Citations

Abstract Objective: The need for effective nonhormonal treatments for hot flash management without unwanted side effects continues. The primary aim of this pilot study was to evaluate the effect of combining a nonhormonal pharmacologic agent with a behavioral treatment for hot flash reduction. Methods: Seventy-one postmenopausal women were randomized to one of four groups: venlafaxine 75 mg + hypnosis (VH) versus venlafaxine 75 mg + sham hypnosis (VSH) versus a placebo pill + hypnosis (PH) versus placebo pill + sham hypnosis (PSH). Women recorded hot flash severity and frequency in a daily diary, in real time. The intrapatient difference in hot flash score (frequency × severity) at 8 weeks was analyzed using a General Estimating Equation model, using VSH as the referent arm, controlling for baseline hot flashes. Results: The active arms including PH or VH were not statistically significantly different than VSH (P = 0.34, P = 0.05, respectively). Women in each active arm reported hot flash reductions of about 50%, with the PSH group reporting a 25% reduction. Women receiving the PSH reported statistically significantly smaller reductions in hot flash score than women in the referent VSH arm (P = 0.001). There were no significant negative side effects during the course of the study. Conclusions: Hypnosis alone reduced hot flashes equal to venlafaxine alone, but the combination of hypnosis and venlafaxine did not reduce hot flashes more than either treatment alone. More research is needed to clarify whether combining hypnosis with a different antidepressant would provide synergistic benefits.

Abstract This abstract was not presented at the symposium.

AbstractIntroductionA harmonized protocol (HarP) for manual hippocampal segmentation on magnetic resonance imaging (MRI) has recently been developed by an international European Alzheimer's Disease Consortium–Alzheimer's Disease Neuroimaging Initiative project. We aimed at providing consensual certified HarP hippocampal labels in Montreal Neurological Institute (MNI) standard space to serve as reference in automated image analyses.MethodsManual HarP tracings on the high‐resolution MNI152 standard space template of four expert certified HarP tracers were combined to obtain consensual bilateral hippocampus labels. Utility and validity of these reference labels is demonstrated in a simple atlas‐based morphometry approach for automated calculation of HarP‐compliant hippocampal volumes within SPM software.ResultsIndividual tracings showed very high agreement among the four expert tracers (pairwise Jaccard indices 0.82–0.87). Automatically calculated hippocampal volumes were highly correlated (rL/R = 0.89/0.91) with gold standard volumes in the HarP benchmark data set (N = 135 MRIs), with a mean volume difference of 9% (standard deviation 7%).ConclusionThe consensual HarP hippocampus labels in the MNI152 template can serve as a reference standard for automated image analyses involving MNI standard space normalization.

87 Background: The increasing demand for specialist palliative care (PC) in cancer care requires an available and responsive clinical workforce. But as the volume of work expands, PC providers face an increased risk of burnout, characterized by depersonalization and work-related emotional exhaustion. The prevalence and predictors of burnout among PC providers is poorly understood. Methods: Members of the American Academy of Hospice and Palliative Medicine (e.g. advanced practice provider, registered nurses, chaplains) completed the Maslach Burnout Inventory (MBI) via electronic survey in the second half of 2013; respondents were recruited via e-mail, blog and Facebook posts, and Twitter tweets. The MBI determine burnout severity across two domains, Emotional Exhaustion (EE) and Depersonalization (DP). Severity is reported as “low”, “moderate”, or “high”; “high” is consistent with burnout. Demographic and work-related variables associated with burnout in other studies were determined via stepwise logistic regression. Results: We received surveys from 1,241 clinicians. We estimated a response rate of 30%. 68% of respondents were physicians. Most respondents were over age 50 (57%), female (65%), were married or partnered (82%) and had worked in the field for less than 10 years (67%). 42% took overnight call regularly, 30% reported working 50 hours per week or more, and 57% had at least 4 colleagues. Regarding burnout, 24% reported high DP, 59% reported high EE, and 62% of reported high burnout symptoms on either EE or DP scales. In logistic regression, younger physicians, those working more than 50 hours per work, and those with fewer colleagues within their practice were at greatest risk of burnout (p<0.02). Conclusions: PC clinicians report a burnout rate of 62%. Even with the limitations of our methods and potentially biased sampling, this prevalence is remarkable and higher than data reported in medical oncology (45%). Burnout severity is associated with working in isolation and working longer hours. Further studies on how burnout affects sustainability of the PC workforce are needed, especially since this workforce is so critical to the provision of high quality cancer care.

617 Background: Over the past decade, seven agents were approved for metastatic renal cell carcinoma (mRCC) leading to a rapidly evolving clinical landscape. Clinical trials addressing treatment efficacy, sequencing, and other questions may continue to impact management decisions. Furthermore, our prior retrospective experience indicates a significant proportion of pts may not receive systemic therapy in the first year after diagnosis with metastatic disease. This analysis describes contemporary patterns of care in the first 109 real world pts enrolled in a multicenter, prospective, observational registry. Methods: MaRCC Registry will enroll 500 pts from up to 60 US academic (ACAD) and community (COMM) sites with ~2 years of recruitment and ≥ 3 years of follow-up. Key inclusion criteria are age ≥ 18 years and diagnosis of mRCC with no prior systemic therapy (STx) for mRCC at study entry. Pts currently not on STx but who are being observed are permitted. Key endpoints include descriptive characteristics of treatments (e.g. treatment agents, sequence, duration, reasons for therapy choice and discontinuation), treatment effectiveness (e.g. overall response rate, progression free survival, overall survival), quality of life (PROs), medication adherence, and health resource utilization. Results: At data cutoff, 105 pts have been accrued with known STx status; median age 64 (Q1-3 range, 56-70); 66% male; 75% ACAD; 87% clear cell histology; and 31% stage IV at diagnosis. Initial management strategies are shown in the table. The initial management decision after accrual was to defer systemic therapy (Def STx) in 40% (N=44). In ACAD and COMM sites, Def STx percentages were 38% and 48% respectively. Baseline demographics and clinical characteristics were not notably different between Def STx pts and those treated with STx. Conclusions: In our prospective registry, we have identified a large percentage of pts who initially received Def STx. In the context of contemporary pts undergoing investigational and standard of care STx, we will describe the management and outcomes of the Def STx population. [Table: see text]

563 Background: Clinical trials mRCC pts are different from real world pts, resulting in bias in the literature. The MaRCC Registry is designed to survey questions not asked in tri als, such as why physicians make certain management decisions. This analysis describes reasons for physician treatment (tx) selection in the first 109 real world pts enrolled in a multicenter, prospective, observational registry. Methods: MaRCC Registry will enroll 500 pts from up to 60 US academic (ACAD) and community (COMM) sites with ~2 years of recruitment and ≥ 3 years of follow-up. Key inclusion criteria are age ≥ 18 years and diagnosis of mRCC with no prior systemic therapy (STx) for mRCC at study entry. Pts currently not on STx but who are being observed are permitted. Key endpoints include descriptive characteristics of txs, tx and patient outcomes, medication adherence, and health resource utilization. Results: At data cutoff, 105 pts have been accrued with known STx status; median age 64 (Q1-3 range, 56-70); 66% male; 75% ACAD; 87% clear cell histology; and 31% stage IV at diagnosis. Initial management decision was: 40% deferred systemic therapy (DSTx), 22% pazopanib, 18% clinical trial, 10% sunitinib, and 6% other. Among STx pts (N = 61), the most common categories for therapy choice as selected by providers were likelihood of clinical benefit (41%) followed by pt characteristics (30%). Within the clinical benefit category, the most common reason was OS/PFS (31%). Within the pt characteristic category the most common reasons included performance status/frailty (12%) or prognostic factors (12%). Common reasons for DSTx pts (N = 44) were active surveillance (AS) with disease present (39%), AS without disease present following a procedure (11%), or local therapy (7%). ACAD sites had higher percentages of pts with DSTx undergoing AS with disease present and local therapy. Conclusions: This is the first report describing factors driving physician decision making in management of mRCC. Management choices are not captured in other prospective or retrospective studies. Early experience suggests that clinical benefit and pt characteristics were common reasons for initial STx. Side effect profile rarely determined initial STx selection.

BACKGROUND. No laboratory test can predict the risk of nonrelapse mortality (NRM) or severe graft-versus-host disease (GVHD) after hematopoietic cellular transplantation (HCT) prior to the onset of GVHD symptoms. METHODS. Patient blood samples on day 7 after HCT were obtained from a multicenter set of 1,287 patients, and 620 samples were assigned to a training set. We measured the concentrations of 4 GVHD biomarkers (ST2, REG3α, TNFR1, and IL-2Rα) and used them to model 6-month NRM using rigorous cross-validation strategies to identify the best algorithm that defined 2 distinct risk groups. We then applied the final algorithm in an independent test set ( n = 309) and validation set ( n = 358). RESULTS. A 2-biomarker model using ST2 and REG3α concentrations identified patients with a cumulative incidence of 6-month NRM of 28% in the high-risk group and 7% in the low-risk group ( P < 0.001). The algorithm performed equally well in the test set (33% vs. 7%, P < 0.001) and the multicenter validation set (26% vs. 10%, P < 0.001). Sixteen percent, 17%, and 20% of patients were at high risk in the training, test, and validation sets, respectively. GVHD-related mortality was greater in high-risk patients (18% vs. 4%, P < 0.001), as was severe gastrointestinal GVHD (17% vs. 8%, P < 0.001). The same algorithm can be successfully adapted to define 3 distinct risk groups at GVHD onset. CONCLUSION. A biomarker algorithm based on a blood sample taken 7 days after HCT can consistently identify a group of patients at high risk for lethal GVHD and NRM. FUNDING. The National Cancer Institute, American Cancer Society, and the Doris Duke Charitable Foundation.

Healthcare workers suffer musculoskeletal disorders at rates that exceed most other industries. Despite this, many healthcare facilities do not follow best practices that are proven to reduce risk of injury. This discussion panel will explore the reasons for why the healthcare industry struggles to prevent injuries and will outline successful strategies for protecting workers. The panelists are leaders and advisors for large medical systems and their specialties include ergonomics, safe patient handling, and patient safety. The panelists relate the critical challenges faced when preventing caregiver injuries. Such challenges include underreporting of staff injuries, lack of funding or administrative support, and inability to sell the connections among caregiver safety, caregiver productivity, and patient safety. The panelists also share success stories of when they have achieved positive change and explain the strategies that were effective. Practitioners should gain new insights for implementing successful change, and researchers will be exposed to new and important research questions.

Abstract Background: Genome-wide association studies (GWAS) in European populations have identified genetic risk variants associated with multiple myeloma. Methods: We performed association testing of common variation in eight regions in 1,318 patients with multiple myeloma and 1,480 controls of European ancestry and 1,305 patients with multiple myeloma and 7,078 controls of African ancestry and conducted a meta-analysis to localize the signals, with epigenetic annotation used to predict functionality. Results: We found that variants in 7p15.3, 17p11.2, 22q13.1 were statistically significantly (P &lt; 0.05) associated with multiple myeloma risk in persons of African ancestry and persons of European ancestry, and the variant in 3p22.1 was associated in European ancestry only. In a combined African ancestry–European ancestry meta-analysis, variation in five regions (2p23.3, 3p22.1, 7p15.3, 17p11.2, 22q13.1) was statistically significantly associated with multiple myeloma risk. In 3p22.1, the correlated variants clustered within the gene body of ULK4. Correlated variants in 7p15.3 clustered around an enhancer at the 3′ end of the CDCA7L transcription termination site. A missense variant at 17p11.2 (rs34562254, Pro251Leu, OR, 1.32; P = 2.93 × 10−7) in TNFRSF13B encodes a lymphocyte-specific protein in the TNF receptor family that interacts with the NF-κB pathway. SNPs correlated with the index signal in 22q13.1 cluster around the promoter and enhancer regions of CBX7. Conclusions: We found that reported multiple myeloma susceptibility regions contain risk variants important across populations, supporting the use of multiple racial/ethnic groups with different underlying genetic architecture to enhance the localization and identification of putatively functional alleles. Impact: A subset of reported risk loci for multiple myeloma has consistent effects across populations and is likely to be functional. Cancer Epidemiol Biomarkers Prev; 25(12); 1609–18. ©2016 AACR.