Principal Component Analysis on Recurrent Venous Thromboembolism

Abstract

The rates of recurrent venous thromboembolism (RVTE) vary widely, and its causes still need to be elucidated. Statistical multivariate methods can be used to determine disease predictors and improve current methods for risk calculation. The objective of this study was to apply principal component analysis to a set of data containing clinical records of patients with previous venous thromboembolism and extract the main factors that predict recurrent thrombosis. Records of 39 factors including blood and lipid parameters, hereditary thrombophilia, antiphospholipid syndrome, clinical data regarding previous thrombosis and treatment, and Doppler ultrasound results were collected from 235 patients. The results showed that 13 principal components were associated with RVTE and that 18 of 39 factors are the important for the analysis. These factors include red blood cell, white blood cell, hematocrit, red cell distribution width, glucose, lipids, natural anticoagulant, creatinine, age, as well as first deep vein thrombosis data (distal/proximal, d-dimer, and time of anticoagulation). The results demonstrated that simple clinical parameters easy to be collected can be used to predict rates of recurrence and to develop new clinical decision support systems to predict the rates of RVTE.