Griffin, Joan M.

Abstract To develop an operational definition of and typologies for episodes of lucidity (EL), we conducted a cross-sectional study of former/current family caregivers from UsAgainstAlzheimer’s A_LIST (N = 538). More than 60% of caregivers (n = 294, 62%) reported witnessing EL with their care recipient over the course of their dementia. Most episodes happened in late stages of dementia (71%). Only 10% happened within 7 days before death. The majority of episodes (71%) lasted <30 minutes. About half the episodes were characterized by uncharacteristic speech and communication. Caregivers perceived these experiences positively (M = 4.10; range = 1–5), but also expressed desire to know why/when EL occurs and how to best respond to it. Data will be used to refine definitions and typologies, and then a prospective, demographically diverse survey will be administered to family caregivers to assess predictors of EL, linking EL to caregiver well-being and bereavement response.

Abstract Background: Self-reported financial hardship (FH) amongst cancer patients is increasingly becoming a challenge for patients, caregivers, and healthcare providers. FH not only leads to financial struggles, significant lifestyle changes, and emotional distress, but also contributes to treatment noncompliance, affecting clinical outcomes. As treatment costs rise, it is crucial to develop efficient methods to proactively identify and alleviate FH in hematology practice. One potential approach is utilizing automated processes to identify those at highest risk of FH. At Mayo Clinic, screening for FH involves using a single financial strain question 'How hard is it for you to pay for the very basics like food, housing, medical care, and heating?' which all cancer patients answer annually as part of the institution's Social Determinants of Health (SDOH) assessment. Answers are on a five-point scale including not hard at all, not very hard, somewhat hard, hard, and very hard. In this study, we assess the prevalence and predictors for FH (denoted by a response of "Very hard" "Hard" or "Somewhat hard") amongst the Mayo Clinic hematologic malignancy patient population. Our study objective was to determine if this automated process could identify those at risk for FH. Methods: Patients who received care for hematologic malignancies (lymphoma, leukemia, plasma disorders, myelodysplastic/myeloproliferative disorders, and other heme malignancies) at any of the Mayo Clinic cancer centers (Minnesota, Arizona, and Florida) and who had completed the SDOH screen at least once were included in this study. The electronic medical record (EMR) and Mayo Clinic Cancer Registry were utilized to extract demographic and disease variables. Patient's home zip code was used to determine rural/urban residence, distance from cancer center, and the Area Deprivation Index (ADI), a measure of socioeconomic disadvantage based on home zip code (ranging from 1-100, with 100 representing the most disadvantaged). Multivariable logistic regression modeling was used to examine predictor variables for FH in this patient population. Results: The final cohort included 10,024 patients from 2018 to 2020. Median age was 64.6 years (IQR 58.1,73.7), 58% were male, and 79% married. Race/ethnicity composition was 94% White (n=9,268), 2.5% Black (n=246), 0.4% American Indian/Alaskan Native (44), and 4% Hispanic (n=360). Fifty-six percent of patients had Medicare and 41% had commercial insurance. Fifty percent were retired, 40% were working/students, and 72% were urban residents. Mean ADI was 41.2. Fifty-six percent of patients had lymphomas, 23.5% had plasma cell disorders, 8.5% had leukemias, 6.8% had other hematological malignancies, and 5.5% had myelodysplastic/myeloproliferative neoplasms. FH was reported by 12.8% (n=1286) of the patients. Table 1 shows the results of the multivariable model. A significantly higher likelihood of endorsing FH was noted in Hispanic vs non-Hispanics, Black and American Indian/Alaskan Native groups vs whites, Disabled/Unemployed vs working, Medicaid, Medicare, and Self-Pay groups vs commercial insurance, higher ADI (5 th quintile vs 1 st), and myelodysplastic/myeloproliferative disorder and other hematologic malignancy vs lymphoma patients. Older age, being retired, and living farther from the cancer center were associated with significantly less likelihood of endorsing FH. Conclusion: Our study used automated data extraction from the EMR to efficiently identify predictors of FH in hematologic cancer patients. Employing a dichotomized and automated "flag" for FH, particularly if incorporated in the EMR, could ease the identification of SDOH issues, facilitate timely connection to appropriate resources, and help provide better patient-centered care. Figure 1 Figure 1. Disclosures Ailawadhi: Sanofi: Consultancy; Cellectar: Research Funding; Karyopharm: Consultancy; Ascentage: Research Funding; Genentech: Consultancy; Janssen: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Beigene: Consultancy; GSK: Consultancy, Research Funding; AbbVie: Consultancy; Medimmune: Research Funding; Pharmacyclics: Consultancy, Research Funding; Takeda: Consultancy; Amgen: Consultancy, Research Funding; Xencor: Research Funding. Fonseca: OncoTracker: Consultancy, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy; BMS: Consultancy; Mayo Clinic in Arizona: Current Employment; Aduro: Consultancy; AbbVie: Consultancy; GSK: Consultancy; Merck: Consultancy; Juno: Consultancy; Scientific Advisory Board: Adaptive Biotechnologies: Membership on an entity's Board of Directors or advisory committees; Patent: Prognosticaton of myeloma via FISH: Patents & Royalties; Novartis: Consultancy; Bayer: Consultancy; Celgene: Consultancy; Caris Life Sciences: Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy; Janssen: Consultancy; Amgen: Consultancy; Pharmacyclics: Consultancy; Sanofi: Consultancy. Griffin: Exact Sciences: Research Funding.

Abstract Background The symptom burden associated with cancer and its treatment can negatively affect patients’ quality of life and survival. Symptom-focused collaborative care model (CCM) interventions can improve outcomes, but only if patients engage with them. We assessed the receptivity of severely symptomatic oncology patients to a remote nurse-led CCM intervention. Methods In a pragmatic, cluster-randomized, stepped-wedge trial conducted as part of the National Cancer Institute IMPACT Consortium (E2C2, NCT03892967), patients receiving cancer care were asked to rate their sleep disturbance, pain, anxiety, emotional distress, fatigue, and limitations in physical function. Patients reporting at least 1 severe symptom (≥7/10) were offered phone consultation with a nurse symptom care manager (RN SCM). Initially, patients had to “opt-in” to receive a call, but the protocol was later modified so they had to “opt-out” if they did not want a call. We assessed the impact of opt-in vs opt-out framing and patient characteristics on receptiveness to RN SCM calls. All statistical tests were 2-sided. Results Of the 1204 symptom assessments (from 864 patients) on which at least 1 severe symptom was documented, 469 (39.0%) indicated receptivity to an RN SCM phone call. The opt-out period (odds ratio [OR] = 1.61, 95% confidence interval [CI] = 1.12 to 2.32, P = .01), receiving care at a tertiary care center (OR = 3.59, 95% CI = 2.18 to 5.91, P < .001), and having severe pain (OR = 1.80, 95% CI = 1.24 to 2.62, P = .002) were associated with statistically significantly greater willingness to receive a call. Conclusions Many severely symptomatic patients were not receptive to an RN SCM phone call. Better understanding of reasons for refusal and strategies for improving patient receptivity are needed.

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.

e18862 Background: Hematopoietic cell transplantation (HCT) is a medically complicated, expensive and resource intensive medical technology. High health care costs are usually associated with high Out-of-Pocket (OOP) costs. In this population-based study, using deidentified administrative claims data from OptumLabs Data Warehouse, we describe total and patient level costs for one-year pre and post HCT and describe the characteristics of those with top 25% cost. Methods: Patients who had undergone an autologous or allogeneic HCT in 2015 to 2019 and had continuous health plan coverage for one year prior and post the index date-the date of the first claim for HCT, were included. We examined total costs, patient paid (OOP) and plan paid costs for one year before and after HCT. We also examined inpatient, outpatient, and pharmacy costs for the one year before and after the HCT. Logistic regression models examined factors including age, gender, race/ ethnicity, Charlson comorbidity index, geographic region and costs in the year prior to HCT (baseline costs) for their association with high total costs and high OOP costs (top 25%). Results: A total of 3,346 patients (2,344 commercial plan (CPE) and 1,002 under Medicare Advantage plan (MAPE) enrollees) were included in the study. Median one year post HCT costs for CPE was $ 612,517 (IQR 413,348- 960,456) and for MAPE was $ 521,000 (IQR 347,388-736,685). Median one year post HCT OOP costs were $ 5,407 (IQR 1,584 -10,000) in CPE and $7,199 (IQR 3,485 -16,396) in MAPE group. 625 subjects in the CPE and 253 subjects in MAPE group had overall costs in the top 25%. Median baseline costs were $482,107 vs. $846,943 in the low vs. high cost group in CPE and $278,656 vs. $343,633 in MAPE. There was a low correlation between OOP costs and overall costs (r = 0.17 for CPE and 0.15 for MAPE; p < 0.05). Younger age (OR for age 0.98; p < 0.001) and higher baseline costs (OR1.89; p < 0.001) predicted top 25% total costs post HCT in the CPE group. Not being African American (OR for African American 0.5; p = 0.02) and higher baseline costs (OR 1.07; p < 0.001) predicted top 25% total costs post HCT in MAPE group. Age (OR 0.97; p < 0.001), not being African American (OR 0.5; p = 0.001) and high baseline costs (OR 1.44; p < 0.001) also predicted higher OOP costs in the CPE group. The only factor that was associated with lower likelihood of OOP costs in top 25% in MAPE group was being Hispanic (OR 0.6; p = 0.004). Conclusions: HCT is an expensive treatment modality. High costs prior to HCT are associated with high post HCT costs both for patients on commercial and Medicare Advantage plan. The magnitude of correlation between patient level and total costs was low, likely due to differences in benefit plan. A careful assessment of benefits and costs of HCT is needed for providing high-value care and allow optimum allocation of the finite resources while minimizing patient level financial burden.

6594 Background: Patient reported financial hardship (FH) in cancer care is a growing challenge for patients, their caregivers and healthcare providers. As treatment costs escalate, it is imperative to develop effective strategies to proactively recognize and mitigate FH within oncology practice. Using automated processes to screen and refer patients to appropriate resources is a potential option. At Mayo Clinic, screening for FH involves using a single financial strain question ‘ How hard is it for you to pay for the very basics like food, housing, medical care, and heating?’ completed by all cancer patients annually as part of the Social Determinants of Health (SDOH) assessment. In this study, we describe the prevalence and predictors for FH (denoted by the answer ‘hard and very hard’) in our patient population. Methods: Patients receiving cancer care at the three Mayo Clinic sites (Minnesota, Arizona, and Florida) who completed the FH screen at least once were included in this study. Demographics (age, gender, race/ ethnicity, insurance, employment status, marital status, and zip code) and disease state data for included patients was extracted from the EMR and Mayo Clinic Cancer Registry. Disease state was categorized by type of cancer (hematological or solid malignancy) and cancer stage. Zip code was used to derive median income, rural/urban residence and distance from the cancer center. Multivariable logistic regression models were utilized to examine factors associated with FH. Results: The final study cohort included 31,969 patients with median age 66 years (IQR 57,73), 51% females, and 76% married. Race/ethnicity composition was 93% White, 3% Black, and 4% Hispanic. 52% of patients had Medicare and 43% had commercial insurance. Other notable factors included 48% retired, 41% working/ students, 76% married, and 72% urban residents. Median time from cancer diagnosis was 1.1 year (IQR 0.1, 3.8) and median income was $64,406 (IQR 53,067, 82,038). 31% of patients had hematological malignancies, 20% of the cancers for which staging information was available were metastatic. FH was reported by 4% (n = 1194) of the patients. A significantly higher likelihood of endorsing FH (p < 0.001 for all) was noted in Hispanic (OR 1.64), Black (OR 1.84), American Indian/Alaskan native (OR 2.02), below median income (OR 1.48), rural (OR 1.17), self-pay (OR 2.77), Medicaid (OR 2.29), Medicare (OR 1.43), unemployed/disabled (OR 2.39), single (OR 2.07), or divorced (OR 2.43) patients. Older age, being retired, and living farther from the cancer center were associated with significantly less likelihood of endorsing FH. Conclusions: Our study successfully leveraged the EMR to identify key sociodemographic groups more likely to report FH. An electronic trigger to flag such patients at high-risk of FH and proactively address FH is currently being developed.

AbstractRecommended colorectal cancer screening modalities vary with respect to safety, efficacy, and cost. Better understanding of the factors that influence patient preference is, therefore, critical for improving population adherence to colorectal cancer screening. To address this knowledge gap, we conducted a panel survey focused on three commonly utilized colorectal cancer screening options [fecal immunochemical test or guaiac-based fecal occult blood test (FIT/gFOBT), multi-target stool DNA (mt-sDNA) test, and colonoscopy] with a national sample of U.S. adults, ages 40–75 years and at average risk of colorectal cancer, in November 2019. Of 5,097 panelists invited to participate, 1,595 completed the survey (completion rate, 31.3%). Our results showed that when presented a choice between two colorectal cancer screening modalities, more respondents preferred mt-sDNA (65.4%) over colonoscopy, FIT/gFOBT (61%) over colonoscopy, and mt-sDNA (66.9%) over FIT/gFOBT. Certain demographic characteristics and awareness of and/or experience with various screening modalities influenced preferences. For example, uninsured people were more likely to prefer stool-based tests over colonoscopy [OR, 2.53; 95% confidence interval (CI), 1.22–5.65 and OR, 2.73; 95% CI, 1.13–7.47]. People who had heard of stool-based screening were more likely to prefer mt-sDNA over FIT/gFOBT (OR, 2.07; 95% CI, 1.26–3.40). People who previously had a stool-based test were more likely to prefer FIT/gFOBT over colonoscopy (OR, 2.75; 95% CI, 1.74–4.41), while people who previously had a colonoscopy were less likely to prefer mt-sDNA or FIT/gFOBT over colonoscopy (OR, 0.39; 95% CI, 0.24–0.63 and OR, 0.40; 95% CI, 0.26–0.62). Our survey demonstrated broad patient preference for stool-based tests over colonoscopy, contrasting the heavy reliance on colonoscopy for colorectal cancer screening in clinical practice and highlighting the importance of considering patient preference in colorectal cancer screening recommendations.Prevention Relevance:Our national survey demonstrated broad patient preference for stool-based tests over colonoscopy, contrasting the heavy reliance on colonoscopy for colorectal cancer screening in clinical practice and highlighting the importance of considering patient preference in colorectal screening recommendations.

We conducted a survey of primary care clinicians and gastroenterologists (n = 938) between 11/06/19–12/06/19 to assess knowledge and attitudes regarding colorectal cancer screening. We assessed clinicians’ attitudes toward lowering the colorectal cancer screening initiation age to 45 years, a topic of current debate. We also evaluated provider and practice characteristics associated with agreement. Only 38.1% of primary care clinicians endorsed colorectal cancer screening initiation at age 45 years, compared to 75.5% of gastroenterologists (p < .0001). Gastroenterologists were over 5 times more likely than primary care clinicians to endorse lowering the screening initiation age (OR = 5.30, 3.54-7.93). Other factors found to be independently associated with agreement with colorectal cancer screening initiation at age 45 years included seeing more than 25 patients per day (vs. fewer) and suburban (vs. urban) location. Results emphasize the need for collaboration between primary care clinicians and gastroenterologists to ensure that patients receive consistent messaging and evidence-based care.