous thromboembolism', 'Artificial intelligence', 'Electronic health record', 'Thromboprophylaxis', 'Patient safety', 'Implementation study'], 'conditions': ['Venothromboembolism']}, 'referencesModule': {'references': [{'pmid': '38882464', 'type': 'BACKGROUND', 'citation': 'Tillman BF, Domenico HJ, Moore RP, Byrne DW, Morton CT, Mixon AS, French B. A real-time prognostic model for venous thromboembolic events among hospitalized adults. Res Pract Thromb Haemost. 2024 May 6;8(4):102433. doi: 10.1016/j.rpth.2024.102433. eCollection 2024 May.'}, {'pmid': '41042513', 'type': 'DERIVED', 'citation': 'Walsh CG, Long Y, Novak LL, Salwei ME, Tillman B, French B, Mixon AS, Law ME, Franklin J, Embi PJ. AI-Driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism: A Trial Protocol. JAMA Netw Open. 2025 Oct 1;8(10):e2535137. doi: 10.1001/jamanetworkopen.2025.35137.'}]}, 'descriptionModule': {'briefSummary': "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.\n\nThis 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.\n\nThe 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.\n\nCurrently, 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.", 'detailedDescription': '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.\n\nDespite 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.\n\nThe 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.\n\nRationale 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.\n\nThe 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).\n\nThe 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.\n\nThe 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.\n\nObjectives 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.\n\nSecondary 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.\n\nTrial design Two-arm RCT with randomization at encounter-level.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Inpatient admission to Vanderbilt Adult Hospital, Vanderbilt Tullahoma Harton Hospital, Vanderbilt Bedford County Hospital, or Vanderbilt Wilson County Hospital\n\nExclusion Criteria:\n\n* None'}, 'identificationModule': {'nctId': 'NCT06939803', 'acronym': 'VTE-AI RCT', 'briefTitle': 'AI-driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism: Study Protocol for the VTE-AI Randomized Trial.', 'organization': {'class': 'OTHER', 'fullName': 'Vanderbilt University Medical Center'}, 'officialTitle': 'AI-driven Clinical Decision Support to Reduce Hospital-Acquired Venous Thromboembolism', 'orgStudyIdInfo': {'id': '241978'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Interventional', 'description': 'Hospitalizations randomized to receive risk model-driven CDS', 'interventionNames': ['Other: Risk model-driven CDS']}, {'type': 'NO_INTERVENTION', 'label': 'Standard of Care', 'description': 'Hospitalizations randomized to receive Standard of Care in a given clinical setting'}], 'interventions': [{'name': 'Risk model-driven CDS', 'type': 'OTHER', 'description': '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', 'armGroupLabels': ['Interventional']}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'Protected Health Information'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Vanderbilt University Medical Center', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate Professor', 'investigatorFullName': 'Colin G. Walsh', 'investigatorAffiliation': 'Vanderbilt University Medical Center'}}}}