Sloan, Jeff A.

Abstract BACKGROUND The impact of treatment on both the quality and the quantity of life, i.e. the ‘net clinical benefit’, should be considered to inform glioma patients and facilitate shared decision making. We applied two methods (i.e. Quality Adjusted Effect Sizes (QASES) and Joint Modelling (JM)) that combine survival and health-related quality of life (HRQoL) data into one outcome, to gain insight in the net clinical benefit of a treatment strategy. In addition, we assessed if both methods result in similar interpretations. MATERIAL AND METHODS We calculated the net clinical benefit in one randomized controlled trial, EORTC 26951 comparing radiotherapy (RT) + PCV chemotherapy versus RT alone, as a proof of concept for other trials. With the QASES method, effect sizes for differences in survival and HRQoL between treatment arms were calculated. Next, the combined effect size can be determined by weighing the emphasis put on survival or HRQoL (e.g. survival more important). JM allows simultaneous modeling of a longitudinal outcome (HRQoL), and a time-to event outcome (survival). HRQoL scales/items that were selected for primary analysis in the main study were also selected for this analysis: fatigue, global health, social functioning, communication deficit, seizures, physical functioning, and nausea/vomiting. RESULTS 288/386 patients completed baseline HRQoL forms and were included in the analysis. Overall survival (OS) was significantly longer with combined treatment (difference of 10.8 months). In contrast, the percentage of patients who experienced a clinically relevant deterioration (≥10 points) in nausea/vomiting, fatigue, social functioning and global health up to one year after treatment compared to baseline was larger in the RT+PCV arm. The QASES corresponded to a reduction in the median OS difference from 10.8 months to 6.8 months when adjusted for the HRQoL scales/items, when given equal weights to OS and HRQoL. JM analyses resulted in a theoretical loss of treatment effect in OS of 2–6% when adjusting for HRQoL. CONCLUSION Both methods showed that adjusting for the impact of treatment on a relevant HRQoL parameter reduced the survival benefit in the experimental treatment arm compared to standard treatment arm. Applying these methods may facilitate communicating the impact of treatment to patients in clinical practice.

Abstract Background A better understanding of social determinants of health and psychosocial and economic sequelae of cancer for an ethnoculturally diverse population is needed. A Patient-Reported Outcomes Quality of Life (PROQOL) instrument has been developed to capture less commonly discussed patient concerns. We sought to adapt this instrument and pilot it in underserved, ethnic minority cancer patients at Maricopa Integrated Health System (MIHS) in Phoenix, AZ. Methods Two focus groups (FG) were conducted to understand patient perspectives of an adapted Spanish version of PROQOL. A revised version based on FG data was then piloted among patients on active cancer treatment. Patients also completed a Linear Analog Scale Assessment (from 1-10) for quality of life (QOL) and its domains. Results All participants in both FG (11/12 female) were Hispanic, with a median age of 53 years. Participants agreed that the domains covered in the PROQOL represented the most important psychosocial needs in their cancer continuum. No difficulty in understanding the individual domains or questions for delving deeper into each domain was noted. Median age of 34 pilot study participants (74% female, 79% Hispanic) was 48 years. 28/34 completed the Spanish version. The most common concerns were ‘Money’ (32%) and ‘Cancer Diagnosis, Treatment and Survivorship’ (32%). Median overall QOL was 8 (range 3-10). Conclusions PROQOL is feasible for systematic capture of patient concerns in underserved, ethnic minority cancer patients. A better understanding of these concerns can help design interventions to improve psychosocial outcomes for the vulnerable groups and decrease disparities in care delivery.

e18132 Background: Older adults (65+ years) make up > 50% of cancer patients in the US,1 yet, older patient enrollment onto trials remains < 30%.2 This study’s aim was to determine the association between trial level variables and accrual rates of adults 65+ to better design trials that can more improve enrollment of these patients. Methods: We analyzed enrollment percentages of patients age 65+ in non-age specific Alliance trials with > 50 patients, that completed accrual between 1995- 2015. Enrollment percentage by 65+ for each study was compared to cancer incidence percentage by 65+ according to the Surveillance, Epidemiology, and End Results (SEER) Program for the same time periods. Enrollment Disparity Difference (EDD)3, defined as the difference between SEER incidence percentage of patients 65+ and the trial percentage enrollment of 65+ was calculated for each trial ( SEER % – trial %). Univariate analyses (UVA) were performed using simple linear regression to determine trial variables associated with larger EDDs (higher disparity). Variables that had an F-test p-value < 0.20 were included in a multivariable fixed-effects linear model for multivariate analysis (MVA). Results: Median age of 66,708 patients across 237 trials with 10 cancer types was 60 years. Average % of 65+ in trials was lower than average SEER comparison % 65+ per trial (37.6% vs. 57.8%). The median trial EDD, was 16.7%. On UVA, cancer type was significantly associated with higher EDD (see Table). Use of fewer modalities (1, 2, vs 3+) was associated with lower estimated EDDs (1 = -11 [+/-3.6], 2 = -8.9 [+/-3.9], F-test p = .012). On MVA, non-genitourinary (GU) cancer type, number of treatment modalities, and the phase (II or III compared to observational/other) were all significantly associated with higher EDD. Conclusions: Older adults were under-represented in this large sample of trials. This information can be used as a platform to better design disease-specific trials to improve older patient accrual, particularly in disease areas which are under-performing. Support: U10CA180821, U10CA180882, UG1CA189823: https://acknowledgments.alliancefound.org . References: 1. Smith BD et al. J Clin Oncol 27:2758-65, 2009; 2. Hurria A, et al. J Geriatr Oncol 1:40-44, 2010; 3. Pang HH, et al. J Clin Oncol, 2016.[Table: see text]

Background: Prospective, randomized trials are needed to determine optimal treatment approaches for palliative care problems such as malignant bowel obstruction (MBO). Randomization poses unique issues for such studies, especially with divergent treatment approaches and varying levels of equipoise. We report our experience accruing randomized patients to the Prospective Comparative Effectiveness Trial for Malignant Bowel Obstruction (SWOG S1316) study, comparing surgical and nonsurgical management of MBO. Methods: Patients with MBO who were surgical candidates and had treatment equipoise were accrued and offered randomization to surgical or nonsurgical management. Patients choosing nonrandomization were offered prospective observation. Trial details are listed on www.clinicaltrials.gov (NCT #02270450). An accrual algorithm was developed to enhance enrollment. Results: Accrual is ongoing with 176 patients enrolled. Most (89%) patients chose nonrandomization, opting for nonsurgical management. Of 25 sites that have accrued to this study, 6 enrolled patients on the randomization arm. Approximately 59% (20/34) of the randomization accrual goal has been achieved. Patient-related factors and clinician bias have been the most prevalent reasons for lack of randomization. An algorithm was developed from clinician experience to aid randomization. Using principles in this tool, repeated physician conversations discussing treatment options and goals of care, and a supportive team–approach has helped increase accrual. Conclusions: Experience gained from the S1316 study can aid future palliative care trials. Although difficult, it is possible to randomize patients to palliative studies by giving clinicians clear recommendations utilizing an algorithm of conversation, allotment of necessary time to discuss the trial, and encouragement to overcome internal bias.

Introduction: What if you could only ask one question of the patient during a clinic visit? Further, suppose the patient’s biggest concern can pragmatically be incorporated into routine clinical care and clinical pathways that can address the patient’s single biggest concern can be identified. If the principal concern can be dealt with efficiently at each visit through key stakeholder case management, positive outcomes should result. Therefore, motivated by the need for patient-centered health care visits, the Beacon electronic patient-reported outcomes (PRO) quality of life (QOL) tool was developed. Methods: Central to the tool is that at each health care visit, the patient’s biggest concern is electronically communicated to the health care team. Therefore, the tool can help catalyze important discussions between the health care team and the patient, perhaps on topics that would not have been discussed otherwise at a routine visit. In recognition of the community of resources needed to provide comprehensive care, the tool generates clinical pathways or actions that can be pursued to address the patient’s biggest concern. The concern is efficiently triaged such that members of the health care community with appropriate expertise and resources are identified to address and manage that single biggest concern signaled by the patient. A report, which can be uploaded into the patient’s medical chart, is created and provides a list of resources for a case manager to assist the patient and contains graphical presentations of the patient’s QOL and a history of prior concerns. The report also labels potentially significant changes in QOL. Discussion: The tool, which has been applied successfully in several health conditions, acts as a beacon to health care providers so that a patient’s self-reported concern can be consistently and effectively integrated into their care. Key Points: It is impractical to try to deal with every patient concern in every visit. The key to the Beacon tool is that at each visit the patient’s biggest concern is identified, clinical pathways indicated, and resources efficiently matched to address the patient’s biggest concern.

We studied family members’ care experiences during the palliative care phase as predictors of family members’ health and family functioning during the palliative care phase and three months following the death of the patient. Eighty family members of advanced cancer patients participated in the study at time one (T1) (palliative care phase) and 64 family members completed the data collection protocol at time two (T2) (three months bereavement). Data were also obtained from 36 of the patients within one month of their deaths. Fulfillment theory accounted for 31% of the variance in family care satisfaction and Discrepancy theory accounted for 72% of the variance in family care satisfaction. Discrepancy theory predicted family functioning in the bereavement period (r=-0.33, p<0.05). Patients’ quality of life scores were moderately correlated with family members’ health during the palliative care phase (r=-0.38, p<0.05). Family members’ scores on the health index (symptom of stress scale) were significantly lower (p<0.05) than normative scores reported in a study using a healthy population. The strongest predictor of family members’ health scores in the bereavement period was their health score at T1 (r=0.71, p<0.01). As well, family functioning at T1 was strongly correlated with family functioning at T2.