Background Data on patients with COVID-19 who have cancer are lacking. Here we characterise the outcomes of a cohort of patients with cancer and COVID-19 and identify potential prognostic factors for mortality and severe illness.Methods In this cohort study, we collected de-identified data on patients with active or previous malignancy, aged 18 years and older, with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from the USA, Canada, and Spain from the COVID-19 and Cancer Consortium (CCC19) database for whom baseline data were added between March 17 and April 16, 2020. We collected data on baseline clinical conditions, medications, cancer diagnosis and treatment, and COVID-19 disease course. The primary endpoint was all-cause mortality within 30 days of diagnosis of COVID-19. We assessed the association between the outcome and potential prognostic variables using logistic regression analyses, partially adjusted for age, sex, smoking status, and obesity. This study is registered with ClinicalTrials.gov, NCT04354701, and is ongoing. FindingsOf 1035 records entered into the CCC19 database during the study period, 928 patients met inclusion criteria for our analysis. Median age was 66 years (IQR 57-76), 279 (30%) were aged 75 years or older, and 468 (50%) patients were male. The most prevalent malignancies were breast (191 [21%]) and prostate (152 [16%]). 366 (39%) patients were on active anticancer treatment, and 396 (43%) had active (measurable) cancer. At analysis (May 7, 2020), 121 (13%) patients had died. In logistic regression analysis, independent factors associated with increased 30-day mortality, after partial adjustment, were: increased age (per 10 years; partially adjusted odds ratio 1•84, 95% CI 1•53-2•21), male sex (1•63, 1•07-2•48), smoking status (former smoker vs never smoked: 1•60, 1•03-2•47), number of comorbidities (two vs none: 4•50, 1•33-15•28), Eastern Cooperative Oncology Group performance status of 2 or higher (status of 2 vs 0 or 1: 3•89, 2•11-7•18), active cancer (progressing vs remission: 5•20, 2•77-9•77), and receipt of azithromycin plus hydroxychloroquine (vs treatment with neither: 2•93, 1•79-4•79; confounding by indication cannot be excluded). Compared with residence in the US-Northeast, residence in Canada (0•24, 0•07-0•84) or the US-Midwest (0•50, 0•28-0•90) were associated with decreased 30-day all-cause mortality. Race and ethnicity, obesity status, cancer type, type of anticancer therapy, and recent surgery were not associated with mortality. Interpretation Among patients with cancer and COVID-19, 30-day all-cause mortality was high and associated with general risk factors and risk factors unique to patients with cancer. Longer follow-up is needed to better understand the effect of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments.

Risk of venous thromboembolism (VTE)is elevated in cancer, but individual risk factors cannot identify a sufficiently highrisk group of outpatients for thromboprophylaxis. We developed a simple model for predicting chemotherapy-associated VTE using baseline clinical and laboratory variables. The association of VTE with multiple variables was characterized in a derivation cohort of 2701 cancer outpatients from a prospective observational study. A risk model was derived and validated in an independent cohort of 1365 patients from the same study. Five predictive variables were identified in a multivariate model: site of cancer (2 points for very high-risk site, 1 point for highrisk site), platelet count of 350 ؋ 10 9 /L or more, hemoglobin less than 100 g/L (10 g/dL) and/or use of erythropoiesisstimulating agents, leukocyte count more than 11 ؋ 10 9 /L, and body mass index of 35 kg/m 2 or more (1 point each). Rates of VTE in the derivation and validation cohorts, respectively, were 0.8% and 0.3% in low-risk (score ‫؍‬ 0), 1.8% and 2% in intermediate-risk (score ‫؍‬ 1-2), and 7.1% and 6.7% in high-risk (score > 3) category over a median of 2.5 months (Cstatistic ‫؍‬ 0.7 for both cohorts). This model can identify patients with a nearly 7% short-term risk of symptomatic VTE and may be used to select cancer outpatients for studies of thromboprophylaxis. IntroductionCancer and antineoplastic therapy are frequently complicated by the development of venous thromboembolism (VTE). Several risk factors for cancer-associated VTE have been described in recent studies and include primary site of cancer, presence of metastatic disease, and use of antineoplastic therapy including chemotherapy, hormonal therapy, surgery, and erythropoiesis-stimulating agents. [1][2][3][4] Cancer patients on active therapy are at greatest risk for development of VTE. In a population-based study, cancer was associated with a 4.1-fold greater risk of thrombosis, whereas the use of chemotherapy increased the risk 6.5-fold. 5,6 In women with stage II breast cancer, the risk of VTE decreases dramatically after chemotherapy is completed. 7,8 The occurrence of VTE has important implications for the cancer patient including requirement for chronic anticoagulation, possible delays in delivering chemotherapy, a high risk of recurrent VTE, risk of bleeding complications on anticoagulation, decreased quality of life, and consumption of health care resources. 9,10 Furthermore, cancer patients with VTE have a 2-fold or greater increase in mortality compared with cancer patients without VTE, even after adjusting for stage. 11,12 Indeed, thromboembolism is a leading cause of death in cancer patients. 13 Primary VTE prophylaxis can reduce deep vein thrombosis (DVT), pulmonary embolism (PE), and fatal PE in several highrisk populations such as hospitalized patients or in the postoperative setting. [14][15][16][17][18] In the cancer population, identification of patients most at risk for VTE followed by institution of effective prophylaxis could improve morbidity, m...

The results of this evidence-based methodology were incorporated into the AJCC melanoma staging as described in the companion publication.