Gradient boosted machine learning algorithms predicted in-hospital deep venous thrombosis with AUROCs of 0.83 and 0.85 at 12- and 24-hour windows prior to onset, respectively.
Observational (n=99,237)
No
Can gradient boosted machine learning algorithms accurately predict the risk of developing deep venous thrombosis in hospitalized patients?
Gradient boosted machine learning algorithms demonstrated high accuracy (AUROC 0.83-0.85) in predicting in-hospital DVT 12 to 24 hours prior to onset, which could improve risk stratification and targeted prophylaxis.
Effect estimate: AUROC 0.83 and 0.85
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.
Ryan et al. (2021) conducted an observational in Deep venous thrombosis (n=99,237). Gradient boosted machine learning algorithms was evaluated on Diagnosis of in-hospital DVT (AUROC 0.83 and 0.85). Gradient boosted machine learning algorithms predicted in-hospital deep venous thrombosis with AUROCs of 0.83 and 0.85 at 12- and 24-hour windows prior to onset, respectively.