3D-PAST: Risk Assessment Model for Predicting Venous Thromboembolism in COVID-19

Autor: Yi Lee, Qasim Jehangir, Chun-Hui Lin, Pin Li, Anupam A. Sule, Laila Poisson, Venugopal Balijepally, Abdul R. Halabi, Kiritkumar Patel, Geetha Krishnamoorthy, Girish B. Nair
Rok vydání: 2022
Předmět:
Zdroj: Journal of Clinical Medicine; Volume 11; Issue 14; Pages: 3949
ISSN: 2077-0383
DOI: 10.3390/jcm11143949
Popis: Hypercoagulability is a recognized feature in SARS-CoV-2 infection. There exists a need for a dedicated risk assessment model (RAM) that can risk-stratify hospitalized COVID-19 patients for venous thromboembolism (VTE) and guide anticoagulation. We aimed to build a simple clinical model to predict VTE in COVID-19 patients. This large-cohort, retrospective study included adult patients admitted to four hospitals with PCR-confirmed SARS-CoV-2 infection. Model training was performed on 3531 patients hospitalized between March and December 2020 and validated on 2508 patients hospitalized between January and September 2021. Diagnosis of VTE was defined as acute deep vein thrombosis (DVT) or pulmonary embolism (PE). The novel RAM was based on commonly available parameters at hospital admission. LASSO regression and logistic regression were performed, risk scores were assigned to the significant variables, and cutoffs were derived. Seven variables with assigned scores were delineated as: DVT History = 2; High D-Dimer (>500–2000 ng/mL) = 2; Very High D-Dimer (>2000 ng/mL) = 5; PE History = 2; Low Albumin (100 bpm) = 1. The model had a sensitivity of 83% and specificity of 53%. This simple, robust clinical tool can help individualize thromboprophylaxis for COVID-19 patients based on their VTE risk category.
Databáze: OpenAIRE
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