**6. The scoring system for risk assessment for VTE in cancer patients**

The development of predictive risk assessment model in non-cancer patients has helped to stratify patients according to their VTE risk and tailor thromboprophylaxis accordingly. Some models in surgical patients stratified patients according to the type of operation (major or minor), age and the presence of additional risk factors eg. cancer, prior VTE, obesity, co-morbid medical conditions.

In cancer patients, risk stratification is a dynamic process depending on the type and stage of cancer, performance status, and supportive and specific cancer therapy. A model-based approach that incorporates multiple risk factors for VTE can help identify the high-risk subgroups in the cancer population and would allow for a directed prophylactic strategy to improve outcomes of management and sparing the low risk patients from unnecessary anticoagulation therapy with its complications, social and financial burden. The ideal score model has to be simple, sensitive, specific and well validated.

Khorana AA et al (209) developed a simple model for predicting chemotherapy-associated VTE using baseline clinical and laboratory variables. The association of VTE with multiple variables was characterized in a derivation cohort of 2701 cancer outpatients from a prospective observational study. A risk model was derived and validated in an independent cohort of 1365 patients from the same study. Five (2 clinical and 3 laboratory) predictive variables were identified in a multivariate model: site of cancer (2 points for very high-risk site, 1 point for high-risk site), platelet count of ≥350 × 109/L, Hb <100 g/L (10 g/dL) and/or use of erythropoiesis-stimulating agents, WBC ≥11 × 109/L, and BMI of ≥35 kg/m2 or more (1 point each). Rates of VTE in the derivation and validation cohorts, respectively, were 0.8% and 0.3% in low-risk (score = 0), 1.8% and 2% in intermediate-risk (score = 1-2), and 7.1% and 6.7% in high-risk (score ≥ 3) category over a median of 2.5 months (C-statistic = 0.7 for both cohorts). Khorana AA et al stated that their model can identify patients with a nearly 7% short-term risk of symptomatic VTE.


Table 2. Predictive model for chemotherapy-associated VTE Adapted from Khorana et al. (209) with permission

To improve prediction of VTE in cancer patients, Ay C et al (210) performed a prospective and observational cohort study of patients with newly diagnosed cancer or progression of disease after remission. Khorana's risk scoring model for prediction of VTE that included clinical (tumor entity and body mass index) and laboratory (Hb, platelet and WBC count) parameters was expanded by incorporating 2 biomarkers, soluble P-selectin, and D-Dimer. Pathophysiology and Clinical Aspects of 94 Venous Thromboembolism in Neonates, Renal Disease and Cancer Patients

The development of predictive risk assessment model in non-cancer patients has helped to stratify patients according to their VTE risk and tailor thromboprophylaxis accordingly. Some models in surgical patients stratified patients according to the type of operation (major or minor), age and the presence of additional risk factors eg. cancer, prior VTE,

In cancer patients, risk stratification is a dynamic process depending on the type and stage of cancer, performance status, and supportive and specific cancer therapy. A model-based approach that incorporates multiple risk factors for VTE can help identify the high-risk subgroups in the cancer population and would allow for a directed prophylactic strategy to improve outcomes of management and sparing the low risk patients from unnecessary anticoagulation therapy with its complications, social and financial burden. The ideal score

Khorana AA et al (209) developed a simple model for predicting chemotherapy-associated VTE using baseline clinical and laboratory variables. The association of VTE with multiple variables was characterized in a derivation cohort of 2701 cancer outpatients from a prospective observational study. A risk model was derived and validated in an independent cohort of 1365 patients from the same study. Five (2 clinical and 3 laboratory) predictive variables were identified in a multivariate model: site of cancer (2 points for very high-risk site, 1 point for high-risk site), platelet count of ≥350 × 109/L, Hb <100 g/L (10 g/dL) and/or use of erythropoiesis-stimulating agents, WBC ≥11 × 109/L, and BMI of ≥35 kg/m2 or more (1 point each). Rates of VTE in the derivation and validation cohorts, respectively, were 0.8% and 0.3% in low-risk (score = 0), 1.8% and 2% in intermediate-risk (score = 1-2), and 7.1% and 6.7% in high-risk (score ≥ 3) category over a median of 2.5 months (C-statistic = 0.7 for both cohorts). Khorana AA et al stated that their model can identify patients with a nearly

 Very high risk (stomach, pancreas) 2 High risk (lung, lymphoma, gynecologic, bladder, testicular) 1 Prechemotherapy platelet count ≥ 350x10/L 1 Hemoglobin level ≤ 10g/dl or use or erythropoietin 1 Prechemotherapy leukocyte count more than 11000/mm3 1 Body mass index 35 kg/m2 or more 1

To improve prediction of VTE in cancer patients, Ay C et al (210) performed a prospective and observational cohort study of patients with newly diagnosed cancer or progression of disease after remission. Khorana's risk scoring model for prediction of VTE that included clinical (tumor entity and body mass index) and laboratory (Hb, platelet and WBC count) parameters was expanded by incorporating 2 biomarkers, soluble P-selectin, and D-Dimer.

**Patients characteristics Risk** 

**score** 

**6. The scoring system for risk assessment for VTE in cancer patients** 

obesity, co-morbid medical conditions.

7% short-term risk of symptomatic VTE.

Site of Cancer

model has to be simple, sensitive, specific and well validated.

Table 2. Predictive model for chemotherapy-associated VTE

Adapted from Khorana et al. (209) with permission

Of 819 patients 61 (7.4%) experienced VTE during a median follow-up of 656 days. The cumulative VTE probability in the original risk model after 6 months was 17.7% in patients with the highest risk score (≥ 3, n = 93), 9.6% in those with score 2 (n = 221), 3.8% in those with score 1 (n = 229) and 1.5% in those with score 0 (n = 276). In the expanded risk model, the cumulative VTE probability after 6 months in patients with the highest score (≥ 5, n = 30) was 35.0% and 10.3% in those with an intermediate score (score 3, n = 130) as opposed to only 1.0% in patients with score 0 (n = 200); the hazard ratio of patients with the highest compared with those with the lowest score was 25.9 (8.0-84.6). The authors demonstrated that clinical and standard laboratory parameters with addition of biomarkers enable prediction of VTE and allow identification of cancer patients at high or low risk of VTE.

Ay C et al concluded that with expanded risk model, which included sP-selectin ≥53.1 mg/ml and D-Dimer ≥1.44 mg/ml, (2 biomarkers) the risk prediction can be considerably improved. In patients with the highest compared with patients with the lowest risk, the probability for VTE was 26-fold higher.

The advantage of the "Khorana-Score" is that all parameters of this risk model are routinely determined in cancer patients at diagnosis.
