Session – Digital Health and Healthcare
Post-Anticoagulant D-dimer as a Highly Prognostic Biomarker of COVID-19 Mortality
Department of Population Health Science and Policy
Icahn School of Medicine at Mount Sinai
1590 Anderson Ave, APT 7K, Fort Lee, NJ, 07024
Dr. Xiaoyu Song is an Assistant Professor of Biostatistics in the Department of Population Health Science and Policy at Icahn School of Medicine at Mount Sinai. She obtained her DrPH degree in Biostatistics from Columbia University in the City of New York in 2015. Her research interests are mainly in developing statistical methods for genomics and other complex biological, medical, and public health data. Dr. Song is an active member of the NCI’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) to accelerate our understanding of the proteogenomics basis of cancer. She has developed many statistical tools for integrative analysis of multi-omic data, high-dimensional data, and quantile regression, and have authored many studies published in top journals like Cell to apply these tools for the association, network, integration, and prediction analysis for understanding the genomic basis of cancer and other complex human diseases.
Importance: Clinical biomarkers that accurately predict mortality are needed for the effective management of patients with severe COVID-19 illness. Objective: To determine whether D-dimer levels after anticoagulation treatment are predictive of in-hospital mortality. Design: Retrospective study using electronic health record data. Setting: A large New York City hospital network serving a diverse, urban patient population. Participants: Adult patients hospitalized for severe COVID-19 infection who received therapeutic anticoagulation for thromboprophylaxis between February 25, 2020, and May 31, 2020. Exposures: Mean and trend of D-dimer levels in the 3 days following the first therapeutic dose of anticoagulation. Main Outcomes: In-hospital mortality versus discharge. Results: 1835 adult patients (median age, 67 years [interquartile range, 57-78]; 58% male) with PCR-confirmed COVID-19 who received therapeutic anticoagulation during hospitalization were included. 74% (1365) of patients were discharged and 26% (430) died in hospital. The study cohort was divided into four groups based on the mean D-dimer levels and its trend following anticoagulation initiation, with significantly different in-hospital mortality rates (p<0.001): 49% for the high mean-increase trend (HI) group; 27% for the high-decrease (HD) group; 21% for the low-increase (LI) group; and 9% for the low-decrease (LD) group. Using penalized logistic regression models to simultaneously analyze 67 variables (baseline demographics, comorbidities, vital signs, laboratory values, D-dimer levels), post-anticoagulant D-dimer groups had the highest adjusted odds ratios (ORadj) for predicting in-hospital mortality. The ORadj of in-hospital death among patients from the HI group was 6.58 folds (95% CI 3.81-11.16) higher compared to the LD group. The LI (ORadj: 4.06, 95% CI 2.23-7.38) and HD (ORadj: 2.37; 95% CI 1.37-4.09) groups were also associated with higher mortality compared to the LD group. Conclusions and Relevance: D-dimer levels and its trend following the initiation of anticoagulation have high and independent predictive value for in-hospital mortality. This novel prognostic biomarker should be incorporated into management protocols to guide resource allocation and prospective studies for emerging treatments in hospitalized COVID-19 patients.