Yost, Kathleen J.

PURPOSE: This study assessed the long-term quality of life (QOL) of patients with aggressive lymphoma subtypes treated with autologous hematopoietic cell transplant (autoHCT) compared with those without history of transplant. METHODS: Patient-reported QOL measures were prospectively gathered from patients enrolled in the Iowa/Mayo Specialized Program of Research Excellence Molecular Epidemiology Resource cohort with aggressive lymphoma subtypes. QOL was measured using the Functional Assessment of Cancer Therapy-General (FACT-G), Functional Assessment of Chronic Illness Therapy–Fatigue Scale, State-Trait Anxiety Inventory (STAI), and Profile of Mood States instruments and with a numeric rating scale for overall QOL and spiritual QOL. The autoHCT group and no HCT groups were compared at 3 years (FU3) and 6 years (FU6) after lymphoma diagnosis. RESULTS: In total, 980 patients with lymphoma (106 autoHCT and 874 no HCT) diagnosed between 2002 and 2013 were included for analysis. The mean FACT-G total score was similar in the autoHCT and no HCT groups at FU3 (89.9 v 90.1, P = .64) and also at FU6 (91.5 v 89.6, P = .44). No differences between the autoHCT and no HCT groups were identified in the FACT subscales. The STAI identified lower anxiety in the autoHCT group by mean STAI1 (state) at FU3 (30.1 v 33.4, P < .01) and by mean STAI2 (trait) at FU6 (30.1 v 33.5, P = .02). No other clinically meaningful differences were identified between the two groups using the other QOL instruments. CONCLUSION: Patients remaining in remission at 3 and 6 years after diagnosis had a high level of QOL with no significant differences associated with history of treatment with autoHCT.

PURPOSE: Patients' concerns regarding clinical trial (CT) participation include apprehension about side effects, quality of life (QoL), financial burden, and quality of care. METHODS: We prospectively evaluated the experience of patients with multiple myeloma or lymphoma who were treated on CTs (CT group, n = 35) versus patients treated with standard approaches (non-CT group, n = 88) focusing on QoL, financial burden of care, and patients' perception of quality of care over a 1-year period. RESULTS: There were no significant differences in any of the patient-reported outcomes in CT versus non-CT groups. We observed an initial decline in overall QoL in the first 3 months across both groups, driven primarily by physical and functional well-being. QoL gradually improved and was above baseline by month 12. Patients reported highest improvement in the functional well-being subdomain. Patients in both groups reported high satisfaction with the quality of care received, and there were no differences in overall satisfaction, communication with team, or access to care. At baseline, 16%-19% of patients reported financial burden, which increased to a peak of 33% in the CT group and to 49% in the non-CT group over the course of 1 year. There was no significant difference in financial burden in the two groups overall. Most of the patients reported getting all the care that was deemed medically necessary in both groups. However, a significant proportion of patients reported having to make other kinds of financial sacrifices because of their cancer (CT group: 33% of patients at baseline and 21%-40% over 1 year; non-CT group: 19% at baseline and 25%-36% over 1 year). CONCLUSION: Patients treated on CTs reported comparable QoL and quality of care with the non-CT group. A high proportion of patients reported financial burden over time in both groups. Our findings can serve as a guide to educate patients regarding CT participation and highlight the need to address the significant financial burden experienced by patients with cancer.

Abstract Background Treatment burden refers to the work involved in managing one’s health and its impact on well-being and has been associated with nonadherence in patients with chronic illnesses. No kidney transplant (KT)-specific measure of treatment burden exists. The aim of this study was to develop a KT-specific supplement to the Patient Experience with Treatment and Self-Management (PETS), a general measure of treatment burden. Methods After drafting and pretesting KT-specific survey items, we conducted a cross-sectional survey study involving KT recipients from Mayo Clinic in Minnesota, Arizona, and Florida. Exploratory factor analysis (EFA) was used to identify domains for scaling the KT-specific supplement. Construct and known-groups validity were determined. Results Survey respondents (n = 167) had a mean age of 61 years (range 22–86) and received a KT on average 4.0 years ago. Three KT-specific scales were identified (transplant function, self-management, adverse effects). Higher scores on the KT-specific scales were correlated with higher PETS treatment burden, worse physical and mental health, and lower self-efficacy (p < 0.0001). Patients taking more medications reported higher transplant self-management burden. Conclusions We developed a KT-specific supplement to the PETS general measure of treatment burden. Scores may help providers identify recipients at risk for nonadherence.

Background In clinical trials and clinical practice, patient-reported outcomes are almost always assessed using multiple patient-reported outcome measures at the same time. This raises concerns about whether patient responses are affected by the order in which the patient-reported outcome measures are administered. Methods This questionnaire-based study of order effects included adult cancer patients from five cancer centers. Patients were randomly assigned to complete questionnaires via paper booklets, interactive voice response system, or tablet web survey. Linear Analogue Self-Assessment, Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events, and Patient-Reported Outcomes Measurement Information System assessment tools were each used to measure general health, physical function, social function, emotional distress/anxiety, emotional distress/depression, fatigue, sleep, and pain. The order in which the three tools, and domains within tools, were presented to patients was randomized. Rates of missing data, scale scores, and Cronbach’s alpha coefficients were compared by the order in which they were assessed. Analyses included Cochran–Armitage trend tests and mixed models adjusted for performance score, age, sex, cancer type, and curative intent. Results A total of 1830 patients provided baseline patient-reported outcome assessments. There were no significant trends in rates of missing values by whether a scale was assessed earlier or later. The largest order effect for scale scores was due to a large mean score at one assessment time point. The largest difference in Cronbach’s alpha between the versions for the Patient-Reported Outcomes Measurement Information System scales was 0.106. Conclusion The well-being of a cancer patient has many different aspects such as pain, fatigue, depression, and anxiety. These are assessed using a variety of surveys often collected at the same time. This study shows that the order in which the different aspects are collected from the patient is not important.

Abstract Work history is associated with long term health outcomes We hypothesize that characteristics of the first work experience, such as age at first job and length of work (hereafter job) are associated with future risk of hospitalization. We further hypothesize that the length of work will be associated with hospitalization. We conducted a survey of adults &gt;60 years using a nested case-control approach within the Mayo Clinic Biobank. We collected job related variables including age at job start, reason for ending, and length of time. To test associations between each variable and hospitalization, we used age and gender adjusted logistic regression models. Our study included 4,024 subjects: 1,801 cases and 2223 controls. The mean age at time of match was 77.3 years (SD 7.2 years) with 49.2% males. Older age at the first full-time job was associated with lower chance of hospitalization later in life (OR=0.81 [0.67, 0.97] for those who started the job over 22 compared to those started at 18 or less). Cases were more likely to have stopped working because of illness (OR=2.04 [95% CI 1.29,3.27]). Cases were less likely to have stopped working because of retirement (OR=0.82 [95% CI: 0.72, 0.93]). We found cases were employed with a slightly shorter time (20.5 yrs. (SD 16.6)) compared to controls (21.8 yrs. (SD 16.3)) (p=0.005). Cases started work earlier and stopped work more frequently because of illness/disability compared to controls. This could reflect educational attainment in controls. This study highlights work history as potential predictor of future hospitalization.

BACKGROUND: The pattern of perioperative use of personal electronic devices (PEDs) among anesthesia providers in the United States is unknown. METHODS: We developed a 31-question anonymous survey of perioperative PED use that was sent to 813 anesthesiologists, anesthesiology residents, and certified registered nurse anesthetists at 3 sites within one health system. The electronic survey assessed patterns of PED use inside the operating room (OR), outside the OR, and observed in others. Questions were designed to explore the various purposes for PED use, the potential impact of specific hospital policies or awareness of medicolegal risk on PED use, and whether PED was a source of perioperative distraction. RESULTS: The overall survey response rate was 36.8% (n = 299). With regard to often/frequent PED activity inside the OR, 24% reported texting, 5% reported talking on the phone, and 11% reported browsing on the Internet. With regard to often/frequent PED activity outside the OR, 88% reported texting, 26% reported talking on the phone, and 63% reported browsing the Internet. With regard to often/frequent PED activity observed in others, 52% reported others texting, 14% reported others talking on the phone, and 34% reported others browsing the Internet. Two percent of respondents self-reported a distraction compared to 15% who had observed a distraction in others. Eighty percent of respondents recognized PED as a potential distraction for patient safety. CONCLUSIONS: Our data reinforce that PED use is prevalent among anesthesia providers.

Introduction: Little is known about the characteristics and resources that enable patients with heart failure (HF) to engage in effective self-management. To address this gap in knowledge, we measured personal and health care resources for self-management and examined associations with mortality among patients with HF. Methods: We surveyed 5543 residents of 11 counties in Southeast Minnesota with a first-ever code for HF [International Classification of Disease, Ninth Revision code 428 or Tenth Revision code I50] between 1/1/2013 and 3/31/2016. Self-management resources were measured with the health care and personal subscales of the Chronic Illness Resources Survey (CIRS), both of which included 3 questions on a 5-point scale. The responses were averaged and participants were categorized as low if the mean score was below the median of the distribution (range from 1 to 5). The survey was returned by 2866 participants (response rate 52%) and those with complete data on the main items of interest were retained for analysis (N=2212). Cox proportional hazards regression was used to determine the association between each subscale and mortality. Results: Among 2212 participants (mean age 72.8 years, 54.1% men) the median health care score was 4, while the personal score was 3. Those with low health care resources were older and less educated than those with a higher score (p<0.05), while those with low personal resources had less comorbidities and lower education attainment compared to those with a higher score (p<0.05). After a mean (SD) follow-up of 1.3 ± 0.6 years, 207 deaths occurred. Low levels of both self-management resources were associated with an increased risk of death compared with patients with high levels (Table). Conclusions: Having limited self-management resources is associated with an increased risk of mortality among patients with HF. Thus, interventions aimed at supporting self-management among patients with HF may improve outcomes.

Abstract Background: Most indolent non-Hodgkin lymphomas (NHL) are considered incurable and are characterized by relapse and remission. Quality of life (QOL) can be affected by disease burden, side effects of treatment, and psychosocial effects of living with an incurable cancer. However, little is known about QOL in this population. We sought to describe QOL at diagnosis, change in QOL over time, and determinants of QOL in patients with indolent NHL. Methods: Newly diagnosed lymphoma patients were prospectively enrolled within 9 months of diagnosis in the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource. Eligibility criteria included age ≥18 years; exclusion criteria included HIV-positive, non-US residency, and inability to complete surveys in English. All pathology was reviewed by a hematopathologist. Patients with indolent B-cell NHL were included in this analysis. QOL was measured at baseline (BL) and 3 year follow-up (FU3) with the Functional Assessment of Cancer Therapy-General scale (FACT-G), which measures 4 QOL domains: physical, social/family, emotional, and functional well-being (WB), and a single item Linear Analogue Self-Assessment (LASA) for measuring overall QOL. Patients who did not complete ≥80% of FACT-G questions were excluded. Clinical data were abstracted according to a standard protocol and patients were systematically followed for events (progression, re-treatment, and death). CLL patients were excluded for event analysis due to differing definitions of outcome (progression/retreatment vs. time to first treatment). Treatment was defined as any systemic therapy (including chemotherapy, targeted agents, or antibody therapy) or radiation. QOL scores were transformed to a 0-100 scale with 100 representing the best WB or QOL for analysis. Change in QOL was assessed using the raw difference in QOL scores (i.e., FU3-BL) on the 0-100 scale with positive change scores representing improvements; minimally important difference was defined as 7 points on the transformed FACT-G subscales per previously established cutoffs (Yost and Eton, Eval & Health Prof. 2005 Jun;28:172-91). Changes in QOL from BL to FU3 were assessed using one-sample t-test of change scores vs zero. Results: 1050 patients with indolent NHL enrolled between 9/02-12/2012 and with both pre-treatment and 3 year QOL were included. QOL was collected after initiation of therapy in 215 patients (20%) and prior to therapy in 835 (80%). Subtype was 41% CLL/SLL, 32% grade 1-2 FL, 14% extranodal marginal zone, and 13% other (lymphoplasmacytic lymphoma and other). Median age was 62 years and 55% were male. At the FU3 assessment, 577 patients (55%) had received treatment, 42 (4%) transformed, and 53 (25%) had an event. Emotional WB significantly improved from BL to FU3 (mean +5.9, SD 13.3, p&lt;0.001) while social/family WB significantly decreased (mean -4.8, SD 20.5, p&lt;0.001). Functional WB, physical WB, overall FACT-G, and overall QOL LASA were not significantly different at FU3. These findings were similar in those who were treated vs. those who were observed. We also assessed the results stratified by non-CLL patients who had progression or retreatment between BL and FU3. In patients with an event in the first 3 years, emotional WB had a significant improvement from BL to FU3 (mean +3.6, SD 13.6, p=0.002) and overall QOL LASA decreased (mean -4.0, SD 20.0, p=0.01). In patients without an event, improvements were reported in functional WB (mean +2.1, SD 18.2, p=0.01), physical WB (mean +2.5, SD 13.9, p=0.001), emotional WB (mean +7.0, SD 15.0, p&lt;0.001), and overall QOL LASA (mean +1.1, SD 11.4, p=0.03), while social/family WB decreased (mean -6.2, SD 20.3, p&lt;0.001). Sensitivity analysis showed that results were consistent in the subset of patients who had BL QOL collected prior to initiating therapy. While many changes were statistically significant, they were small in magnitude; only the improvement in emotional WB in the entire cohort approached clinical significance and met that criterion in those without an event. Conclusions: In this large sample of patients with indolent lymphoma, QOL generally remained stable from diagnosis to FU3 while emotional WB showed improvement, suggesting psychosocial adaptation by the patient. Mean score changes in functional WB, physical WB, emotional WB, social/family WB, and overall QOL were more pronounced in patients without an event in the first 3 years since diagnosis. Disclosures Shanafelt: Hospira: Research Funding; AbbVie: Research Funding; Celgene: Research Funding; GlaxoSmithKline: Research Funding; Genentech: Research Funding; Jannsen: Research Funding; Pharmacyclics: Research Funding.

Introduction: Lymphoma is the most common cancer among adolescents and young adults (AYAs). We examined changes in health-related quality of life (HRQoL) and its predictors in AYA patients (pts). Patients and Methods: We identified AYA pts (aged 18-39) enrolled 2002-2015 in a prospective cohort of pts with newly diagnosed lymphoma from the University of Iowa and Mayo Clinic Molecular Epidemiology Resource, part of the Lymphoma Epidemiology of Outcomes cohort. Enrollment could occur prior to or after initiation of treatment. We measured HRQoL using the Functional Assessment of Cancer Therapy-General (FACT-G) questionnaire at baseline, 12, and 24 months. FACT-G yields five HRQoL domain scores: emotional well-being (EWB), functional WB (FWB), physical WB (PWB), social/family WB (SFWB), and total FACT-G score (a sum of the domains). Pts completing &lt;80% of the FACT-G questions were excluded. Linear mixed models with random subject intercepts estimated changes in FACT-G scores from baseline. The covariates in multivariate analysis were lymphoma subtype, stage, and treatment. Interaction effects between treatment (chemotherapy and/or radiation) and subtype were added to the model. We calculated effect sizes (ES) for the magnitude of mean change scores: 0.2, 0.5, and 0.8 were considered small, medium, and large ESs, respectively. Only ESs for mean score differences with p&lt;0.05 are reported. Results: We identified 467 pts; median age at diagnosis was 30 years, median follow up was 5.9 years. 53% of pts completed the baseline FACT-G assessment pre-treatment, 47% completed after treatment began. Pts assessed after treatment initiation had lower baseline total FACT-G (ES -0.25), FWB (ES -0.27), and PWB scores (-0.46); but baseline EWB was higher in pts assessed prior to treatment (ES 0.20). There was no association between HRQoL scores at baseline or over time and lymphoma subtype, stage, or treatment type, or interactions. Total FACT-G scores modestly improved over time, ES 0.32 at 1 year and ES 0.45 at 2 years after enrollment. EWB, FWB, and PWB also improved over time (ES 0.36, 0.44, 0.30 at 1 year; and 0.49, 0.56, 0.38 at 2 years, respectively). SFWB scores slightly worsened over time (ES -0.24 at 1 year and -0.12 at 2 years). Conclusions: AYA pts with lymphoma had higher baseline total FACT-G scores, FWB and PWB prior to therapy initiation compared to after initiation. HRQoL improved from diagnosis through the first 2 years after diagnosis, except for SFWB. Neither stage, lymphoma subtype, nor treatment type affected change in HRQoL. The lack of improvement in SFWB suggests social interventions and future studies should examine factors impacting SFWB in AYA pts. Disclosures Cohen: Genentech, BMS, Novartis, LAM, BioInvent, LRF, ASH, Astra Zeneca, Seattle Genetics: Research Funding; Janssen, Adicet, Astra Zeneca, Genentech, Aptitude Health, Cellectar, Kite/Gilead, Loxo: Consultancy. Flowers:Leukemia and Lymphoma Society: Membership on an entity's Board of Directors or advisory committees; National Cancer Institute: Research Funding; AbbVie: Consultancy, Research Funding; Cancer Prevention and Research Institute of Texas: Research Funding; Eastern Cooperative Oncology Group: Research Funding; Burroughs Wellcome Fund: Research Funding; Kite: Research Funding; V Foundation: Research Funding; TG Therapeutics: Research Funding; Millennium/Takeda: Consultancy, Research Funding; Acerta: Research Funding; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy; Karyopharm: Consultancy; OptumRx: Consultancy; Gilead: Consultancy, Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; Denovo Biopharma: Consultancy; Celgene: Consultancy, Research Funding; BeiGene: Consultancy; Bayer: Consultancy. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding.