Venous thromboembolism (VTE) is associated with significant morbidity, functional disability and mortality which leads to annual direct medical costs of 6 to 8 billion U.S. dollars. The incidence of VTE among patients with sickle cell disease (SCD) is significantly higher than in those without SCD, with lifetime risk of up to 25%. The highly variable clinical phenotypes of SCD, in addition to complex pathogenesis of thrombosis in SCD, are challenges to the early identification of high-risk patients and timely initiation of anticoagulant prophylaxis.


To develop a population-based risk assessment model (Predictive AlgoRithm of VTE in SCD, PARViS) for the identification of SCD patients at high-risk of VTE using least absolute shrinkage and selection operator (LASSO) methodology and compare its validity to the Caprini VTE risk assessment model.


We conducted a retrospective cohort study using the 2009-2014 Truven Health MarketScan® databases to identify commercially-insured health plan enrollees with VTE and SCD based on International Classification of Diseases (ICD) codes for inpatient and outpatient encounters. Baseline characteristics were assessed over the 6 months period following cohort entry and a risk window for any VTE events starting from day 181 after cohort entry and onwards. The clinical outcomes were defined as occurrence of VTE over the 30-, 90- and 180-day period.

The population-based cohort was divided into derivation and validation sets in a 2:1 ratio. The risk score was calculated using LASSO generalized linear regression models and divided into three risk categories for predicting 180-day VTE risk. Kaplan-Meier survivor functions were estimated for VTE rates by estimated risk score and censored for end of continuous enrollment, and end of observation period. The C-statistic was used to assess the prediction performance of the 7-factor risk score, which was compared with the Caprini VTE risk prediction model.


Among 11,774 subjects with SCD in the derivation cohort, the mean (SD) age at enrollment was 32.1 (19.8) years and 62.2% were female. From the validation cohort, 5949 SCD subjects were analyzed, participants' mean (SD) age at enrollment was 32.2 (19.7) years, and 62.6% were female. The 30-, 90- and 180-day VTE rates of the overall cohort were 0.6%, 1.3% and 2.0%, respectively.

The risk model included age, recent central vein catheter use (<30 day), active cancer, history of VTE, iron overload, osteomyelitis and pulmonary hypertension. Patients with SCD in the validation cohort were stratified into high-, intermediate- and low-risk in 2:3:5 ratio by VTE risk scores. Demographics and distribution of VTE risk factors are listed in Table 1. The rates of VTE at 180-days were 0.47% (95%CI 0.35%-0.64%), 1.38% (95%CI 1.10%-1.73%),6.71% (95%CI 5.94%-7.57%). [Figure 1]

In the derivation cohort, C statistics were 0.845 (95%CI 0.818-0.872) for 7-factor RAM in predicting 180-day VTE, 0.883 (95%CI 0.853-0.914) for 90-day VTE, and 0.917 (95%CI 0.875-0.959) for 30-day VTE. In the validation cohort, C statistics were 0.833 (95%CI 0.791-0.875) for 7-factor VTE risk assessment model in predicting 180-day VTE, 0.877 (95%CI 0.831-0.923) for 90-day VTE, and 0.942 (95%CI 0.911-0.972) for 30-day VTE. Using the Caprini VTE risk prediction model, we found statistically significant differences (p<0.0001) with C-statistics for 180-, 90- and 30-day VTE prediction of 0.721 (95%CI 0.672-0.770), 0.775 (95%CI 0.719-0.830), and 0.826 (95%CI 0.759-0.892). [Figure 2]


We developed and validated a 7-factor VTE risk assessment model specific to patients with SCD (PARViS). With its straightforward calculation and demonstrated accurate prediction of 6-month VTE rates in patients with SCD, the PARViS model can prove to be a useful prediction tool for clinical practitioners.


No relevant conflicts of interest to declare.

Author notes


Asterisk with author names denotes non-ASH members.