Department of Radiology, Weill Cornell Medicine, New York, NY.
Department of Radiology, Columbia College of Physicians and Surgeons, New York, NY.
Medicine (Baltimore). 2021 Sep 24;100(38):e27216. doi: 10.1097/MD.0000000000027216.
Deep venous thrombosis (DVT) is associated with high mortality in coronavirus disease 2019 (COVID-19) but there remains uncertainty about the benefit of anti-coagulation prophylaxis and how to decide when ultrasound screening is indicated. We aimed to determine parameters predicting which COVID-19 patients are at risk of DVT and to assess the benefit of prophylactic anti-coagulation.Adult hospitalized patients with positive severe acute respiratory syndrome coronavirus-2 reverse transcription-polymerase chain reaction (RT-PCR) undergoing venous duplex ultrasound for DVT assessment (n = 451) were retrospectively reviewed. Clinical and laboratory data within 72 hours of ultrasound were collected. Using split sampling and a 10-fold cross-validation, a random forest model was developed to find the most important variables for predicting DVT. Different d-dimer cutoffs were examined for classification of DVT. We also compared the rate of DVT between the patients going and not going under thromboprophylaxis.DVT was found in 65 (14%) of 451 reverse transcription-polymerase chain reaction positive patients. The random forest model, trained and cross-validated on 2/3 of the original sample (n = 301), had area under the receiver operating characteristic curve = 0.91 (95% confidence interval [CI]: 0.85-0.97) for prediction of DVT in the test set (n = 150), with sensitivity = 93% (95%CI: 68%-99%) and specificity = 82% (95%CI: 75%-88%). The following variables had the highest importance: d-dimer, thromboprophylaxis, systolic blood pressure, admission to ultrasound interval, and platelets. Thromboprophylaxis reduced DVT risk 4-fold from 26% to 6% (P < .001), while anti-coagulation therapy led to hemorrhagic complications in 14 (22%) of 65 patients with DVT including 2 fatal intra-cranial hemorrhages. D-dimer was the most important predictor with area under curve = 0.79 (95%CI: 0.73-0.86) by itself, and a 5000 ng/mL threshold at 7 days postCOVID-19 symptom onset had 75% (95%CI: 53%-90%) sensitivity and 81% (95%CI: 72%-88%) specificity. In comparison with d-dimer alone, the random forest model showed 68% versus 32% specificity at 95% sensitivity, and 44% versus 23% sensitivity at 95% specificity.D-dimer >5000 ng/mL predicts DVT with high accuracy suggesting regular monitoring with d-dimer in the early stages of COVID-19 may be useful. A random forest model improved the prediction of DVT. Thromboprophylaxis reduced DVT in COVID-19 patients and should be considered in all patients. Full anti-coagulation therapy has a risk of life-threatening hemorrhage.
深静脉血栓形成(DVT)与 2019 年冠状病毒病(COVID-19)的高死亡率相关,但对于抗凝预防的益处以及何时进行超声筛查仍存在不确定性。我们旨在确定预测 COVID-19 患者发生 DVT 风险的参数,并评估预防性抗凝的益处。
回顾性分析了 451 例接受深静脉超声评估 DVT 的接受 SARS-CoV-2 逆转录聚合酶链反应(RT-PCR)检测为阳性的住院成年患者。收集了超声检查后 72 小时内的临床和实验室数据。使用拆分采样和 10 倍交叉验证,建立了随机森林模型以寻找预测 DVT 的最重要变量。研究了不同的 D-二聚体截断值以对 DVT 进行分类。我们还比较了接受和未接受血栓预防的患者之间 DVT 的发生率。
在 451 例 RT-PCR 阳性患者中,发现 65 例(14%)患有 DVT。在原始样本的 2/3(n=301)上进行训练和交叉验证的随机森林模型,在测试集(n=150)中对 DVT 的预测具有 0.91 的受试者工作特征曲线下面积(95%置信区间[CI]:0.85-0.97),敏感性为 93%(95%CI:68%-99%),特异性为 82%(95%CI:75%-88%)。最重要的变量包括:D-二聚体、血栓预防、收缩压、超声检查入院间隔和血小板。血栓预防使 DVT 风险从 26%降低至 6%(P<0.001),而抗凝治疗导致 65 例 DVT 患者中的 14 例(22%)发生出血性并发症,包括 2 例致命性颅内出血。D-二聚体本身是最重要的预测因子,曲线下面积为 0.79(95%CI:0.73-0.86),COVID-19 症状发作后 7 天的 5000ng/mL 阈值具有 75%(95%CI:53%-90%)的敏感性和 81%(95%CI:72%-88%)的特异性。与单独的 D-二聚体相比,随机森林模型在 95%敏感性时特异性为 68%,而在 95%特异性时敏感性为 44%。
D-二聚体>5000ng/mL 可准确预测 DVT,表明 COVID-19 早期定期监测 D-二聚体可能有用。随机森林模型提高了 DVT 的预测能力。在 COVID-19 患者中,血栓预防可降低 DVT 风险,应考虑在所有患者中使用。全剂量抗凝治疗有发生危及生命的出血的风险。