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      423. Development and Validation of a Predictive Model of New-Onset Deep Vein Thrombosis/Pulmonary Embolism Among Hospitalized COVID-19 Patients: A Retrospective Cohort Study

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      , MD, , MD, , MD, , MD
      Open Forum Infectious Diseases
      Oxford University Press

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          Abstract

          Background

          COVID-19 can increase the risk of thromboembolism events, such as deep vein thrombosis (DVT) and pulmonary embolism (PE). This study aimed to develop a risk prediction model for new-onset DVT/PE in hospitalized COVID-19 patients based on baseline characteristics, major complaints, and lab values.

          Methods

          Retrospective data from 671 hospitalized COVID-19 patients were collected between March and October 2020. After excluding missing values, 241 cases were included in the final analysis (Table 1). Lasso regression was used to select related variables, and a prediction model was built using logistic regression (Figure 1). A nomogram was established based on multivariate analysis, and the model was validated using the bootstrap method.

          Results

          Among the 241 hospitalized patients with COVID-19 infection, 18 (7.5%) developed new-onset DVT/PE during hospitalization. The final prediction model included the history of diabetes mellitus, chills, sore throat, anorexia, hemoglobin, and D-dimer levels on admission. A predictive nomogram model for new-onset DVT/PE based on these variables was built (Figure 2). Our prediction model showed good predictive performance with an area under the curve (AUC) of 0.89 and a bootstrap-corrected AUC of 0.82 (95% CI: 0.767-0.873) (Figure 3). The calibration plots demonstrated good agreement between the estimated probability and the actual observation (Figure 4A). Decision curve analysis shows the nomogram is clinically useful (Figure 4B).

          Conclusion

          The developed model for new-onset DVT/PE in hospitalized COVID-19 patients based on baseline characteristics, major complaints, and lab values had good discrimination and calibration. It may help identify high-risk patients who require close monitoring and prophylactic anticoagulation. Future studies are needed to validate the model in different populations and settings.

          Disclosures

          All Authors: No reported disclosures

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          Author and article information

          Contributors
          Journal
          Open Forum Infect Dis
          Open Forum Infect Dis
          ofid
          Open Forum Infectious Diseases
          Oxford University Press (US )
          2328-8957
          December 2023
          27 November 2023
          27 November 2023
          : 10
          : Suppl 2 , IDWeek 2023 Abstracts
          : ofad500.493
          Affiliations
          Ascension Saint Francis Hospital , Evanston, Illinois
          Stanford University School of Medicine , Palo Alto, California
          Ascension Health Saint Francis Hospital , Evanston, Illinois
          Ascension Health , Evanston, Illinois
          Author notes

          Session: 44. COVID-19: Complications, Coinfections and Clinical Outcomes

          Thursday, October 12, 2023: 12:15 PM

          Article
          ofad500.493
          10.1093/ofid/ofad500.493
          10676992
          c52080e0-849a-44d2-9c3e-9b77e06f5125
          © The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America.

          This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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          AcademicSubjects/MED00290

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