Predictors of venous thromboembolism in cancer patients using machine learning.

Abstract

e24042 Background: The increased risk of venous thromboembolism (VTE) in cancer patients is clearly documented. However, given the heterogeneity and increased risk of bleeding in cancer population, patient selection for thromboprophylaxis is still challenging. Methods: In order to predict risk factors of VTE in cancer patients, we performed a retrospective study of 706 patients who were diagnosed with either solid or hematological malignancies between 2015 and 2019. Demographics, body mass index, complete blood count with differential, kidney function tests, electrolytes, liver function tests, lipid profile and cancer staging were recorded. Random forest analysis with bagging was used to rank these variables and the Kaplan-Meier survival analysis was implemented to stratify cancer subtypes based on the risk of VTE occurrence. Results: The mean follow-up time was 19 months. 8.2% of the patients developed VTE. Based on the random forest analysis, the most important five factors in prediction of VTE in cancer patients were determined as cancer subtype, white blood cell count, platelets, neutrophil and hemoglobin. At one-year mark, the risk of VTE in lung cancer and hematological malignancies was found to be significantly higher than breast, colorectal and endometrial cancer (p<0.05). Conclusions: Machine learning approach is infrequently used in risk factor prediction of VTE in cancer patients. The risk factors identified by the machine learning algorithm in our study are consistent with prior studies and show a clear difference in risk of VTE in various cancer subtypes. Moreover, hematological malignancies and lung cancer patients may develop VTE earlier than other cancer subtypes based on the Kaplan-Meier analysis. Further prospective studies with longer follow up are needed to better risk-stratify cancer patients and explore the temporal associations of VTE risk factors. [Table: see text]