Optimizing the Prediction of Venous Thromboembolis by a Risk Assessment Model in Patients with Glioblastoma

Abstract PurposeTo find an optimal model to predict venous thromboembolism specially for glioblastoma by a prospective research.Methods Patients with newly histologically confirmed glioblastoma multiform (GBM) were recruited for this study. Status of IDH1, PTEN, P53, BRAF, MGMT and TERT were determined using genetic sequencing through polymerase chain reaction (PCR). Amplification of EGFR was established through fluorescence in situ hybridization (FISH). Competing-risks regression model was performed to calculate the risk of VTE. Clinical and laboratory parameters that were independently predicted risk of VTE were used to develop a risk assessment model (RAM).Results145 patients with GBM were included in the present analysis. A total of 48/145 patients (33.1%) developed VTE, all 48 patients were found to be IDH1wt . D-dimer, ECOG score and EGFR staus were sugggested to be significantly associtaed with the VTE risk in multivariable analysis. High ECOG score (>2), high D-dimer (>1.6 μg/mL) and EGFR amplification were used as the strongest independent predictors of increased risk of VTE. The cumulative incidence of VTE was 17.2% for patients with score 0 (n =29), 23.6% for patients with score 1 (n =55 ) and 63.8% for patients with score 2 (n = 35) or score 3 (n = 12) by application of a RAM .Conclusion GBM patients with IDH1 mutation were at very low risk of VTE. In IDHwt patients, by applying a VTE risk assessment model, we could identify patients with a very high and low risk of VTE.