Back

Coronavirus-19 and coagulopathy: A Systematic Review

Lee, S. G.; Fralick, M.; Tang, G.; Tse, B.; Baumann Kreuziger, L.; Cushman, M.; Juni, P.; Sholzberg, M.

2021-01-06 hematology
10.1101/2021.01.05.20248202 medRxiv
Show abstract

BackgroundUnderstanding the association between Coronavirus Disease 2019 (COVID-19) and coagulopathy may assist clinical prognostication, and influence treatment and outcomes. We aimed to systematically describe the relationship between hemostatic laboratory parameters and important clinical outcomes among adults with COVID-19. MethodsA systematic review of randomized clinical trials, observational studies and case series published in PubMed (Medline), EMBASE, and CENTRAL from December 1, 2019 to March 25, 2020. Studies of adult patients with COVID-19 that reported at least one hemostatic laboratory parameter were included. ResultsData were extracted from 57 studies (N=12,050 patients) that met inclusion criteria. The average age of patients was 52 years and 45% were women. Of the included studies, 92.7% (N=38/41 studies) reported an average platelet count [≥] 150 x 109/L, 68.2% (N=15/22 studies) reported an average prothrombin time (PT) between 11-14 s, 55% (N=11/20 studies) reported an average activated partial thromboplastin time (aPTT) between 25-35 s, and 34.4% (N=11/32 studies) reported a D-dimer concentration above the upper limit of normal (ULN). Eight studies (7 cohorts and 1 case series) reported hemostatic lab values for survivors versus non-survivors. Among non-survivors, D-dimer concentrations were reported in 4 studies and all reported an average above the ULN. InterpretationMost patients had a normal platelet count, elevated D-dimer, PT and aPTT values in the upper reference interval; D-dimer elevation appeared to correlate with poor outcomes. Further studies are needed to better correlate these hemostatic parameters with the risk of adverse outcomes such as thrombosis and bleeding.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.