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3D-PAST: Risk Assessment Model for Predicting Venous Thromboembolism in COVID-19
Yi Lee1, Qasim Jehangir1, Chun-Hui Lin2
1Department of Medicine, St. Joseph Mercy Oakland Hospital, Pontiac, MI 48341, USA.
Insights
A new risk assessment model (RAM) helps identify hospitalized COVID-19 patients at risk for venous thromboembolism (VTE). This tool aids in personalizing anticoagulation therapy for better patient outcomes.
Area of Science:
- Medical Research
- Clinical Medicine
- Cardiology
Background:
- SARS-CoV-2 infection is associated with hypercoagulability.
- Existing risk assessment models (RAMs) are insufficient for stratifying venous thromboembolism (VTE) risk in COVID-19 patients.
- There is a need for a dedicated tool to guide anticoagulation therapy.
Purpose of the Study:
- To develop and validate a simple clinical risk assessment model (RAM) for predicting VTE in hospitalized COVID-19 patients.
- To aid in individualizing thromboprophylaxis strategies.
- To improve patient management by stratifying VTE risk.
Main Methods:
- A large-cohort retrospective study involving adult patients with confirmed SARS-CoV-2 infection.
- Model development using LASSO and logistic regression on 3531 patients (March-December 2020).
- Model validation on 2508 patients (January-September 2021), defining VTE as deep vein thrombosis (DVT) or pulmonary embolism (PE).
Main Results:
- A novel RAM was created using seven common admission parameters: DVT/PE history, D-Dimer levels, low albumin, low systolic blood pressure, and tachycardia.
- The model demonstrated a sensitivity of 83% and specificity of 53%.
- Risk scores were assigned to variables, with cutoffs derived for risk stratification.
Conclusions:
- The developed RAM is a simple and robust clinical tool for predicting VTE in COVID-19 patients.
- This model can assist clinicians in personalizing thromboprophylaxis.
- It facilitates risk-stratification to guide anticoagulation decisions.
Abstract:
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 (<3.5 g/dL) = 1; Systolic Blood Pressure <120 mmHg = 1, Tachycardia (heart rate >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.
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