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Description
In late 2019, COVID was initially discovered in Wuhan, China, and the COVID-19 pandemic primarily began in early 2020. Along with respiratory distress, COVID-19 patients had an increased risk of forming abnormal clotting. In ICU COVID-19 patients, abnormal clotting increases the risk of mortality by approximately 74% (Montiel et al., 2022). The aim for this meta-narrative review was to identify what hemostatic parameters are predictive of coagulopathy in COVID-19 patients. Determining predictive markers of coagulopathy in COVID-19 infection may allow for early identification of severe cases before bleeding and thrombotic manifestations occur. The review included articles primarily from PubMed and were published between 2020 and 2022. After screening for eligibility, seven articles were deemed fit to be included. Our findings indicate that elevated D-dimer levels were the most common predictive hemostatic parameter utilized, along with elevated Von Willebrand Factor, elevated Factor VIII, and decreased fibrinogen levels. COVID-19 patients that presented with these parameters upon admission were highly likely to experience clotting events such as deep vein thrombosis and pulmonary embolism. Although many of the articles in this review focused on elevated D-dimer as an early marker of coagulopathy, one study found that elevated soluble thrombomodulin was the best predictor of coagulopathy in COVID patients. Future research will be needed to confirm soluble thrombomodulin’s ability as a predictive parameter and compare its suggestive power to D-dimer levels. Clinical trials will also be needed to assess how these predictive markers can be used to inform prophylactic treatment in COVID-19 patients.
DOI
https://doi.org/10.52519/00114
Program
Clinical Laboratory Science
Publication Date
2023
Recommended Citation
Bedoy, Arili A.; Chapa, Adriana L.; Enriquez, Jose; Saquin, Christy C.; Tran, Uyen N.; and Trevizo, Ashley N., "Predictive Markers of Coagulopathy in COVID-19 Infection: A Meta Narrative Review" (2023). Research Methods Poster Session 2023. 6.
doi:https://doi.org/10.52519/00114
