Antithrombotic Use Patterns in COVID-19 Patients from Spain: A Real-World Data Study.

Antithrombotics have been widely used to treat and prevent COVID-19-related thrombosis; however, studies on their use at population levels are limited. We aimed to describe antithrombotic use patterns during the pandemic in Spanish primary care and hospital-admitted patients with COVID-19. Methods: A real-world data study was performed. Data were obtained from BIFAP's electronic health records. We investigated the antithrombotic prescriptions made within ±14 days after diagnosis between March 2020 and February 2022, divided their use into prior and new/naive groups, and reported their post-discharge use. Results: We included 882,540 individuals (53.4% women), of whom 78,499 were hospitalized. The median age was 44.7 (IQR 39-59). Antithrombotics were prescribed in 37,183 (4.6%) primary care subjects and 42,041 (53.6%) hospital-admitted patients, of whom 7505 (20.2%) and 20,300 (48.3%), respectively, were naive users. Prior users were older and had more comorbidities than new users. Enoxaparin was the most prescribed antithrombotic in hospitals, with higher prescription rates in new than prior users (2348.2, IQR 2390-3123.1 vs. 1378, IQR 1162-1751.6 prescriptions per 10,000 cases, p = 0.002). In primary care, acetylsalicylic acid was the most used antithrombotic, with higher use rates in prior than in naïve users. Post-discharge use occurred in 6686 (15.9%) subjects (median use = 10 days, IQR 9-30). Conclusions: Our study identified a consensus on prescribing antithrombotics in COVID-19 patients, but with low use rates in hospitals.

A Clinical Prediction Rule for Thrombosis in Critically Ill COVID-19 Patients: Step 1 Results of the Thromcco Study.

The incidence of thrombosis in COVID-19 patients is exceptionally high among intensive care unit (ICU)-admitted individuals. We aimed to develop a clinical prediction rule for thrombosis in hospitalized COVID-19 patients. Data were taken from the Thromcco study (TS) database, which contains information on consecutive adults (aged ≥ 18) admitted to eight Spanish ICUs between March 2020 and October 2021. Diverse logistic regression model analysis, including demographic data, pre-existing conditions, and blood tests collected during the first 24 h of hospitalization, was performed to build a model that predicted thrombosis. Once obtained, the numeric and categorical variables considered were converted to factor variables giving them a score. Out of 2055 patients included in the TS database, 299 subjects with a median age of 62.4 years (IQR 51.5-70) (79% men) were considered in the final model (SE = 83%, SP = 62%, accuracy = 77%). Seven variables with assigned scores were delineated as age 25-40 and ≥70 = 12, age 41-70 = 13, male = 1, D-dimer ≥ 500 ng/mL = 13, leukocytes ≥ 10 × 103/µL = 1, interleukin-6 ≥ 10 pg/mL = 1, and C-reactive protein (CRP) ≥ 50 mg/L = 1. Score values ≥28 had a sensitivity of 88% and specificity of 29% for thrombosis. This score could be helpful in recognizing patients at higher risk for thrombosis, but further research is needed.