Bodepudi, Sri Lekha

Background: Survival of patients with MM has improved significantly over recent years due to therapeutic advancements. Population-level data has shown that a substantial proportion of MM patients may die early after their diagnosis and real-world evidence regarding early mortality (≤6 months after MM diagnosis) has been conflicting. We evaluated the trends, causes and predictors of early mortality among newly diagnosed MM (NDMM) patients in the real world. Methods: We used Surveillance Epidemiology and End Results (SEER) database, to identify all adult patients (age ≥ 18 years) who were diagnosed of MM from 1975 to 2015. Data on age, gender, geographical regions, year of diagnosis, survival time, vital status at 6 months after diagnosis and cause of death (COD) were extracted. Ages were categorized as quartiles while period of diagnosis for multivariate analysis were categorized by running a joinpoint analysis separately for the all-cause and myeloma-specific mortality. To look at trends in early mortality, we estimated proportion of deaths for each year and plotted it as a scatterplot against the year of diagnosis and fitted it in the join point regression model by applying the NCI's Join point Regression Program, Version 4.5.0.1. By this method we identified the year(s) when a trend change was produced, calculated the annual percentage change (APC) in rates between trend-change points, and the average annual percentage change (AAPC) in the whole period studied. To correct for known errors with COD attribution, we applied a special COD variable which has been recently developed by the SEER program to indicate if the death was due to the primary cancer diagnosis or other causes. A multivariate analysis was conducted to analyze the predictors of all-cause and myeloma-specific mortality within 6 months by fitting into logistic regression model. Results: Of 90,975 NDMM patients, early mortality due to any cause was noted in 18,810 (20.7%). For the whole cohort, median age was 68 years, majority of the patients were males (53.1%), of non-Hispanic white (NHW) race/ethnicity (65.5%) and were diagnosed in SEER registries in the western region (53.9%). An overall survival (OS) of ≤6 months was seen for approximately 22% of NHW but 18% of non-Hispanic blacks (NHB) and Hispanics. OS of ≤6 months was seen in 10% of patients in the first quartile of age (<60 years) as compared to 36% of patients in the 4th quartile (≥78 years). By geographical regions, 23.3% of MM patients in the Midwest had ≤6-month OS as compared to only 18.4% in the Northeast. The most common early mortality COD was MM in 74.7% of the patients, followed by cardiovascular (11.9%), other (8.8%) and infectious causes (3.1%). The overall pattern of COD at 6 months was similar to COD at any time in the whole cohort. Joinpoint regression analysis of trends data resulted in 1 joinpoint for all-cause 6-month mortality while 2 joinpoints were noticed for myeloma-specific mortality (Figure 1). A steep decline in mortality was noticed in the more recent time periods for both, all-cause (2006-2015) and myeloma-specific (2003-2015) mortality rates with an AAPC of 3.93 (95%CI p<0.001) for all-cause and 3.95 (95%CI p<0.001) for myeloma-specific mortality. Prior to 2006, the decrease in all-cause mortality was only modest whereas decrease in myeloma-specific mortality witnessed a sharp decline from 1975-1987 as well. On MVA, advanced age (p<0.001), NHW race/ethnicity and Midwest geographic region (p<0.001) were associated with higher likelihood of mortality while female gender (p<0.001), more recent period of diagnosis (p<0.001), and "other" races (not NHW, NHB or Hispanics) (p<0.001) were associated with lower risk of mortality. Conclusion: Our results show that early mortality due to MM remains high despite therapeutic improvements and there are significant gender, racial and geographic disparities for this phenomenon. However, significant declines in all-cause and MM-specific early mortality in the more recent times are promising. The risk of early mortality is higher among men, older patients, NHW and Midwest region. A focus on interventions to reduce early mortality, including early diagnosis, use of efficacious anti-MM therapy and aggressive supportive care should be directed at groups with high-risk for early mortality from MM. Disclosures Chanan-Khan: Millennium: Research Funding; Ascentage: Research Funding; AbbVie: Research Funding; Xencor: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment. Ailawadhi:Celgene: Consultancy; Pharmacyclics: Research Funding; Amgen: Consultancy, Research Funding; Takeda: Consultancy; Cellectar: Research Funding; Janssen: Consultancy, Research Funding.

Background: Median age at diagnosis for MM is 69 years with approximately 33% of the newly diagnosed MM (NDMM) patients aged ≥80 years (elderly). Yet, outcomes data and clinical trial participation in elderly MM patients are limited due to frailty and frequent comorbidities. Moreover, elderly patients are not candidates for stem cell transplant (SCT) and age is an independent poor prognostic marker, leading to disparate outcomes. The present study evaluated trends in outcomes and predictors of survival among elderly NDMM patients in the United States (U.S.). Methods: Using Surveillance Epidemiology and End Results (SEER) database, we extracted data on age, gender, geographical regions, year of diagnosis, and vital status for elderly patients (age ≥ 80 years) with MM from 1975 to 2016. Relative survival (RS) was estimated as the ratio of observed survival to the survival expected in the general population in the absence of malignancy. This adjusted for the competing causes of mortality prevalent in elderly patients. Mortality rates were age-adjusted. To estimate trends in mortality, we estimated annual percentage change (APC) and average annual percentage change (AAPC) by fitting the data in the join point regression model using the NCI's Join point Regression Program, Version 4.5.0.1. A multivariate analysis was conducted to analyze the predictors of overall and myeloma-specific mortality among elderly patients fitting into Cox proportion hazard regression model. Results: A total of 17,265 elderly MM patients were eligible for the study. Majority were females (n=9,487, 55%), were non-Hispanic whites (NHW) (n=12,895, 74.7%) and were diagnosed in the Western region of the U.S. (n=8,541, 49.5%). The median age was 84 years (IQR 81-87). The median RS for the whole period (1975-2016) was 19.8 months with 1- and 5- year RS of 60.1% (95%CI: 58.8-61.3) and 20.9% (95%CI: 19.5-22.3) respectively. The median RS increased from 8.45 months in 1975 to 29.86 months in 2013. One and 5- year RS also increased from 42.7% (95%CI: 32.4-52.5) and 12.9% (95%CI: 5.8-22.7) in 1975 to a 1- and 5-year RS of 72.3% (95%CI: 65.4-78.1) and 31.1% (95%CI: 23.4-39.1) in 2013, respectively (Fig.1). However, the age standardized mortality rate for the whole period showed an increasing trend with an AAPC of 2.3 (0.5-4, P<0.001). On fitting joinpoint regression, 2 distinct joinpoints were identified, calendar years 1977 and 1989 (Fig 2). Most of the increase in mortality rate was noted in the period 1975-1989. Since 1989, the mortality rates have shown a decreasing trend which is marginal but statistically significant, with an APC of -0.41 (95%CI: -0.7- -0.1, p<0.001). On multivariate analysis, increasing age (p<0.001) and non-northeast location (p<0.01) were associated with higher overall and MM-specific mortality. Female gender was associated with lower overall mortality (p<0.001) but not myeloma-specific mortality. While "other" race/ethnicity (not NHW, non-Hispanic black; NHB or Hispanic) was associated with lower risk of overall and myeloma-specific mortality, NHB were associated with a lower MM-specific (p<0.001) but not overall mortality. Conclusions: Our results suggest that although RS has improved significantly in the elderly patients, the mortality rates have not changed drastically over the years. Besides, factors like advancement in diagnosis and treatment which have improved RS, early diagnosis (leading to lead time bias) may also be contributing. Furthermore, there are several gender-specific, geographic and racial/ethnic disparities in outcomes of elderly MM patients, which need to be assessed prospectively. Although, a small decline in mortality rates in the more recent time is promising, further studies are warranted on interventions including aggressive supportive care directed at elderly patients with MM, so that MM survivorship can be improved. There should be strong consideration to increaseenrollment of the elderly in prospective clinical trials so that evidence-based guidelines can be developed. Disclosures Manochakian: Takeda: Membership on an entity's Board of Directors or advisory committees; Guardant Health: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Novocure: Membership on an entity's Board of Directors or advisory committees. Chanan-Khan:Merck: Research Funding; Ascentage: Research Funding; Millennium: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment; Pharmacyclics: Research Funding; AbbVie: Research Funding; Xencor: Research Funding. Ailawadhi:Janssen: Consultancy, Research Funding; Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy, Research Funding; Cellectar: Research Funding; Pharmacyclics: Research Funding. Off Label Use: Do not remove- this information is relevant to Abstract ID 125629: TAK-169 is a dimeric fusion protein of an anti-CD38 antibody single chain variable fragment fused to a modified Shiga-like toxin-A subunit..

Abstract Background: Therapeutic advancements have led to significant improvement in outcomes for MM patients. Revised diagnostic criteria from International Myeloma Working Group focus on initiating treatment before the onset of end-organ damage. Thus, timeliness of initial therapy can significantly improve patient morbidity and outcomes. Such an analysis has not been reported for MM. Methods: We analyzed patients diagnosed with MM between 2004-2015 from the National Cancer Database (NCDB). Patients who received systemic treatment any time within the first year of diagnosis were included; those treated later than one year were considered possibly initially diagnosed with smoldering myeloma and hence, excluded. Time to initial treatment was divided into centiles with the 4 centiles analyzed being 0-7 days, 8-18 days, 19-37 days and >37 days. We performed univariate and multivariate analyses to compare sociodemographic and clinical factors influencing patient distribution across these timeliness categories and trends over time. Results: A total of 53,665 patients were included with 15,282 in 0-7 days, 12,462 in 8-18 days, 12,723 in 19-37 days and 13,198 in the >37 days treatment categories. Distribution across these centiles over time is shown in Figure. Univariate analyses showed a significant difference in distribution across the timeliness categories by year of diagnosis, patient age, gender, race/ethnicity, education level, insurance type, comorbidity score, treatment facility type, geographical location and distance to treating facility (all p<0.001) but no effect of income level. Multivariate analysis showed that there was a significantly higher likelihood of being in 4th centile (>37 days to treatment) for women (p<0.001), African Americans (p<0.001) or Hispanics (p=0.03; reference: Whites) or diagnosed in more recent years (p<0.001). Patients were less likely to be in the 4th centile if they were ≥80 years (HR 0.89, 95% CI 0.81-0.98, p=0.02), had >0 Charlson comorbidity score (p<0.001), were uninsured (p=0.02) or had Medicaid (p=0.03) as compared to having private insurance, were in non-community treatment facilities or geographical location other than Northeast (p<0.001). Patient education, income level or distance from treating facility was no longer significant. Conclusions: Several factors were found to affect timeliness of treatment in MM. We noted relatively faster time to initial treatment in elderly, uninsured or those who were treated in academic/comprehensive cancer programs but a delay in treatment for women and racial/ethnic minorities among other factors. These factors need to be addressed for equitable access and treatment practices and to realize improved outcomes for all. Figure. Figure. Disclosures Ailawadhi: Amgen: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Pharmacyclics: Research Funding.

Abstract Background: Racial disparities in outcomes of cancer patients have been reported. Access to comprehensive cancer centers is associated with improved overall survival (OS) but racial/ethnic minorities may have a disparate access to such care. While the impact of treatment facility volume on outcomes has been evaluated, outcomes of centers with minority-predominant patient population have not been studied. We compared demographic profiles, facility level data and OS of patients treated at minority-predominant facilities to facilities that treated predominantly non-Hispanic Whites (NHW) with non-DLBCL. Methods: The National Cancer Database (NCDB) was used to identify all non-DLBCL patients diagnosed between 2004 and 2015. "Minority-treating facilities" were defined as facilities in the top decile by proportion for initial treatment of non-Hispanic African-Americans (NHAA), Hispanics and other races. We performed univariate and multivariate analyses to compare sociodemographic and clinical factors influencing outcomes between minority treating and non-minority treating facilities. A subgroup analysis stratified by race/ethnicity was also conducted to study the effect of treating facilities on the outcome of NHWs and minorities separately. Results: Of 1339 total facilities, 123 (9.1%) qualified as minority treating. Of 207,239 eligible patients in NCDB, 18,719 (9.03%) received treatment at the minority-treating facilities and of these, 11,190 (~60%) belonged to the minority races. Overall, 4.5% (6,988/156,664) NHWs and 30% (11,190/37,639) minorities received treatment at the minority-treating facilities. Several demographic and facility level characteristics were significantly different among the patients treated at minority-treating facilities as compared to non-minority treating facilities. Overall, significantly higher number of patients in minority-treating hospitals had lower income and education, had Medicaid coverage or lack of insurance. The OS of patients in minority treating facilities was significantly worse as compared to non-minority facilities (Figures). On multivariate analysis, patients who received treatment at minority-treating facilities were at 10% (HR=1.10, 95% CI: 1.06-1.14 p<0.001) higher risk of mortality as compared to those treated at the non-minority treating facilities. On multivariate analysis, NHAA (30% increased risk) and 'other races' (9% increased risk) were at significantly higher risk of mortality as compared to the NHW (Table 2). To study the effect of treatment at minority-treating facilities on OS among the patients of same race/ethnicity group, a multivariate analysis was also run separately for NHW and racial minorities. The NHWs who received treatment at minority-treating facilities were at 13% higher risk of death (HR=1.13, 95% CI: 1.08-1.19 p<0.001) as compared to NHWs who were treated at non-minority treating facilities. Similarly, the racial minorities who received treatment at minority treating facilities were at 8% higher risk of death (HR=1.08, 95% CI: 1.03-1.19 p=0.003) as compared to those who received treatment at the nonminority treating facilities. Conclusions:Outcomes of patients who received treatment at minority treating facilities was significantly worse than those at non-minority treating facilities. This was true for NHWs and racial minorities separately as well. Several demographic and facility level characteristics were significantly different in the two groups however OS remained worse after adjusting for them. Causes of poor outcomes at minority-treating facilities must be analyzed to mitigate them and improve outcomes for all. Figure. Figure. Disclosures Ailawadhi: Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Pharmacyclics: Research Funding.

Abstract Background: The treatment of NHL has witnessed a paradigm shift over time, with targeted immunotherapy, stem cell transplant (SCT) among others. Historically, XRT was used quite frequently for the management of NHL but with advent of better systemic therapy, its utilization has changed. Trends and patterns of care for XRT use have never been formally reported. Methods: We identified patients with NHL diagnosis in the National Cancer Database (NCDB) between years 2004 and 2015. Demographic, clinical, facility level, initial treatment and outcome data were collected. The utilization of XRT in diffuse large B-cell (DLBCL) and non-DLBCL NHL were analyzed separately by univariate and multivariate analyses. To analyze the trends in the rates of XRT, we applied segmented linear regression to calculate the average annual percent change (AAPC) and 95% confidence Interval (CI) with a 'p' value. AAPC and CIs were calculated using the segmented package in R studio v1.1.49. Rest of the analyses was conducted using StataCorp version 15.1. Results: A total of 133,182 DLBCL and 204,933 non-DLBCL patients were identified. Among patients with DLBCL, 27,895 (20.9%) patients received RT. The rate of XRT declined from 25% in 2004 to 18.4% in 2015 with estimated AAPC of -0.59% (95%CI: -0.70- -0.49), p= 0.03 (Figure 1). In a subgroup analysis, a similar decline in the rate of XRT was evident across all the age groups, combined stages I and II vs stages III and IV and nodal vs extra nodal DLBCL (Table 1). Among non DLBCL, 33,369 (16.3%) patients received XRT. There was a statistically significant decline in the rate of XRT from 18.03% in 2004 to 16.3% in 2014 with an AAPC of -0.26 (95%CI: -0.38- -0.14) p <0.001 (Figure 1). On the subgroup analysis, the utilization of XRT declined across all the studied subgroups except among patients aged 80 years and above (Table 1). On multivariate analysis, several demographic, clinical and facility level factors were found to be significantly associated with XRT utilization in NHL (Table 2). In particular, older age groups, racial/ethnic minorities, advanced stages, higher Charlson comorbidity scores and diagnosis in the more recent calendar years were associated with lesser chances of receiving RT. Extranodal DLBCL had lower utilization while extranodal non-DLBCL had increased XRT utilization. Conclusion: There has been a significant decline in the utilization of XRT among patients with NHL (DLBCL and non-DLBCL) in the US since 2004, likely due to introduction of practice changing systemic therapeutics. There is still significant heterogeneity noted in practice patterns regarding utilization of XRT across the US. Having more standardized guidelines will help streamline delivery of evidence-based patient care. Disclosures Ailawadhi: Celgene: Consultancy; Janssen: Consultancy; Amgen: Consultancy; Takeda: Consultancy; Pharmacyclics: Research Funding.

Abstract Background: Clinical trials are fundamental to advance therapeutics systematically and improve patient outcomes. Despite this, enrollment on clinical trials remains dismal in the United States (US) and is a constant focus of healthcare policy. We studied distribution of clinical trials for B-cell malignancies over time across the US and unique clinical trial opportunities i.e. individual clinical trials for the given diagnosis at a site that patients may have access to participate. Methods: We abstracted data from clinicaltrials.gov for all trials that had non-Hodgkin lymphoma (NHL) or multiple myeloma (MM) as an inclusion indication between 1999-2018. Clinical trial characteristics and distribution over US geographical divisions (West, Midwest, Northeast, and South) were studied, and differences were assessed by Chi-square test. Results: A total of 1930 trials were identified (NHL: 982, MM: 948), of which 483 were recruiting at the time of data abstraction (NHL: 250, MM: 233). Over the past 2 decades, 182691 patients were enrolled on the various trials (NHL: 81592, MM: 101099). Trials by phase of study included phase 1: 629, phase 1/2: 316, phase 2: 813, phase 2/3: 11 and phase 3: 161. Number of trials by phase separated by NHL and MM are shown in Figure 1. Of these, 197 trials were randomized (NHL: 67, MM: 130). Geographical distribution of trials by diagnosis type is shown in Figure 2. A total of 31806 unique trial opportunities were noted for MM and NHL, of which 9,513 were international and 22,293 were in the US, with a geographical distribution of 5080 in West, 8198 in Midwest, 3944 in Northeast, and 5071 in South. 4,883 of the unique trial opportunities were available at NCI/NCCN accredited sites and 17,410 were at non-NCI/NCCN sites in the US. Treatment characteristics of the trials included monoclonal antibodies in 1218, other targeted agents in 2641, stem cell transplant in 526, and other agents in 517 trials with several trials utilizing more than one of these therapeutic options. There was no statistically significant difference in the distribution of clinical trials by phase of study across various US geographical regions for MM (p=0.71), NHL (p=0.98) or combined MM+NHL (p=0.16). On the other hand, unique trial opportunities were significantly different by study phase and geographical distribution for MM, NHL or MM+NHL (all p<0.001) (Figure 3). Conclusions: Widespread access to clinical trials within a cancer diagnosis is imperative for generalizability of trial results. In a comprehensive, national analysis we noted that while it may appear that clinical trials are available across the US, sites where they are open are distributed unevenly, giving rise to a disparity in access to evidence-based therapeutic advancements for patients. Disclosures Ailawadhi: Janssen: Consultancy; Amgen: Consultancy; Pharmacyclics: Research Funding; Takeda: Consultancy; Celgene: Consultancy. Sher:Affimed: Research Funding.