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Updated: Nov 8, 2025
Point-Of-Care Ultrasound Screening for Proximal Lower Extremity Deep Venous Thrombosis
Published on: February 10, 2023
A risk score for iliofemoral patients with deep vein thrombosis
Soroosh Shekarchian1, Pascale Notten1, Mohammad Esmaeil Barbati2
1Department of Vascular Surgery, Maastricht University Medical Center, Maastricht, The Netherlands.
Insights
A new model using d-dimer, Wells score, age, and anticoagulation use can identify patients with iliofemoral deep vein thrombosis (IFDVT). This helps prioritize those who may benefit from early iliofemoral thrombectomy, improving outcomes.
Area of Science:
- Vascular Medicine
- Diagnostic Imaging
- Thrombosis Research
Background:
- Deep vein thrombosis (DVT) poses a significant risk of post-thrombotic morbidity, particularly with proximal thrombus.
- Early intervention, such as iliofemoral clot removal, may reduce the severity of post-thrombotic syndrome.
- Accurate and timely diagnosis of iliofemoral DVT (IFDVT) is crucial for initiating effective thrombus lysis.
Purpose of the Study:
- To develop and validate a predictive model for identifying patients with IFDVT.
- To assess the utility of d-dimer, Wells score, and other risk factors in predicting IFDVT.
- To facilitate early referral and treatment for patients at high risk of IFDVT.
Main Methods:
- Retrospective analysis of 3381 patients suspected of DVT.
- Inclusion of diagnostic workup data: Wells score, d-dimer, and duplex ultrasound.
- Logistic regression analysis to develop a multivariate model for IFDVT prediction.
Main Results:
- A multivariate model incorporating d-dimer, Wells score, age, and anticoagulation use was developed.
- The model demonstrated a sensitivity of 77% and specificity of 82% in distinguishing IFDVT patients.
- The area under the receiver operating characteristic curve was 0.90, indicating good predictive performance.
Conclusions:
- The developed multivariate model effectively distinguishes IFDVT among patients with suspected DVT.
- This model can provide a pre-duplex risk score to prioritize patients for immediate imaging.
- Further validation studies are recommended to confirm the model's potential in clinical practice.
Objective:
Deep vein thrombosis (DVT) is a common condition with a high risk of post-thrombotic morbidity, especially in patients with a proximal thrombus. Successful iliofemoral clot removal has been shown to decrease the severity of post-thrombotic syndrome. It is assumed that earlier thrombus lysis is associated with a better outcome. Generally, the earlier IFDVT is confirmed, the earlier thrombus lysis could be performed. d-Dimer levels and Wells score are currently used to assess the preduplex probability for DVT; however, some studies indicate that the d-dimer value varies depending on the thrombus extent and localization. Using d-dimer and other risk factors might facilitate development of a model selecting those with an increased risk of IFDVT that might benefit from early referral for additional analysis and adjunctive iliofemoral thrombectomy.
Methods:
All consecutive adult patients from a retrospective cohort of STAR diagnostic center (primary care) in Rotterdam suspected of having DVT between September 2004 and August 2016 were assessed for this retrospective study. The diagnostic workup for DVT including Wells score and d-dimer were performed as well as complete duplex ultrasound examination. Patients with objective evidence of DVT were categorized according to thrombus localization using the Lower Extremity Thrombolysis classification. Logistic regression analysis was done for a model predicting IFDVT. The cut-off value of the model was determined using a receiver operating characteristic curve.
Results:
A total of 3381 patients were eligible for study recruitment, of whom 489 (14.5%) had confirmed DVT. We developed a multivariate model (sensitivity of 77% and specificity of 82%; area under the curve, 0.90; 0.86-0.93) based on d-dimer, Wells score, age, and anticoagulation use, which is able to distinguish IFDVT patients from all patients suspected of DVT.
Conclusions:
This multivariate model adequately distinguishes IFDVT among all suspected DVT patients. Practically, this model could give each patient a preduplex risk score, which could be used to prioritize suspected IFDVT patients for an immediate imaging test to confirm or exclude IFDVT. Further validation studies are needed to confirm potential of this prediction model for IFDVT.
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