Impact of Workforce Challenges and Funds Flow on Cancer Clinical Trial Development and Conduct.

The conduct of clinical cancer research has faced considerable challenges in recent years, and the situation has only been exacerbated by the global pandemic. The growing complexity of clinical trials and rising administrative burdens had been causing greater expense and difficulty in recruiting and retaining an appropriately trained workforce even before the well-publicized increase in turnover caused by the pandemic. Longstanding issues such as restrictive inclusion criteria and complicated trial designs have negatively affected already low clinical trial accrual rates, limited sites capable of opening studies and enrolling patients, and worsened disparities in trial participation. Opposing these elements are efforts by ASCO and other organizations to increase affordability, access, and equity in clinical trial enrollment. To provide diverse perspectives on how these challenges are affecting cancer research as we emerge from the pandemic, we asked a panel of experienced clinical research leaders from both academic and community cancer centers to answer questions they felt most pressing about the business of conducting clinical research today and where they felt the field was moving in the near future.

Increased Productivity and Efficiency Among Cancer Center Clinical Trials Workforce during the COVID-19 Pandemic

Background: Independent of the SARS-CoV-2 pandemic, we developed a pilot program tracking productivity allowing research coordinators (RCs) to work from home. However, with the pandemic's onset all RCs were ordered to work from home starting March 25, 2020. Simultaneously, all in-person clinical trial site monitoring visits were prohibited, forcing sponsors to either halt research monitoring operations (10%) or adapt to remote monitoring (90%). We analyzed the productivity and efficiency of RCs during at home days versus in office days to better understand the impact of the pandemic on clinical trial operations. Methods: During this study period, RCs performed daily productivity tracking in a RedCap database, whether at the office or at home. Productivity was defined as total data fields entered;efficiency was defined as data fields entered in a given time period. Continuous variables were summarized using the median and interquartile range (IQR). To account for the fact that the data are clustered by RC, comparisons between working location were made using a logistic regression model with a random intercept for RC. A p-value <.05 was considered statistically significant. RCs who entered values incorrectly or who entered/exited the team during the tracking period were excluded. The data entry work was also categorized into 16 distinct disease groups for analysis. Results: There were 2,369 observations recorded by 58 RCs between March 2 and June 29, 2020. RCs spent a median of 2.75 hours (IQR 1.50-4.00) performing data entry at home, compared to a median of 3.00 hours (IQR 2.00-5.25) performing data entry in the office (P=.5). All 58 RCs recorded a total of 17,966 hours over 81 days working at home, where 24 of the RCs recorded a total of 1,169 hours over 69 days working from the office (Tables 1 and 2). For all disease groups, the median number of hours worked by RCs from home and the office were 8.00 (IQR 7.92-8.10) and 8.50 (IQR 7.91-10.00), respectively (P=.046). On average, RCs entered significantly more data fields at home (95.5, IQR 32-240) compared to at the office (75, IQR 35-145, P<.001). There was no significant difference in the number of patients for whom data were entered. There was a trend towards an increase in the median number of data fields entered per hour from home (40, IQR 20-72) compared to the office (21, IQR 13-36, P=.064, Tables 3 and 4). Among the hematology group, the median number of hours worked by RCs from home and the office were 8.00 (IQR 7.90-8.05) and 8.02 (IQR 7.92-8.36), respectively (P=.1). The median number of data fields entered by RCs from home and the office were 150 (IQR 47-336) and 74 (IQR 41-164), respectively (P<.001), and the median number of data entry hours for RCs from home and the office were 3.50 (IQR 2-5) and 2.62 (IQR 1.56-3), respectively (P=.004). There was no significant difference in the number of patients for whom data were entered or the number of data fields per hour. Among the solid tumor group, the median number of hours worked by RCs from home and the office were 8.00 (IQR 7.95-8.18) and 9.87 (IQR 7.87-10), respectively (P=.2). There was no significant difference in the number of data fields entered, the number of data entry hours, nor the number of data fields entered per hour. Hematology RCs completed a median of 150 (IQR 47-329) data fields per day while the solid tumor RCs completed a median of 65 (IQR 25-159) data fields per day. The multiple myeloma and leukemia groups completed the most data fields per day, 320 (IQR 200-650) and 202 (IQR 58.5-390), respectively (Tables 5 and 6). Total median time spent on data entry and total median time spent on all other tasks was 2.98 hours and 5.28 hours respectively, meaning 36% of an RCs work was comprised of data entry tasks. With the hematology research RCs bearing the brunt of the data entry workload, per hour, RCs completed nearly double the average amount of data fields when at home (40, IQR 20-72 vs 21, IQR 13-36). This translates into RCs being 17% more efficient overall when working at home. Conclu ions: A silver lining to the SARS-CoV-2 pandemic includes increased data entry by RCs, and virtual monitoring and site initiation visits by sponsors and contract research organizations. These have created efficiencies including a greater number of trials opened and a reduction in trial times to open, when compared to a similar time period in 2019. Preliminary employee satisfaction surveys also reveal a high degree of satisfaction when working from home. Disclosures Gerds:Apexx Oncology: Consultancy;Imago Biosciences: Research Funding;AstraZeneca/MedImmune: Consultancy;Roche/Genentech: Research Funding;Gilead Sciences: Research Funding;Incyte Corporation: Consultancy, Research Funding;Sierra Oncology: Research Funding;Celgene: Consultancy, Research Funding;Pfizer: Research Funding;CTI Biopharma: Consultancy, Research Funding. Pennell:Cota: Consultancy;Eli Lilly: Consultancy;Amgen: Consultancy;Genentech: Consultancy;Merck: Consultancy;Astrazeneca: Consultancy;BMS: Consultancy;G1 Therapeutics: Consultancy;Inivata: Consultancy. Sekeres:Takeda/Millenium: Consultancy;BMS: Consultancy;Pfizer: Consultancy.

Geriatric risk factors for serious COVID-19 outcomes among older adults with cancer: a cohort study from the COVID-19 and Cancer Consortium.

BACKGROUND: Older age is associated with poorer outcomes of SARS-CoV-2 infection, although the heterogeneity of ageing results in some older adults being at greater risk than others. The objective of this study was to quantify the association of a novel geriatric risk index, comprising age, modified Charlson comorbidity index, and Eastern Cooperative Oncology Group performance status, with COVID-19 severity and 30-day mortality among older adults with cancer. METHODS: In this cohort study, we enrolled patients aged 60 years and older with a current or previous cancer diagnosis (excluding those with non-invasive cancers and premalignant or non-malignant conditions) and a current or previous laboratory-confirmed COVID-19 diagnosis who reported to the COVID-19 and Cancer Consortium (CCC19) multinational, multicentre, registry between March 17, 2020, and June 6, 2021. Patients were also excluded for unknown age, missing data resulting in unknown geriatric risk measure, inadequate data quality, or incomplete follow-up resulting in unknown COVID-19 severity. The exposure of interest was the CCC19 geriatric risk index. The primary outcome was COVID-19 severity and the secondary outcome was 30-day all-cause mortality; both were assessed in the full dataset. Adjusted odds ratios (ORs) and 95% CIs were estimated from ordinal and binary logistic regression models. FINDINGS: 5671 patients with cancer and COVID-19 were included in the analysis. Median follow-up time was 56 days (IQR 22-120), and median age was 72 years (IQR 66-79). The CCC19 geriatric risk index identified 2365 (41·7%) patients as standard risk, 2217 (39·1%) patients as intermediate risk, and 1089 (19·2%) as high risk. 36 (0·6%) patients were excluded due to non-calculable geriatric risk index. Compared with standard-risk patients, high-risk patients had significantly higher COVID-19 severity (adjusted OR 7·24; 95% CI 6·20-8·45). 920 (16·2%) of 5671 patients died within 30 days of a COVID-19 diagnosis, including 161 (6·8%) of 2365 standard-risk patients, 409 (18·5%) of 2217 intermediate-risk patients, and 350 (32·1%) of 1089 high-risk patients. High-risk patients had higher adjusted odds of 30-day mortality (adjusted OR 10·7; 95% CI 8·54-13·5) than standard-risk patients. INTERPRETATION: The CCC19 geriatric risk index was strongly associated with COVID-19 severity and 30-day mortality. Our CCC19 geriatric risk index, based on readily available clinical factors, might provide clinicians with an easy-to-use risk stratification method to identify older adults most at risk for severe COVID-19 as well as mortality. FUNDING: US National Institutes of Health National Cancer Institute Cancer Center.

Assessment of Regional Variability in COVID-19 Outcomes Among Patients With Cancer in the United States.

Importance: The COVID-19 pandemic has had a distinct spatiotemporal pattern in the United States. Patients with cancer are at higher risk of severe complications from COVID-19, but it is not well known whether COVID-19 outcomes in this patient population were associated with geography. Objective: To quantify spatiotemporal variation in COVID-19 outcomes among patients with cancer. Design, Setting, and Participants: This registry-based retrospective cohort study included patients with a historical diagnosis of invasive malignant neoplasm and laboratory-confirmed SARS-CoV-2 infection between March and November 2020. Data were collected from cancer care delivery centers in the United States. Exposures: Patient residence was categorized into 9 US census divisions. Cancer center characteristics included academic or community classification, rural-urban continuum code (RUCC), and social vulnerability index. Main Outcomes and Measures: The primary outcome was 30-day all-cause mortality. The secondary composite outcome consisted of receipt of mechanical ventilation, intensive care unit admission, and all-cause death. Multilevel mixed-effects models estimated associations of center-level and census division-level exposures with outcomes after adjustment for patient-level risk factors and quantified variation in adjusted outcomes across centers, census divisions, and calendar time. Results: Data for 4749 patients (median [IQR] age, 66 [56-76] years; 2439 [51.4%] female individuals, 1079 [22.7%] non-Hispanic Black individuals, and 690 [14.5%] Hispanic individuals) were reported from 83 centers in the Northeast (1564 patients [32.9%]), Midwest (1638 [34.5%]), South (894 [18.8%]), and West (653 [13.8%]). After adjustment for patient characteristics, including month of COVID-19 diagnosis, estimated 30-day mortality rates ranged from 5.2% to 26.6% across centers. Patients from centers located in metropolitan areas with population less than 250 000 (RUCC 3) had lower odds of 30-day mortality compared with patients from centers in metropolitan areas with population at least 1 million (RUCC 1) (adjusted odds ratio [aOR], 0.31; 95% CI, 0.11-0.84). The type of center was not significantly associated with primary or secondary outcomes. There were no statistically significant differences in outcome rates across the 9 census divisions, but adjusted mortality rates significantly improved over time (eg, September to November vs March to May: aOR, 0.32; 95% CI, 0.17-0.58). Conclusions and Relevance: In this registry-based cohort study, significant differences in COVID-19 outcomes across US census divisions were not observed. However, substantial heterogeneity in COVID-19 outcomes across cancer care delivery centers was found. Attention to implementing standardized guidelines for the care of patients with cancer and COVID-19 could improve outcomes for these vulnerable patients.

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.

American Society of Clinical Oncology Road to Recovery Report: Learning From the COVID-19 Experience to Improve Clinical Research and Cancer Care.

This report presents the American Society of Clinical Oncology's (ASCO's) evaluation of the adaptations in care delivery, research operations, and regulatory oversight made in response to the coronavirus pandemic and presents recommendations for moving forward as the pandemic recedes. ASCO organized its recommendations for clinical research around five goals to ensure lessons learned from the COVID-19 experience are used to craft a more equitable, accessible, and efficient clinical research system that protects patient safety, ensures scientific integrity, and maintains data quality. The specific goals are: (1) ensure that clinical research is accessible, affordable, and equitable; (2) design more pragmatic and efficient clinical trials; (3) minimize administrative and regulatory burdens on research sites; (4) recruit, retain, and support a well-trained clinical research workforce; and (5) promote appropriate oversight and review of clinical trial conduct and results. Similarly, ASCO also organized its recommendations regarding cancer care delivery around five goals: (1) promote and protect equitable access to high-quality cancer care; (2) support safe delivery of high-quality cancer care; (3) advance policies to ensure oncology providers have sufficient resources to provide high-quality patient care; (4) recognize and address threats to clinician, provider, and patient well-being; and (5) improve patient access to high-quality cancer care via telemedicine. ASCO will work at all levels to advance the recommendations made in this report.

Utilization of COVID-19 Treatments and Clinical Outcomes among Patients with Cancer: A COVID-19 and Cancer Consortium (CCC19) Cohort Study.

Among 2,186 U.S. adults with invasive cancer and laboratory-confirmed SARS-CoV-2 infection, we examined the association of COVID-19 treatments with 30-day all-cause mortality and factors associated with treatment. Logistic regression with multiple adjustments (e.g., comorbidities, cancer status, baseline COVID-19 severity) was performed. Hydroxychloroquine with any other drug was associated with increased mortality versus treatment with any COVID-19 treatment other than hydroxychloroquine or untreated controls; this association was not present with hydroxychloroquine alone. Remdesivir had numerically reduced mortality versus untreated controls that did not reach statistical significance. Baseline COVID-19 severity was strongly associated with receipt of any treatment. Black patients were approximately half as likely to receive remdesivir as white patients. Although observational studies can be limited by potential unmeasured confounding, our findings add to the emerging understanding of patterns of care for patients with cancer and COVID-19 and support evaluation of emerging treatments through inclusive prospective controlled trials. SIGNIFICANCE: Evaluating the potential role of COVID-19 treatments in patients with cancer in a large observational study, there was no statistically significant 30-day all-cause mortality benefit with hydroxychloroquine or high-dose corticosteroids alone or in combination; remdesivir showed potential benefit. Treatment receipt reflects clinical decision-making and suggests disparities in medication access.This article is highlighted in the In This Issue feature, p. 1426.

Outcomes of patients with hematologic malignancies and COVID-19: a report from the ASH Research Collaborative Data Hub.

Coronavirus disease 2019 (COVID-19) is an illness resulting from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019. Patients with cancer, and especially those with hematologic malignancies, may be at especially high risk of adverse outcomes, including mortality resulting from COVID-19 infection. The ASH Research Collaborative COVID-19 Registry for Hematology was developed to study features and outcomes of COVID-19 infection in patients with underlying blood disorders, such as hematologic malignancies. At the time of this report, data from 250 patients with blood cancers from 74 sites around the world had been entered into the registry. The most commonly represented malignancies were acute leukemia (33%), non-Hodgkin lymphoma (27%), and myeloma or amyloidosis (16%). Patients presented with a myriad of symptoms, most frequently fever (73%), cough (67%), dyspnea (50%), and fatigue (40%). Use of COVID-19-directed therapies, such as hydroxychloroquine (n = 76) or azithromycin (n = 59), was common. Overall mortality was 28%. Patients with a physician-estimated prognosis from the underlying hematologic malignancy of <12 months at the time of COVID-19 diagnosis and those with relapsed/refractory disease experienced a higher proportion of moderate/severe COVID-19 disease and death. In some instances, death occurred after a decision was made to forgo intensive care unit admission in favor of a palliative approach. Taken together, these data support the emerging consensus that patients with hematologic malignancies experience significant morbidity and mortality resulting from COVID-19 infection. Batch submissions from sites with high incidence of COVID-19 infection are planned to support future analyses.

Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study.

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. FINDINGS: Of 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. FUNDING: American Cancer Society, National Institutes of Health, and Hope Foundation for Cancer Research.