A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients

Abstract

Deep venous thrombosis (DVT) is associated with significant morbidity, mortality, and increased healthcare costs. Standard scoring systems for DVT risk stratification often provide insufficient stratification of hospitalized patients and are unable to accurately predict which inpatients are most likely to present with DVT. There is a continued need for tools which can predict DVT in hospitalized patients. We performed a retrospective study on a database collected from a large academic hospital, comprised of 99,237 total general ward or ICU patients, 2,378 of whom experienced a DVT during their hospital stay. Gradient boosted machine learning algorithms were developed to predict a patient's risk of developing DVT at 12- and 24-hour windows prior to onset. The primary outcome of interest was diagnosis of in-hospital DVT. The machine learning predictors obtained AUROCs of 0.83 and 0.85 for DVT risk prediction on hospitalized patients at 12- and 24-hour windows, respectively. At both 12 and 24 hours before DVT onset, the most important features for prediction of DVT were cancer history, VTE history, and internal normalized ratio (INR). Improved risk stratification may prevent unnecessary invasive testing in patients for whom DVT cannot be ruled out using existing methods. Improved risk stratification may also allow for more targeted use of prophylactic anticoagulants, as well as earlier diagnosis and treatment, preventing the development of pulmonary emboli and other sequelae of DVT.

Keywords: algorithms; deep venous thrombosis; machine learning; risk assessment; venous thromboembolism.

Figure 1.

Receiver operating characteristic (ROC) curves and comparison of area under the ROC (AUROC) of the XGBoost (XGB) and IMPROVE models for 12 and 24 hour prediction of deep venous thrombosis.

Figure 2.

Feature correlations and distribution of feature importance for each patient. Input variables are ranked in descending order of feature importance. Red indicates a high feature value; blue indicates a low feature value. Dots to the right resulted in a higher score; dots to the left resulted in a lower score. The superscript denotes the number of hours prior to the time the algorithm was applied, and Δ denotes change between the measurements at each indicated hour. For example, HR⁰. represents heart rate at the time the algorithm was applied, and Δ Antibiotics⁰¹ represents the change in antibiotic status from the previous hour to the current hour. Abbreviations used: INR: international normalized ratio. HR: heart rate. VTE: venous thromboembolism. PPL: pulse pressure.