# Optimizing the implementation of personalized risk-prediction models for venous thromboembolism among hospitalized adults

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2024 · $740,773

## Abstract

In the last 30 years, there has been no significant improvement in rates of venous thromboembolism (VTE).
These blood clots develop in the limbs and can travel to the lungs and form pulmonary emboli, which are the
most common cause of preventable deaths in the hospital. Currently available tools for predicting and preventing
hospital-acquired VTE (HA-VTE) were developed without sufficient input from frontline clinicians, add to clinician
workload, are too cumbersome to implement in daily clinical practice, exhibit poor-to-fair prediction accuracy,
and do not consider the risk of bleeding complications. Importantly, use of these tools has not been shown to
improve patient outcomes. A significant gap therefore exists between the current system of variable practice
patterns in VTE risk assessment and the goal of driving down rates of HA-VTE and reducing preventable deaths.
 Our objective is to refine, implement, and test a real-time prognostic model for HA-VTE among hospitalized
adults to facilitate appropriate and timely initiation of thromboprophylaxis by busy clinicians. Our multidisciplinary
team has developed a model that predicts the probability of HA-VTE among all adult inpatients based on clinical
factors and medical history. The model updates as the clinical scenario evolves, discriminates well between
high- and low-risk patients, and exhibits superior prediction performance compared with extant risk-stratification
tools. It is unknown whether use of a prognostic model for HA-VTE in clinical practice improves patient outcomes.
 To achieve this important objective, we will: conduct observations and interviews with clinicians to elucidate
their challenges with the current risk-assessment workflow and preferences for timing, content, and visualization
of a prognostic model (Aim 1); create user-friendly clinical decision support (CDS) tools—based on an accurate
and validated prognostic model for HA-VTE—that can be seamlessly integrated into existing clinical workflows,
simultaneously consider the risk of bleeding complications, and maximize use of electronic health record data in
real time (Aim 2); and conduct a pragmatic randomized trial and implementation evaluation of the prognostic
model plus CDS for prophylaxis compared with usual care for the prevention of HA-VTE. In an adaptive platform
trial, we will evaluate on a prospective basis the effectiveness of model-guided CDS to reduce HA-VTE, both
overall and among key patient subgroups, and study through randomization the implementation strategies that
work best for clinicians and improve patient outcomes (Aim 3). We will broadly disseminate the generalizable
knowledge and implementation tools that are urgently needed to prevent HA-VTE and avoid deaths in the
hospital, including an implementation manual, CDS knowledge artifacts, and open-source statistical software.
Relevance: Our proposal closely aligns with NHLBI objectives, namely: developing and optimizing a real-time
prognostic model to...

## Key facts

- **NIH application ID:** 10917080
- **Project number:** 5R01HL164482-02
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** BENJAMIN FRENCH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $740,773
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10917080

## Citation

> US National Institutes of Health, RePORTER application 10917080, Optimizing the implementation of personalized risk-prediction models for venous thromboembolism among hospitalized adults (5R01HL164482-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10917080. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*