Author Contributions: Drs Lamont and Katriel had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Background: Past studies have indicated a potential racial disparity in Multiple Myeloma (MM) survival between black and white patients (Costa et al., 2017; Marinac et al., 2020), an issue compounded by minority underrepresentation in clinical trials (Ailawadhi et al., 2018). To better understand how racial disparities affect both MM survival and access to treatment, we performed an analysis of pooled clinical trial (CT) and Real-World EMR Data (RWD). Methods: Eligible Phase II and III open-label MM clinical trials were identified from the Medidata Enterprise Data Store, which comprises over 22,000 historical clinical trials, for de-identified aggregate analyses. De-identified Oncology RWD was sourced from the Guardian Research Network of integrated delivery systems from 2016 to 2020. Baseline characteristics were analyzed in both cohorts. Race was categorized as black, white, or other. Overall Survival (OS) was assessed using Kaplan-Meier analysis. In the RWD, therapy utilization was assessed by race. Results: The RWD contained 5871 patients, with 17.5% black, 78.3% white, and 4.2% other race. Median age in years at diagnosis was 69 for blacks, 72 for whites, and 70 for other races. The gender breakdown was 54.2% female in blacks, 46.0% in whites, 45.9% in those of other races respectively. Median number of prior regimens was 2, with no differences between racial groups. The CT data contained 851 patients, with 1.4% black, 93.5% white, and 5.1% other race. Median age in years at diagnosis was 62 for blacks, 58 for whites, and 55 for other races. The gender breakdown was 33.3% female in blacks, 43.5% in whites, and 46.7% in those of other races respectively. Median number of prior regimens was 5, with no differences between racial groups. There was no statistically significant difference in OS between racial groups in either dataset. In the CT data with median follow-up of 7.8 years, survival from date of diagnosis to last visit or date of death was 25% for blacks and 18% for whites. Currently, in the RWD, 3-year survival comparing blacks to whites is 85% to 83%. The proportion of treated RWD patients appears to be similar between black and white patient groups, with 56% of white and 53% of black patients receiving 1st line therapy, and 33% and 31% receiving 2nd line therapy, respectively. Among newer therapy modalities, white patients had a higher utilization of targeted antibody agent daratumumab (8.7% utilization among whites, 5.2% in blacks, p<0.001), and although not statistically different, proteasome inhibitor carfilzomib use was also higher among whites compared to blacks (6.5% versus 5.5%). Mono daratumumab and ixazomib were used as 1st-line therapy in white patients, while these agents were administered in combination with other treatments in black patients. Adjusting for age and novel therapy use, there was also a suggestion that treatment initiation after diagnosis occurred earlier in whites than blacks (median 1.1 years vs. 1.6 years, p=0.3). Conclusions: Though there were no demonstrated differences in survival between racial groups in either dataset, the RWD suggested differences in treatment utilization, with underutilization of novel therapies like proteasome inhibitors and targeted antibody therapy and later treatment initiation in blacks. Previous studies (Fiala et al., 2017) have noted similar trends, which suggest that therapeutic advances may not be equitably available to all racial groups. This observation could not be replicated in CT data, but merits further exploration. Despite black patients making up 17.5% of patients in the RWD, a racial distribution consistent with published literature (Rosenberg et al., 2015), black patients made up only 1.3% of patients in the CT data. Furthermore, in the CT data, the median age of black patients was older than that of the white and other race groups (62 years compared to 58 and 55, respectively). This observation is magnified by evidence in both the RWD and other datasets (Fillmore et al., 2019) that shows a younger age of onset in black MM patients. Given the strong correlation between age and poorer outcomes in MM (Ludwig et al.,2008), it is possible that these clinical trials are not capturing a representative black patient population, and results may not be generalizable to other groups. Recruitment of black patients should remain a priority in clinical studies in order to effectively assess racial disparities in MM treatment access and survival. Disclosures Rusli: Acorn AI by Medidata, a Dassault Systemes Company: Current Employment, Current equity holder in publicly-traded company. Diao:Acorn AI by Medidata, a Dassault Systemes Company: Current Employment. Liu:Acorn AI by Medidata, a Dassault Systemes Company: Current Employment. Kelkar:Acorn AI by Medidata, a Dassault Systemes Company: Current Employment. Ensign:Acorn AI by Medidata, a Dassault Systemes Company: Current Employment, Current equity holder in publicly-traded company. Watson:Guardian Research Network, Inc.: Current Employment. Galaznik:Acorn AI by Medidata, a Dassault Systemes Company: Current Employment, Current equity holder in publicly-traded company.
6589 Background: While patients with cancer are known to be at increased risk of infection in part due to the immunocompromising nature of cancer treatments, recent data indicate a particularly high risk for COVID-19 infection and poor outcomes (Wang et al., 2020). A recent study (Meltzer et al., 2020) demonstrated Vitamin D deficiency may increase risk of COVID-19 infection, and a small randomized controlled trial in Spain reported significant improvement in mortality among hospitalized patients treated with calcifediol. Vitamin D deficiency has been reported in two leading causes of cancer deaths: breast and prostate. In this study, we performed a retrospective cohort analysis on nationally representative electronic medical records (EMR) to assess whether Vitamin D deficiency affects risk of COVID-19 among these patients. Methods: Patients with breast (female) or prostate (male) cancer were identified between 3/1/2018 and 3/1/2020 from EMR data provided pro-bono by the COVID-19 Research Database ( covid19researchdatabase.org ). Patients with an ICD-10 code for Vitamin D deficiency or < 20ng/mL 20(OH)D laboratory result within 12 months prior to 3/1/2020 were classified as Vitamin D deficient. COVID-19 diagnosis was defined using ICD-10 codes and laboratory results for COVID-19 at any time after 3/1/2020. Logistic regressions, adjusting for baseline demographic and clinical characteristics, were conducted to estimate the effect of Vitamin D deficiency on COVID-19 incidence in each cancer cohort. Results: A total of 16,287 breast cancer and 14,919 prostate cancer patients were included in the study. The average age was 68.9 years in the breast cancer cohort and 73.6 years in the prostate cancer cohort. The breast cancer cohort consisted of 85% Whites, 13% Black or African Americans, and less than 5% of other races. A similar race distribution was observed in the prostate cancer cohort. Unadjusted analysis showed the risk of COVID-19 was higher among Vitamin D deficient patients compared to non-deficient patients in both cohorts (breast: OR = 1.60 [95% C.I.: 1.15, 2.20]; prostate: OR = 1.59 [95% C.I.: 1.08, 2.33]). Similar findings were observed when assessed in subgroups of patients with newly diagnosed cancer in the dataset, as well as after adjusting for baseline characteristics. Conclusions: Our study suggests breast and prostate cancer patients may have an elevated risk of COVID-19 infection if Vitamin D deficient. These results support findings by Meltzer et al., 2020 demonstrating a relationship between Vitamin D deficiency and COVID-19 infection. While a randomized clinical trial is warranted to confirm the role for Vitamin D supplementation in preventing COVID-19, our study underscores the importance of monitoring Vitamin D levels across and within cancer populations, particularly in the midst of the global COVID-19 pandemic.
BACKGROUND: Despite an increasing number of treatment options available and in development, Relapsed-Refractory Multiple Myeloma (RRMM) remains an incurable disease with survival less than 12 months (Kumar SK et al., 2012). In a recent study by Moreau, et al. (2016), a relationship between response and survival was demonstrated in RRMM patients treated with pomalidomide. Understanding the relationships between initial response and long-term prognosis can potentially inform patient treatment changes or guide development of new therapeutic compounds. In a prior presentation by Berry, et al. (2017) clinical trial Study Data Tabulation Data Model (STDM) standards were used to effectively pool clinical trial data in Acute Myeloid Leukemia (AML) to show correlations between response and survival. OBJECTIVES: In this study, we expand upon the analysis of Moreau, et al. (2016) in a pooled clinical trial dataset of RRMM patients. Within this expanded, standardized patient pool, we assess the relationship between response, progression and survival both overall and within patient sub-populations based on patient profiles and prior treatment regimens. METHODS: A retrospective pooled analysis was conducted in a dataset from the Medidata Enterprise Data Store. Subjects were selected based on the inclusion/exclusion criteria from the NIMBUS trial (Moreau et al., 2016). Descriptive statistics were calculated to characterize differences between the overall pooled population and the study group. Response, Progression-free survival (PFS), and Overall Survival (OS) were extracted. Patients were stratified by several covariates including age, gender, number of prior regimens, and prior treatments received. Log-rank tests were conducted to compare PFS and OS in patient sub-populations. Both survival measures were assessed at 90, 180, and 240 days after first day of patient's most recent regimen. Cox proportional hazard models were developed to assess predictors of PFS and OS. Safety was characterized for common potentially treatment-limiting adverse events, such as leukopenia, neutropenia, and thrombocytopenia. Factors associated with development of neutropenia were assessed using logistic regression. Covariates included patient demographics, comorbidities, and treatment regimens (current and prior). RESULTS: Within the pooled analysis, PFS and OS rates were consistent with published literature rates, at ~4 months and ~12 months, respectively. Pooled analysis demonstrated a significant association between response, PFS, and OS. Results were consistent with findings of Moreau, et al. (2016), showing little difference between patients with Stable Disease and Partial Response, and lower overall survival in patients with Progressive Disease versus Stable Disease. Neutropenia was seen in approximately one-fourth of overall patients, and was associated with male patients, older age, and treatment regimen. CONCLUSIONS: The use of SDTM for pooled clinical trial analyses represents an effective way to overcome individual trial sample size limitations, expanding the range of populations, relative treatment outcomes, and safety event rates that can be studied. By working directly with individual patient-level data, there is also a potential for greater matching between trials than with meta-analysis approaches using aggregated data. Disclosures Galaznik: Medidata Solutions: Employment. Rusli:Medidata Solutions: Employment. Davi:Medidata Solutions: Employment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.