Siddiqui, Mustaqeem

Abstract BACKGROUND: While hematologic cancers comprise only 10% of all malignancies, they are divided into >100 distinct World Health Organization subtypes. It is known that higher volume of care is generally associated with better clinical outcomes. However, such a volume-outcome relationship in the medical management of hematologic cancers has not been rigorously explored. The American Society of Clinical Oncology (ASCO) National Census of Oncology Practices shows that the majority of hematologist-oncologists in the United States (US) have a combined hematology-oncology practice (J Oncol Pract 2013). In this study, we estimated the annual number of new and established patients with major hematologic cancers seen on average by a hematologist-oncologist in the US. METHODS: We estimated the number of hematologist-oncologists working in the US using the ASCO workforce information system data from 2011. We utilized statistics from the Surveillance Epidemiology and End Results (SEER) Program to determine the incidence and 37-year limited prevalence of hematologic cancers in 2011. We used 'first malignant tumor per site' statistics as the tumor inclusion method. For potentially curable hematologic cancers (acute lymphocytic leukemia, acute myeloid leukemia, Burkitt lymphoma, diffuse large b-cell lymphoma, Hodgkin lymphoma, and marginal zone lymphoma), we used the estimated 1-5 year survival rates from SEER and excluded patients who survived >5 years, since relapses are rare afterwards. Because prevalence estimates of chronic myelomonocytic leukemia, myelodysplastic syndromes, and certain subtypes of non-Hodgkin lymphoma are unavailable, we were unable to calculate the number of annual established cases. For myeloproliferative neoplasms, we obtained the prevalence estimate from Mehta J, et al (Leuk Lymphoma 2014). We derived the distribution of major non-Hodgkin lymphoma subtypes from the National Cancer Data Base (NCDB) Participant User File. RESULTS: The ASCO workforce information reported a total of 13,084 hematologist-oncologists working in the US in 2011. The Table summarizes the average number of specific hematologic cancer cases seen per hematologist-oncologist in 2011. CONCLUSION: Hematologic cancers are relatively rare but complex. In the US, a hematologist-oncologist on average cares for only 1-2 new patients of any subtype of hematologic cancers annually. The number of established patients is correspondingly low. These numbers are expected to vary by practice setting and disease specialization. As the diagnosis and management of hematologic cancers becomes more sophisticated, future research should explore the potential of a volume to clinical outcome relationship for these providers. Table. Hematologic Cancer Average Annual Number of Cases per Hematologist-Oncologist in the US New Cases Established Cases All Cases Acute lymphocytic leukemia 0.4 1.4 1.8 Acute myeloid leukemia 1 1.5 2.5 Chronic lymphocytic leukemia 1.1 10.7 11.8 Chronic myeloid leukemia 0.4 2.7 3.1 Chronic myelomonocytic leukemia 0.1 - - Hodgkin lymphoma 0.7 2.7 3.4 Multiple myeloma 1.6 6.3 7.9 Myelodysplastic syndromes 1.2 - - Myeloproliferative neoplasms 0.6 22.2 22.8 Non-Hodgkin lymphoma 5.1 - - Anaplastic large cell 0.1 - - Burkitt 0.1 0.3 0.4 Diffuse large B-cell 2 6 8 Follicular 1.1 - - Hairy cell leukemia 0.1 - - Lymphoplasmacytic 0.1 - - Mantle-cell 0.3 - - Marginal zone 0.5 2.1 2.6 Peripheral T-cell, not otherwise specified 0.1 - - Disclosures No relevant conflicts of interest to declare.

Abstract Abstract 602 Although, MM remains the most common indication for ASCT in the US, a controversy exists about the timing of ASCT. Since early ASCT (eASCT) versus delayed ASCT (dASCT) results in similar progression free survival (PFS) and overall survival (OS) in the novel agent era, economic assessment of one strategy over the other may help both clinicians and policy makers in establishing guidelines in the MM treatment paradigm. Here we designed a study to model a decision tree of both the strategies and estimate their cost-effectiveness. A decision tree was developed to compare the outcomes of ASCT in newly diagnosed transplant eligible MM patients after chemotherapy with novel drug lenalidomide which formed the base case. The survival data was obtained from our published data on eASCT versus dASCT in MM patients who underwent initial therapy with dexamethasone + immunomodulatory agents (IMiDs). The initial decision node on the decision tree model was dichotomized based on the cutoff of 12 months for eASCT versus dASCT. The probabilities of events during the length of follow up (60 months) period which formed our base case were: (a) eASCT cohort: Induction chemotherapy for median of 6 months - ASCT - median 30 months to relapse (drug free interval) followed by median therapy of 24 months for relapsed MM; (b) dASCT cohort: Median initial therapy for 36 months - ASCT - median 24 months to relapse (drug free interval). The cost analysis was done from a US third party payer perspective. The cost data for MM ASCT was obtained from the published data in which direct hospital costs were obtained from Nationwide Inpatient Sample and were converted to Medicare cost-to-charge ratio for urban hospitals. Chemotherapy cost consisted of actual drug costs that calculated based on average wholesale prices AWP (2008), costs of medical oncology visits, and the costs of managing adverse events. Post-ASCT maintenance costs were not included in the analysis. TreeAge Pro 2012 software was used to perform a static decision analysis. The outcomes were expressed in the units of health utility values obtained from phase III HOVON trial which measured quality of life (QoL) using EORTC QLQ-C30 & EuroQoL-5D. All scales and items were linearly transformed to range from 0 to 100. The utilities measured by the EuroQol-5D ranged from 0.40 before induction therapy to 0.70 after ASCT and translated to QALYs using area under the curve method. Using a Consumer Price Index (CPI), the costs of ASCT and chemotherapy were converted into 2012 US$. The robustness of the decision model was tested through univariate analyses and tornado diagram to identify those variables which if varied across clinically relevant measures may result in difference in outcomes. The expected cost of providing treatment to newly diagnosed MM patients for a period of 5 years when they undergo eASCT after induction with IMiDs was calculated to be $211,869. This was $52,454 less than the expected cost of undergoing dASCT. Patients who underwent eASCT had an expected benefit of 2.06 QALYs following treatment, which was 0.19 QALYs more than the patients who received dASCT. This implies that eASCT is preferred over (dominates) dASCT. One-way sensitivity analysis was undertaken to identify factors that were found to be most influential and included OS, overall response rate (ORR) and 1 year ASCT related mortality rate (TRM). Specifically, eASCT showed dominance even if the probability of OS was lowered to 52.1% from the actual case base of 65%, or the probability of ORR was lowered to ≥ 75.8% from the actual case base of 92%, or the probability of 1 year TRM was increased to 11.5% from actual case base of 2%, indicating the robustness of the model favoring cost effectiveness of eASCT. In absence of a survival benefit, the cost savings of early ASCT as measured by ICER would favor this approach, and it would help clinicians, patients, policy makers and third party payers in decision making. Future prospective studies in MM ASCT should incorporate economic and QOL components in the analysis and also focus on the added cost of post-ASCT maintenance therapies with respect to ICER and QALYs. Disclosures: No relevant conflicts of interest to declare.

6594 Background: Given the significant short-term adverse effects of CAR-T cell therapy, it is important to evaluate its impact on QOL of patients in addition to efficacy, compared with established forms of cellular therapy like SCT. Methods: QOL was evaluated prospectively in patients undergoing CAR-T therapy, autoSCT & alloSCT for hematologic malignancies. QOL was assessed with FACT-G at baseline, 2 weeks and monthly for 6 months thereafter. Functional well-being (FWB), physical WB (PWB) emotional WB (EWB) & social WB (SWB) and change over time were compared across groups. Results: 45 patients were recruited (CAR-T: 10; Auto SCT: 22; Allo SCT: 13) with follow up for 2 weeks & 1 month available for 23 &15 patients, respectively (Table). There was no statistically significant difference in baseline total QOL scores (p=0.13), though scores were lower in the alloSCT group (85,84,68). EWB &FWB were numerically higher in the CAR-T group, followed by autoSCT group. At 2 weeks, overall QOL decreased by only 2 points in CAR-T group vs. 22 & 18 points in auto & alloSCT groups (p=0.09). Change in PWB vs. baseline was less pronounced in the CAR-T group (-1, -9, -13, p=0.03). At 1 month, overall QOL was 6 points lower than baseline in CAR-T group vs. 3 and 14 points lower in auto & alloSCT groups, respectively (p=0.34). Importantly, PWB had at least returned to baseline in the CAR-T group. Conclusions: Preliminary data show that patients undergoing CAR-T cell therapy do not experience a more significant decline in QOL compared with auto & allo SCT, and may experience fewer physical side effects in the short-term. Accrual & follow-up are ongoing. [Table: see text]

Background: The patient/caregiver experience during CAR-T therapy is stressful, overwhelming, terrifying, and often a patient’s last treatment option. The Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery Innovation and Design team has worked with the CAR-T therapy clinical team to develop a patient experience that provides patients with a sense of caring, supportive environment, timely knowledge, and realistic expectations. Using a human-centered design approach, the Innovation and Design team worked with patients and caregivers to understand latent and unspoken needs in order to develop an ideal CAR-T therapy patient journey. Methods: With qualitative interviewing techniques, patient observation, and low fidelity experimentation, 21 patients/caregiver pairs were interviewed throughout their CAR-T therapy experience in 2018. Patients were interviewed at several touch points as well as encouraged to reach out to the Innovation and Design team at any point with reflections on their experiences. Patients were recruited as they began their evaluation phase for CAR-T therapy. The interviews were unscripted to allow for a breadth of discovery by not constraining the conversations to previously developed themes. As themes emerged from patient/caregiver interviews, artifacts and interventions were designed to alleviate pain points and improve the patient/caregiver experience. These artifacts and interventions were integrated into the clinical processes in real time and patient/caregivers were interviewed to understand the impact of these activities. Results: Several themes emerged from qualitative interviews with patients and caregivers. From the themes, interventions were developed. We were able to demonstrate a qualitative improvement in patient/caregiver experience through these interventions (Figure 1). Conclusions: Patients/caregivers undergoing CAR-T therapy have unique issues surrounding the logistics of care, emotional burden, and physical effects of treatment. We implemented processes to address these issues and observed a qualitative improvement via patient interviews/feedback. Ongoing work includes optimizing remote monitoring, digital platforms for patient education, and a quantitative study looking at patient reported outcomes (PROs) in such patients. To our knowledge, this is the first report for care delivery optimization in real-world practice for this new therapy.

1573 Background: There are no epidemiologic studies of SWM due to lack ofICD codes specific for smoldering status. Methods: The NCDB represents ~70% of cancer cases in the US. It records the time to initial treatment or reasons for not receiving treatment. Using the 2004-2012 database, we estimated the overall proportion of new adult WM cases that are smoldering and analyzed outcomes according to socio-geo-demographic subgroups. Patients who were recommended treatment (irrespective of administration status) or died within 120 days of diagnosis were considered to have active WM while those who did not require treatment and had follow-up ≥120 days after diagnosis were considered to have SWM. Results: 2322 (28%) of 8302 patients diagnosed with WM were SWM. Median age at diagnosis was 71 (range 28-90) years. Median age did not change significantly during 2004-2012 (P = 0.83) but the proportion of smoldering disease decreased with time (P = 0.02). The proportions of SWM according to socio-geo-demographic subgroups are shown in the Table. Conditional survival (given survival to 4 months) at 5-years was 66% vs. 70% and at 10-years was 38% vs. 46% for active WM vs. SWM, respectively. The OS for SWM was significantly better among females and those who were younger, privately insured, with less co-morbidities and residing closer to a health facility. However, the OS did not differ by racial groups or era of diagnosis 2004-2008 vs 2009-2012. On multivariate analysis, sex, age group and co-morbidity index were significant factors affecting OS while race, insurance, distance from health facility and era of diagnosis were not. Conclusions: Approximately 28% of WM cases in the US are smoldering at diagnosis. The prevalence of SWM varied among socio-geo-demographic subgroups. Sex, age and co-morbidities significantly affect survival of SWM. [Table: see text]

Venous thromboembolism (VTE) contributes to significant morbidity, mortality, and socioeconomic burden. There is a paucity of literature regarding sex-based sociodemographic differences in VTE presentation and short-term outcomes. We aimed to compare clinical outcomes between men and women hospitalized for VTE management. We performed a retrospective analysis using data from the National Inpatient Sample (NIS) database from 2012 to 2013. Inclusion criteria were age 18 years and older and a primary discharge diagnosis of VTE. Sociodemographic features and medical comorbidities were analyzed, as were hospital length of stay and in-hospital mortality rates. A total of 107,896 patients met the inclusion criteria; 53% were female. Median age was 65 years (interquartile range 51–77) and women were older than men (65 vs 62 years, p<0.001). There were significant differences between men and women with respect to race, primary insurance payer and medical comorbidities, and small differences with respect to VTE location. Female sex was associated with a small but significantly longer hospital length of stay (mean ratio 1.04, 95% CI 1.03–1.05, p<0.001) but no significant difference in in-hospital mortality (2.2% vs 2.1%, p=0.15). In a multivariate model, there was no significant difference between women and men with respect to hospital length of stay or in-hospital mortality. In conclusion, we used data from the NIS to study over 100,000 patients hospitalized for VTE, and identified several sex-based disparities in sociodemographic factors and location of VTE. However, in a multivariable analysis correcting for these factors, sex was not associated with significant differences in clinical outcomes.

Little is known about which variables put patients with cancer at risk for 30-day hospital readmission. Comanagement of this often complex patient population by specialists and hospitalists has become increasingly common. This retrospective study examined inpatients with cancer comanaged by hospitalists, hematologists, and oncologists to determine the rate of readmission and factors associated with readmission. Patients in this cohort had a readmission rate of 23%. Patients who were discharged to a skilled nursing facility (odds ratio [OR] = 0.34) or hospice (OR = 0.11) were less likely to have 30-day readmissions, whereas patients who had surgery (OR = 3.16) during their index admission were more likely. Other factors, including patient demographics, cancer types, and hospitalization interventions and events, did not differ between patients who were readmitted and those who were not. These findings contribute to a growing body of literature identifying risk factors for readmission in medical oncology and hematology patients.