A Framework for Promoting Diversity, Equity, and Inclusion in Genetics and Genomics Research.

IMPORTANCE: Research into the genetic and genomic ("genomics") foundations of disease is central to our understanding of disease prevention, early detection, diagnostic accuracy, and therapeutic intervention. Inequitable participation in genomics research by historically excluded populations limits the ability to translate genomic knowledge to achieve health equity and ensure that findings are generalizable to diverse populations. OBSERVATIONS: We propose a novel framework for promoting diversity, equity, and inclusion in genomics research. Building on principles of community-based participatory research and collective impact frameworks, the framework can guide our understanding of the social, cultural, health system, policy, community, and individual contexts in which engagement and genomics research are being done. Our framework highlights the involvement of a multistakeholder team, including the participants and communities to be engaged, to ensure robust methods for recruitment, retention, return of genomic results, quality of engagement, follow-up, and monitoring of participants. CONCLUSIONS AND RELEVANCE: The proposed engagement framework will guide investigators in optimizing equitable representation in research and enhancing the rigor of genomics investigation.

The CoVID-TE risk assessment model for venous thromboembolism in hospitalized patients with cancer and COVID-19.

BACKGROUND: Hospitalized patients with COVID-19 have increased risks of venous (VTE) and arterial thromboembolism (ATE). Active cancer diagnosis and treatment are well-known risk factors; however, a risk assessment model (RAM) for VTE in patients with both cancer and COVID-19 is lacking. OBJECTIVES: To assess the incidence of and risk factors for thrombosis in hospitalized patients with cancer and COVID-19. METHODS: Among patients with cancer in the COVID-19 and Cancer Consortium registry (CCC19) cohort study, we assessed the incidence of VTE and ATE within 90 days of COVID-19-associated hospitalization. A multivariable logistic regression model specifically for VTE was built using a priori determined clinical risk factors. A simplified RAM was derived and internally validated using bootstrap. RESULTS: From March 17, 2020 to November 30, 2020, 2804 hospitalized patients were analyzed. The incidence of VTE and ATE was 7.6% and 3.9%, respectively. The incidence of VTE, but not ATE, was higher in patients receiving recent anti-cancer therapy. A simplified RAM for VTE was derived and named CoVID-TE (Cancer subtype high to very-high risk by original Khorana score +1, VTE history +2, ICU admission +2, D-dimer elevation +1, recent systemic anti-cancer Therapy +1, and non-Hispanic Ethnicity +1). The RAM stratified patients into two cohorts (low-risk, 0-2 points, n = 1423 vs. high-risk, 3+ points, n = 1034) where VTE occurred in 4.1% low-risk and 11.3% high-risk patients (c statistic 0.67, 95% confidence interval 0.63-0.71). The RAM performed similarly well in subgroups of patients not on anticoagulant prior to admission and moderately ill patients not requiring direct ICU admission. CONCLUSIONS: Hospitalized patients with cancer and COVID-19 have elevated thrombotic risks. The CoVID-TE RAM for VTE prediction may help real-time data-driven decisions in this vulnerable population.