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Updated: Jan 25, 2026

Stenosis of the Inferior Vena Cava: A Murine Model of Deep Vein Thrombosis
Published on: December 22, 2017
Comparison and screening of different risk assessment models for deep vein thrombosis in patients with solid tumors
Meng-Meng Wang1, Xiao-Juan Qin2,3, Xiao-Xiao He1
1Division of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430077, China.
Abstract:
To increase the detection rate of deep vein thrombosis (DVT) and to compare the predictive value of four different risk assessment scales (Caprini, Autar, Pauda, and Khorana scales) for DVT in patients with solid tumors by the receiver operating curve (ROC). A total of 361 patients with all kinds of malignant solid tumors, who accepted anti-tumor therapy in the cancer center between March 3, 2015 to April 13, 2018, were assigned to a group of 230 cases diagnosed with DVT and a control group of 131 cases without DVT. Data were recorded and summarized, and the predictive value of the above four risk assessment scales for DVT in solid tumor patients was compared based on the area under the ROC curve (AUC). The AUC values determined for the Caprini, Autar, Pauda, and Khorana scales were (0.631 ± 0.030), (0.686 ± 0.028), (0.654 ± 0.029), and (0.599 ± 0.032), respectively; maximum sensitivity, specificity, and Youden index were 80.9% for Khorana, 86.3% for Caprini, and 29.6% for Autar scale, respectively. We found no statistically significant differences in the AUC values between Autar and Caprini, Autar and Khorana, as well as Khorana and Pauda (p > 0.05). However, the AUC differences between Autar and Pauda, Caprini and Khorana, as well as Caprini and Pauda were statistically significant (p < 0.05). All four risk assessment models showed some value in the risk prediction of DVT in patients with solid tumors, but every model also exhibited its own restrictions; maximum sensitivity, specificity, and Youden index were 80.9% for Khorana, 86.3% for Caprini, and 29.6% for Autar scale, respectively. We confirmed that the detection rate can be improved by modifying the BMI cut-off value of the scale or by combining appropriate scales.
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