466 Development of Machine Learning Algorithms to Predict Symptomatic VTE at Time of Admission and Time of Discharge after Severe Traumatic Injury

Abstract

OBJECTIVES/GOALS: Clinical indicators predictive of venous thromboembolism (VTE) in trauma patients at multiple time points are not well outlined, particularly at time of discharge. We aimed to describe and predict inpatient and post-discharge risk factors of VTE after trauma using a multi-variate regression model and best of class machine learning (ML) models. METHODS/STUDY POPULATION: In a prospective, case-cohort study, all trauma patients (pts) who arrived as level 1 or 2 trauma activations, from June 2018 to February 2020 were considered for study inclusion. A subset of pts who developed incident, first time, VTE and those who did not develop VTE within 90 days of discharge were identified. VTE were confirmed either by imaging or at autopsy during inpatient stay or post-discharge. Outcomes were defined as the development of symptomatic VTE (DVT and/or PE) within 90 days of discharge.A multi-variate Cox regression model and a best in class of a set of 5 different ML models (support-vector machine, random-forest, naives Bayes, logistic regression, neural network]) were used to predict VTE using models applied a) at 24 hours of injury date or b) on day of patient discharge. RESULTS/ANTICIPATED RESULTS: Among 393 trauma pts (ISS=12.0, hospital LOS=4.0 days, age=48 years, 71% male, 96% with blunt mechanism, mortality 2.8%), 36 developed inpatient VTE and 36 developed VTE after discharge. In a weighted, multivariate Cox model, any type of surgery by day 1, increased age per 10 years, and BMI per 5 points were predictors of overall symptomatic VTE (C-stat 0.738). Prophylactic IVC filter placement (4.40), increased patient age per 10 years, and BMI per 5 points were predictors of post-discharge symptomatic VTE (C-stat= 0.698). A neural network ML model predicted VTE by day 1 with accuracy and AUC of 0.82 and 0.76, with performance exceeding those of a Cox model. A naīve Bayesian ML model predicted VTE at discharge, with accuracy and AUC of 0.81 and 0.77 at time of discharge, with performance exceeding those of a Cox model. DISCUSSION/SIGNIFICANCE: The rate of inpatient and post-discharge VTEs remain high. Limitations: single institution study, limited number of patients, internal validation only, with the use of limited number of ML models. We developed and internally validated a ML based tool.Future work will focus on external validation and expansion of ML techniques.