# Machine Learning to Determine Dynamically Evolving New-Onset Venous Thromboembolic (VTE) Event Risk in Hospitalized Patients

> **NIH NIH F31** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $17,002

## Abstract

Failure to rescue (FTR), a nurse-sensitive national metric of health care quality, refers to death of a
hospitalized patient from a treatable complication, and is potentiated by failure to recognize and appropriately
respond to early signs of complications. There is a paucity of research examining patient features
predictive of FTR complications. Such information could shift the current paradigm of nursing surveillance to
earlier recognition, prevention and treatment of FTR complications, thereby saving lives. New-onset venous
thromboembolism (VTE), an FTR complication occurring as either a deep vein thrombosis (DVT) or a
pulmonary embolism (PE), is the leading cause of preventable hospital death, carrying a high risk of mortality
and a national cost burden of $7 billion annually. VTE is a complex disease process involving interactions
between clinical risk factors and acquired and/or inherited susceptibilities to thrombosis. Although biomarkers
and clinical factors associated with VTE have been identified, clinical manifestations are subtle, presenting
gradually over hours to days. Current VTE risk assessment models (RAM), the cornerstone of prevention, have
limited utility due to their complexity and lack of reliability, generalizability and external validation. A critical gap
in VTE risk modeling research is that while new-onset VTE pathology evolves over the course of
hospitalization, no current models incorporate the progressive accrual of dynamic patient data and pattern
evolution over time in their modeling approaches. The totality of routinely collected electronic health record
(EHR) data is massive in terms of volume, variety, and production at a rapid velocity in real-time. Such big data
could be used in machine learning (ML) analytic approaches to process time series clinical data to identify
subtle, evolving feature patterns predictive of new-onset VTE and address this gap. This study proposes to
assemble a large scale, multi-source, multi-dimensional VTE study dataset, and in tandem, systematically
define the EHR data elements associated with a new-onset VTE diagnosis for computable phenotype
algorithm development. We will then apply machine learning analytic approaches to baseline and accruing
intensive time series clinical data in the curated dataset to develop models identifying data patterns and
features predictive of dynamically evolving new-onset VTE in adult hospitalized patients. This proposal aligns
with NINR’s strategic vision for nurse scientists to employ new strategies for collecting and analyzing complex
big data sets to permit better understanding of the biological underpinnings of health, and improve ways nurses
prevent and manage illness. This innovative study and individualized training plan under a strong and well-
established team, represents initial steps in the applicant’s research trajectory focused on data science
approaches to predict FTR complication risk, and develop, implement and test dynamic RAMs to info...

## Key facts

- **NIH application ID:** 10219195
- **Project number:** 5F31NR018102-03
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Tiffany Purcell Pellathy
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $17,002
- **Award type:** 5
- **Project period:** 2018-09-01 → 2020-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10219195

## Citation

> US National Institutes of Health, RePORTER application 10219195, Machine Learning to Determine Dynamically Evolving New-Onset Venous Thromboembolic (VTE) Event Risk in Hospitalized Patients (5F31NR018102-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10219195. Licensed CC0.

---

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