Lin Mary Sixian, Hayssen Hilary, Mayorga-Carlin Minerva, Sahoo Shalini, Siddiqui Tariq, Jreij Georges, Englum Brian R, Nguyen Phuong, Yesha Yelena, Sorkin John David, Lal Brajesh K
Department of Surgery, University of Maryland School of Medicine, Baltimore, MD.
Department of Surgery, Baltimore VA Medical Center, Baltimore, Baltimore, MD.
J Vasc Surg Venous Lymphat Disord. 2025 Jan;13(1):101968. doi: 10.1016/j.jvsv.2024.101968. Epub 2024 Sep 19.
Venous thromboembolism (VTE) is a preventable cause of hospitalization-related morbidity and mortality. VTE prevention requires accurate risk stratification. Federal agencies mandated VTE risk assessment for all hospital admissions. We have shown that the widely used Caprini (30 risk factors) and Padua (11 risk factors) VTE risk-assessment models (RAMs) have limited predictive ability for VTE when used for all general hospital admissions. Here, we test whether combining the risk factors from all 23 available VTE RAMs improves VTE risk prediction.
We analyzed data from the first hospitalizations of 1,282,014 surgical and non-surgical patients admitted to 1298 Veterans Affairs facilities nationwide between January 2016 and December 2021. We used logistic regression to predict VTE within 90 days of admission using risk factors from all 23 available VTE RAMs. Area under the receiver operating characteristic curves (AUC), sensitivity, specificity, and positive (PPV) and negative predictive values (NPV) were used to quantify the predictive power of our models. The metrics were computed at two diagnostic thresholds that maximized (1) the value of sensitivity + specificity-1; and (2) PPV and were compared using McNemar's test. The Delong-Delong test was used to compare AUCs.
After excluding those with missing data, 1,185,633 patients (mean age, 66 years; 93% male; and 72% White) were analyzed, of whom 33,253 (2.8%) had a VTE (deep venous thrombosis [DVT], n = 19,218, 1.6%; pulmonary embolism [PE], n = 10,190, 0.9%; PE + DVT, n = 3845, 0.3%). Our composite RAM included 102 risk factors and improved prediction of VTE compared with the Caprini RAM risk factors (AUC composite model: 0.74; AUC Caprini risk-factor model: 0.63; P < .0001). When the sum of sensitivity and specificity-1 was maximized, the composite model demonstrated small improvements in sensitivity, specificity and PPV; NPV was high in both models. When PPV was maximized, the PPV of the composite model was improved but remained low. The nature of the relationship between NPV and PPV precluded any further gain in PPV by sacrificing NPV and sensitivity.
Using a composite of 102 risk factors from all available VTE RAMs, we improved VTE prediction in a large, national cohort of >1 million general hospital admissions. However, neither model has a sensitivity or PPV that permits it to be a reliable predictor of VTE. We demonstrate the limits of currently available VTE risk prediction tools; no available RAM is ready for widespread use in the general hospital population.
静脉血栓栓塞症（VTE）是住院相关发病和死亡的一个可预防原因。VTE预防需要准确的风险分层。联邦机构要求对所有住院患者进行VTE风险评估。我们已经表明，广泛使用的Caprini（30个风险因素）和Padua（11个风险因素）VTE风险评估模型（RAMs）在用于所有综合医院住院患者时，对VTE的预测能力有限。在此，我们测试将所有23种可用的VTE RAMs中的风险因素相结合是否能改善VTE风险预测。
我们分析了2016年1月至2021年12月期间在全国1298家退伍军人事务机构住院的1,282,014例手术和非手术患者首次住院的数据。我们使用逻辑回归，利用所有23种可用的VTE RAMs中的风险因素预测入院后90天内的VTE。受试者工作特征曲线下面积（AUC）、敏感性、特异性以及阳性（PPV）和阴性预测值（NPV）用于量化我们模型的预测能力。这些指标在两个诊断阈值下计算，这两个阈值分别使（1）敏感性+特异性-1的值最大化；以及（2）PPV最大化，并使用McNemar检验进行比较。使用Delong-Delong检验比较AUCs。
在排除数据缺失的患者后，对1,185,633例患者（平均年龄66岁；93%为男性；72%为白人）进行了分析，其中33,253例（2.8%）发生了VTE（深静脉血栓形成[DVT]，n = 19,218，1.6%；肺栓塞[PE]，n = 10,190，0.9%；PE + DVT，n = 3845，0.3%）。我们的综合RAM包括102个风险因素，与Caprini RAM风险因素相比，改善了VTE的预测（AUC综合模型：0.74；AUC Caprini风险因素模型：0.63；P <.0001）
