Biswas, Suman

BackgroundFactors associated with receiving initial care for thyroid cancer (TC) at academic centers (ACs) versus nonacademic centers (NACs) and their impact on patient outcomes have not been reported.MethodsThe National Cancer Database with TC cases from 2004 to 2013 was evaluated for association of type of center for initial care with socioeconomic factors and disease and treatment characteristics, as well as overall survival (OS; all‐cause mortality).ResultsThe patients with TC (n = 200,824) included were predominantly women (74%), non‐Hispanic Whites (85%), and from metro areas (84%). Sixty percent received initial care at a NAC. There were no significant differences between treatment groups by age or gender. Among those treated at an AC, a higher proportion belonged to racial/ethnic minorities (16.5%) versus at a NAC (11.6%). Hormone therapy was used more in an AC versus a NAC (60% vs 47%). Patients with all TC pathologies combined had a lower likelihood of death when they received initial care at an AC (hazard ratio [HR], 0.948; P = .0006). Among individual pathologic subtypes, a lower likelihood of death was noted when initial care was received at an AC for follicular (HR, 0.828, P = .0010) and Hurthle cell cancers (HR, 792; P = .0008), as well as stage II papillary thyroid cancer (HR, 0.828; P = .0026), but not for other histopathologic subtypes.ConclusionsInitial care at an AC was associated with lower likelihood of death for patients with TC, especially for those with follicular or Hurthle cell subtypes. Optimal resource use with consideration of patients' socioeconomic and demographic factors is imperative to ensure the most appropriate management of patients with TC in various treatment settings.

Background: MM is associated with significant morbidity even at the time of presentation and there is ample data to support that MM treatment improves overall survival (OS) as well as quality of life (QoL) due to effective symptom control. Patient refusal to recommended cancer treatment is not common in clinical practice but has been reported in certain cancers. There is no data on characteristics of MM patients who refuse treatment despite physician recommendation and outcomes of such patients. Methods: We used the National Cancer Database (NCDB) from 2004-2015 to extract data on adult MM patients who refused any systemic therapy as part of their initial care despite physician recommendation. Information on patient age, gender, race/ethnicity, education, year of diagnosis, income, geographical location, comorbidities, systemic treatment, survival time and vital status was obtained. Average annual percent change (AAPC) and average percent change (APC) were calculated to analyze the trends, and periods with similar APC were compared for statistically significant changes by applying join point regression. Multivariate models were employed to identify factors predicting refusal to therapy and to assess its impact on OS. Results: Of 75,734 eligible patients who were recommended treatment for MM (chemotherapy and or immunotherapy), 2138 (2.8%) patients did not receive any treatment due to refusal. Significantly higher number of females, patients in older age groups (age groups 60-79 and ³ 80 years), Medicare recipients and with ≥2 comorbidities refused treatment recommendation. On multivariate analysis, advanced age groups 60-79 years (OR = 1.47; 95% CI 1.27-1.31 p<0.001) and ³80 years (OR = 5.02; 95% CI 4.23-5.96, p<0.001), female gender (OR =1.16 ; 95% CI 1.05-1.27, p<0.001), patients with certain primary payor types including Medicaid (OR = 1.59; 95% CI 1.28-1.98, p<0.001), Medicare (OR = 1.63; 95% CI 1.43-1.87, p<0.001) and uninsured (OR = 1.81; 95% CI 1.38-2.38, p<0.001), and presence of ³2 comorbidities (OR = 1.39; 95% CI 1.2-1.61, p<0.001) were the independent factors associated with higher likelihood of refusing treatment. Whereas being seen at academic research programs (OR = 0.7; 95% CI 0.6-0.8, p<0.001), higher education (OR =0.81 ; 95% CI 0.67-0.98, p=0.03), geographical location (Midwest OR =0.87 ; 95% CI 0.76-0.99, p=0.04), longer distance to facility being at 3rd quartile (10.1-26.2 miles: OR =0.78 ; 95% CI 0.68-0.89, p<0.001) or 4th quartile (>26.2 miles: OR = 0.59; 95% CI 0.51-0.68, p<0.001) were associated with lower likelihood of refusing treatment. The overall rate of refusing treatment by MM patients was quite stable during 2004-2015 with an AAPC of 1.1 (95% CI -1.1-3.3 p=0.3). However, on fitting scatter plot into Joinpoint regression model, a concerning trend in the more recent years was noted. While the rate of refusing recommended treatment decreased during 2004-2008, a rising trend was observed during the period 2008-2015 with an APC of 3.8 (95% CI 1.3-6.4, p≤0.001) (Fig 1a). Patients who were recommended treatment but refused had significantly worse survival than those who received treatment (median survival 17.7 vs 52.9 months, Cox hazard ratio 1.21; CI 1.17-1.24, p<0.001) (Fig 1b). Conclusions: The percentage of MM patients refusing any systemic treatment as a part of their initial care despite physician recommendation appears to be increasing in recent years and this needs to be systematically addressed. Disparities in refusal to recommended treatment are independently associated with several variables including age, gender, comorbidities, geographical location, facility and insurance type. These need to be addressed urgently since there are several effective and well-tolerated anti-MM treatments which can potentially benefit patients in case the initial socioeconomic, clinical and psychosocial barriers are overcome. Disclosures Chanan-Khan: Xencor: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment; Ascentage: Research Funding; Millennium: Research Funding; AbbVie: Research Funding. Ailawadhi:Pharmacyclics: Research Funding; Celgene: Consultancy; Amgen: Consultancy, Research Funding; Cellectar: Research Funding; Janssen: Consultancy, Research Funding; Takeda: Consultancy.

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..