Risk Factors For The Development Of Deep Vein Thrombosis In Hospitalized Covid-19 Patients
Harsimran Panesar, BS1, Phillip Huynh, BS1, Jana Tancredi, MA/MSN2, Michael Wilderman, MD2, Joseph Parrillo, MD2, Gregory Simonian, MD2, David O'Connor, MD2.
1Hackensack Meridian School of Medicine, Nutley, NJ, USA, 2Hackensack University Medical Center, Hackensack, NJ, USA.
OBJECTIVE - The study aims to help generate characteristics that may predict the development of deep vein thrombosis (DVT) in COVID-19 patients in order to devise better treatment algorithms, especially those for risk stratification and management. Our understanding of predictive values is constantly evolving. Researchers have not yet come to a consensus on a definitive set of factors that contribute to the development of DVT in COVID-19 patients, and the literature suggests that previous studies may have been constrained by limitations such as sample size.
METHODS - A retrospective cohort study of 1275 COVID-19 positive patients who were admitted to a tertiary care institution from March 1, 2020 to June 1, 2020 and underwent a duplex venous ultrasound (DUS) due to clinical suspicion for deep venous thrombosis (DVT) was conducted. The groups were stratified into DVT positive and DVT negative patients and then further analyzed based on the known risk factors to increase DVT formations. Variables that were studied are included in Table 1 and were analyzed using univariate and multivariate regression.
RESULTS - Multivariable regression models showed that male gender (p<0.0001, OR=41, 95%CI=8 to >100), lower body weight (p<0.0001, OR=0.9, 95%CI=0.87 to 0.93), positive history of pulmonary embolism (PE) (p=0.01,OR=56, 95%CI=2 to >100), and high platelet count at admission (p=0.005, OR=30, 95%CI=2.8 to >100) were the best independent predictors of DVT. Surprisingly, no statistically significant findings were found between DVT positive or negative groups in respect to anticoagulant use at a prophylactic or therapeutic level.
CONCLUSIONS - Through this preliminary data collection some risk factors associated with DVT progression have been eluded. It will be important to continuously reevaluate the data as the virus evolves, and clinicians should maintain a high level of suspicion for venous thromboembolic disease.
Table 1: Variables collected and analyzed via univariate analysis
| Demographics | Medical History | Medication Use | Lab values | |
| Statically significant(p-value < 0.05) | GenderWeightBMIAdmission after 1st DUS | SepsisPulmonary embolismAtrial fibrillation | Remdesivir Steroids | INR peakPTT peakD-dimer at admissionD-dimer peakHbg admission Hbg lowest Plt at admissionPlt peakLD at admissionLD peak |
| Statically not significant (p-value > 0.05) | Age HeightMortalitySmoking status | StrokeCoronary Artery DiseaseDeep Venous ThrombosisUnderwent ChemotherapyIntubation | Hydroxychloroquine Anticoagulants:Therapeutic heparinProphylactic heparinTherapeutic LovenoxProphylactic Lovenox | INR at admissionPTT at admissionCreatinine admissionCreatinine peakCRP at admissionCRP peakIL-6 at admissionIL-6 PeakFerritin at admissionFerritin peak |
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