Watson Connor, Saaid Hicham, Vedula Vijay, Cardenas Jessica C, Henke Peter K, Nicoud Franck, Xu Xiao Yun, Hunt Beverley J, Manning Keefe B
Department of Biomedical Engineering, The Pennsylvania State University, 122 Chemical and Biomedical Engineering Building, University Park, PA, 16802-4400, USA.
Department of Mechanical Engineering, Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY, USA.
Ann Biomed Eng. 2024 Mar;52(3):467-486. doi: 10.1007/s10439-023-03390-z. Epub 2023 Nov 1.
Venous thromboembolism (VTE) is a massive clinical challenge, annually affecting millions of patients globally. VTE is a particularly consequential pathology, as incidence is correlated with extremely common risk factors, and a large cohort of patients experience recurrent VTE after initial intervention. Altered hemodynamics, hypercoagulability, and damaged vascular tissue cause deep-vein thrombosis and pulmonary embolism, the two permutations of VTE. Venous valves have been identified as likely locations for initial blood clot formation, but the exact pathway by which thrombosis occurs in this environment is not entirely clear. Several risk factors are known to increase the likelihood of VTE, particularly those that increase inflammation and coagulability, increase venous resistance, and damage the endothelial lining. While these risk factors are useful as predictive tools, VTE diagnosis prior to presentation of outward symptoms is difficult, chiefly due to challenges in successfully imaging deep-vein thrombi. Clinically, VTE can be managed by anticoagulants or mechanical intervention. Recently, direct oral anticoagulants and catheter-directed thrombolysis have emerged as leading tools in resolution of venous thrombosis. While a satisfactory VTE model has yet to be developed, recent strides have been made in advancing in silico models of venous hemodynamics, hemorheology, fluid-structure interaction, and clot growth. These models are often guided by imaging-informed boundary conditions or inspired by benchtop animal models. These gaps in knowledge are critical targets to address necessary improvements in prediction and diagnosis, clinical management, and VTE experimental and computational models.
静脉血栓栓塞症（VTE）是一项重大的临床挑战，全球每年有数百万患者受其影响。VTE是一种后果尤为严重的病理状况，因为其发病率与极为常见的风险因素相关，而且大量患者在初次干预后会出现复发性VTE。血流动力学改变、高凝状态以及血管组织受损会导致深静脉血栓形成和肺栓塞，这是VTE的两种表现形式。静脉瓣膜已被确定为最初血栓形成的可能部位，但在这种情况下血栓形成的确切途径尚不完全清楚。已知有几种风险因素会增加VTE的发生可能性，特别是那些会增加炎症和凝血能力、增加静脉阻力以及损害内皮内衬的因素。虽然这些风险因素可作为预测工具，但在出现外在症状之前诊断VTE很困难，主要是因为成功对深静脉血栓进行成像存在挑战。临床上，VTE可通过抗凝剂或机械干预进行治疗。最近，直接口服抗凝剂和导管定向溶栓已成为解决静脉血栓形成的主要手段。虽然尚未开发出令人满意的VTE模型，但最近在推进静脉血流动力学、血液流变学、流固相互作用和血栓生长的计算机模拟模型方面取得了进展。这些模型通常由影像学告知的边界条件指导，或受台式动物模型启发。这些知识空白是解决预测和诊断、临床管理以及VTE实验和计算模型方面必要改进的关键目标。
