Aug, Sarah

Abstract Frailty influences treatment outcomes in older patients with multiple myeloma (MM), yet existing frailty tools capture limited geriatric domains and are incompletely studied in the context of contemporary therapies. We prospectively evaluated patient-reported comprehensive geriatric assessment (CGA) domains and their associations with treatment toxicity and patient-reported outcomes (PRO) in older adults with MM. Patients aged ≥60 years with newly diagnosed or relapsed/refractory MM initiating a new therapy completed electronic CGA and PRO questionnaires at baseline and 4 months. Outcomes included clinician- and patient-reported treatment toxicity, treatment adaptations, health care use, and health-related quality of life (HRQOL). A cumulative deficit frailty index (FI) was derived from CGA and HRQOL domains. Among 90 patients (mean age, 72 years), geriatric impairments were common, particularly physical function domains. High-grade nonhematologic toxicities were significantly associated with older age, comorbidities, patient-reported functional limitations, and anxiety. Toxicity rates on the PRO version of the Common Terminology Criteria for Adverse Events (CTCAE) were higher than on CTCAE, highlighting a discordance between physician- and patient-reported treatment toxicity. Frailty was present in 22% and associated with worse baseline HRQOL and increased high-grade nonhematologic toxicity but not treatment adaptations or health care use. Average CGA and HRQOL scores remained stable over 4 months, although individual trajectories varied. Patient-reported geriatric impairments are prevalent in older adults with MM and strongly associated with nonhematologic toxicities even in the contemporary treatment era. Physician-reported CTCAE may not fully reflect patients' symptom burden. Heterogeneity in individual geriatric trajectories during treatment emphasize the need for longitudinal assessment. This study was registered at www.clinicaltrials.gov as NCT05918185.

Abstract Frailty predicts adverse outcomes in multiple myeloma (MM), yet an objective frailty biomarker is lacking. Circulating senescence-associated secretory phenotype (SASP) factors correlate with frailty and adverse outcomes in chronic diseases, but their relevance in MM is unclear. In this study, we investigated the relationship between 36 plasma SASP factors, frailty, and clinical outcomes in: (1) a historical cohort using stored specimens from 59 patients with newly diagnosed MM, and (2) a prospective cohort of 36 patients initiating new MM treatment. Several SASP factors were significantly associated with frailty after adjusting for age, sex, and body mass index; GDF-15, TNF-RI, IL-6, and IL-8 showed positive correlations with both cumulative-deficit based and physical frailty indices. Additionally, significant age-adjusted associations were observed between SASP factors and treatment outcomes, including overall survival (OS), high-grade toxicity, and healthcare utilization. We developed a 5-factor SASP model that demonstrated superior predictive performance for OS compared to chronological age, R-ISS stage, and clinical frailty, and TNF-RI alone (5-year OS AUC: 0.90). These findings highlight the association between senescence biomarkers and frailty and OS, and support further investigation of SASP factors as objective frailty biomarkers in MM.

Abstract Introduction: Multiple myeloma (MM) predominantly affects older adults who vary widely in physiological status and treatment outcomes. Comprehensive Geriatric Assessment (CGA) is considered the gold standard for frailty assessment in older cancer patients (pts), but clinical useis limited. Current frailty tools for MM include only select GCA domains, rely on trial datasets, and do not incorporate pt-reported outcomes (PROs). Since their development, treatment strategies have evolved with the use of anti-CD38 MAbs and quadruplets in older pts. The impact of geriatric impairments on treatment tolerance in the era of modern therapy remains unclear and changes in geriatric domains during treatment are not well characterized. Aim: This prospective study evaluates the association between pt-reported CGA and treatment outcomes in older MM pts.Methods: We included MM pts ≥60 years initiating a new treatment at Mayo Clinic from March 2023-June 2024. Pts completed the CARG GCA (Hurria et al 2005) electronically prior to and after 4 treatment cycles. We collected data on toxicity using CTCAE and PRO-CTCAE and quality of life using the PROMIS10-GH SF.Results: Ninety pts were enrolled. Median age was 72 (range: 63-86), 42% female, and >90% non-Hispanic white. Over half (59%) were newly diagnosed with 41% and 56% receiving quadruplets and triplets, respectively. Among newly diagnosed pts, 34% had early stem cell transplant. Baseline median self- and physician- reported KPS were 80% and 90%, respectively, with high correlation (r=0.657). The median number of meds per pt was 9 (range:0-30), 39% had ≥3 comorbidities, 21% reported ≥1 fall in the past 6 months, and 40% reported ≥1 IADL limitation. On the Hospital Anxiety and Depression Scale, 3% and 7% had anxiety and depression, respectively. On the Blessed Orientation-Memory-Concentration test, 19% had mild cognitive impairment, and 1% had severe cognitive impairment. While only 19% had grade (G) ≥3 non-hematologic toxicity by CTCAE, 71% had ≥1 treatment delay, dose reduction, or discontinuation, and 39% had ≥1 ED visit or unplanned hospitalization within the first 4 cycles. This suggests physician grading of non-hem toxicities may inadequately capture toxicity burden from MM therapy. Notably, the rate of G≥3 toxicity by PRO-CTCAE was 49%, highlighting the discrepancy between physician- and pt-reported symptom burden. CGA domains significantly associated with G≥3 non-hem toxicity were: age (74 v 71.5, p=0.032), ≥3 comorbidities (65% v 33%, p=0.015), anxiety (18% v 0%, p=0.006), ≥1 IADL limitation (65% v 33%, p=0.018), physician KPS ≤80 (53% v 27%, p=0.042), limitations in vigorous activities (100% v 78%, p=0.023) and limitations in climbing several flights of stairs (88% v 56%, p<0.001). Predictors of PRO-CTCAE G≥3 toxicity were ≥1 falls (30% v 11%, p=0.030), ≥3 comorbidities (52% v 26%, p=0.011), polypharmacy (≥9 meds) (67% v 43%, p=0.023), and physician KPS ≤80 (43% v 22%, p=0.030). CGA domains associated with ≥1 treatment adjustments were polypharmacy (62% v 33%, p=0.017) and unintentional weight loss (35% v 12%, p=0.028). Polypharmacy was the only domain associated with healthcare utilization (68% v 45%, p = 0.040), which may reflect underlying multimorbidity and greater pain burden. On multivariable logistic regression, any level of limitation in climbing several flights of stairs (OR: 6.0, 95% CI 1.2-30.3; p= 0.030) and older age (OR: 1.1, 95% CI 1.0-1.1; p=0.032) were independently associated with G≥3 non-hem toxicity, reflecting impaired functional status and reduced physiologic reserve, while ≥3 comorbidities only trended toward significance (OR:1.9, 95% CI 1.1-3.9, p=0.051).Compared to baseline, there were no significant changes in CGA domains or PROMIS10 scores after 4 cycles. While there was no significant difference in the proportion of pts reporting limitations in IADLs or physical function, we observed heterogeneity among individual pts, with changes in all 3 directions (improvement, stability, and decline). Conclusions: Geriatric impairments are prevalent in older pts with MM; pt-reported impairments are significantly associated with G≥3 non-hem toxicities, need for treatment modifications, and increased healthcare utilization, even in the era of novel therapies. Physical functional trajectories during treatment are variable among individual pts, underscoring the need for personalized treatment strategies and reassessment of geriatric domains throughout treatment.

Abstract Background: Age-related clonal hematopoiesis (ARCH) is a precursor state to blood cancers and is associated with epigenetic age acceleration (EpiAgeAccel). While ARCH is associated with increased all-cause mortality and cardiovascular disease, not much is known about these risks with therapy-related clonal hematopoiesis (t-CH) – CH occurring in the context of prior cytotoxic chemotherapy/radiation exposure. Methods: Patients were prospectively recruited for CH and EpiAge assessment from Mayo Clinic between 2022 and 2024. CH status was determined using error corrected sequencing on peripheral blood (PB) DNA (sensitivity 0.5% VAF). Patients with CH were classified as t-CH if they had received chemotherapy or radiation prior to CH detection. All other CH cases were classifed as ARCH. For EpiAge analysis, PB DNA was analyzed using the Illumina MethylationEPIC array. The resulting beta values were used to obtain epigentic age estimates using Horvath’s clock, Hannum’s Clock, PhenoAge, DunedinPACE, and estimated telomere lengths (DNAmTL). Results: 106 (62%) of 170 patients assessed were found to have CH. CH patients were classified into ARCH-63 (59%) and t-CH-43 (41%). While the median chronological age (69 vs 67 years) was similar between ARCH and t-CH, t-CH patients had lower Hgb [10.9g/dL vs. 13g/dL (p=0.0001)], lower platelet [146x109/L vs. 192x109/L (p=0.0132)] and lower WBC [4.4x109/L vs. 5.2x109/L (p=0.0345)]. The 3 most common ARCH mutations were TET2 (20%), DNMT3A (19%), and ASXL1 (14%), while PPM1D (27%), TET2 (18%), and DNMT3A (14%) were most frequent in t-CH. There was no significant difference in median EpiAge via Horvath (68 vs 67.5years p=0.2826), Hannum (65 vs 66 years p=0.9333), or PhenoAge (55 vs 56 years p=0.618) between ARCH and t-CH. However, DunedinPACE of aging was higher in t-CH (1.27 vs 1.18 bioyear/chronoyear p=0.0148) and median DNAmTL was shorter in t-CH (6.35 vs 6.49kb p=0.0247), compared to ARCH (Note patients with telomere biology disorders were excluded). Multivariable regression analysis adjusting for chronologic age, smoking status, and male sex showed that both ARCH and t-CH had EpiAgeAccel via Horvath(5.8years p=0.0001, 3.4 years p=0.051), Hannum(5years p=0.001, 4.5 years p=0.007), and PhenoAge (6.7years p=0.002, 7.6 years p=0.002) clocks. t-CH more than doubled DunedinPACE (0.2 bioyear/chronoyear, p<0.001) of aging compared to ARCH (0.08 bioyear/chronoyear, p=0.026). t-CH also demonstrated a significant decrease in DNAmTL (0.34kb, p<0.001) in comparison to ARCH (0.17kb, p=0.015). With a median follow-up of 20 months, t-CH was associated with worse overall survival in comparison to ARCH, and non-CH patients (p=0.013, 93% vs. 90% alive at 10 months, 87 vs. 72% alive at 20 months). Conclusion: Our prospective CH cohort study validates the association between CH and EpiAgeAccel using several different methylation clocks and for the first time highlights an increased pace of aging and shorter DNAmTL in the context of inferior survival outcomes in t-CH. Citation Format: Marissa Li, Jenna Fernandez, Kristina Kirschner, Tamir Chandra, Kristen McCullough, Sarah Aug, Terra Lasho, Christy Finke, James Foran, Cecilia Arana Yi, Yael Kusne, Talha Badar, Abhishek Mangaonkar, Naseema Gangat, Rachel Simon, Alejandro Ferrer, Mrinal Patnaik. A prospective cohort study of biological aging and epigenetic age acceleration in 106 patients with age and therapy-related clonal hematopoiesis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Methylation, Clonal Hematopoiesis, and Cancer; 2025 Feb 1-4; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2025;85(3 Suppl):Abstract nr A015.

Abstract Several tools have been proposed for assessing frailty in multiple myeloma (MM), but most are based on clinical trial data sets. There are also limited data on the association between frailty and patient-reported outcomes and on the prognostic value of social determinants of health. This study evaluates the prognostic impact of frailty, based on the cumulative deficit frailty index (FI), and relationship and socioeconomic status (SES) in patients with newly diagnosed MM. This retrospective study included 515 patients with MM seen at Mayo Clinic (Rochester, MN) at diagnosis between 2005 and 2018. The FI was calculated using patient-reported data on activities of daily living and comorbidity data, with items scored as 0, 0.5, or 1, in which 1 indicated a deficit. The FI was calculated by dividing the total score by the number of nonmissing items. Frailty was defined as FI ≥0.15; 61% were nonfrail, and 39% were frail. Frailty and nonmarried/relationship status were associated with higher disease stage, decreased the likelihood of early transplantation, and independently associated with decreased survival. SES was not independently associated with survival. Frail patients reported worse scores for fatigue, pain, and quality of life. Approximately a quarter of patients had a deterioration in frailty status at 3 to 12 months, and <10% had improvement. In conclusion, a cumulative deficit FI was associated with higher symptom burden and decreased survival in a real-world cohort of patients with newly diagnosed MM. Frailty status is dynamic and should be reassessed during treatment. Social support has prognostic value and should be evaluated in clinical practice.

Background: Age-related clonal hematopoiesis (ARCH) is a precursor state to blood cancers and is associated with epigenetic age acceleration (EpiAgeAccel) (Aging Cell, 2021). While ARCH in combination with AgeAccel has the highest risk for all cause mortality and cardiovascular disease, not much is known about context-relevant clonal hematopoiesis (CRCH) - CH occurring in the context of prior chemo/radiation, bone marrow failure and germline predisposition syndromes. CRCH has a unique mutational signature enriched in genes in DNA damage response and repair (TP53 and PPM1D). To answer this question, we assessed EpiAge in a prospective cohort of patients with both ARCH and CRCH. Methods: Patients were prospectively recruited for CH and EpiAge assessment from the Mayo Clinic CH clinic between 2022 and 2024. Demographic and clinical data including chronologic age, BMI, sex, blood counts, and medical history were recorded at the time of enrollment. CH status was determined using error corrected sequencing on peripheral blood (PB) DNA. All somatic variants with a variant allele frequency (VAF) ≥0.5% were considered as CH. For EpiAge analysis PB DNA was analyzed using the Illumina MethylationEPIC array. The resulting beta values were used to obtain epigenetic age estimates using Horvath's clock (Genome Biol., 2013), Hannum's Clock (Mol Cell., 2013), PhenoAge (Aging, 2018), DunedinPACE (eLife, 2022), and predicted telomere length (Aging, 2019). Results: 106 (62%) of 170 patients assessed were found to have CH, with the most frequent mutations being TET2 (19%), PPM1D (17%), DNMT3A (16%), ASXL1 (9%), and TP53 (6%); median VAF 9.5%. Except for a higher median chronologic age (68 vs 60 years, p<0.001) in the CH group, baseline characteristics including BMI, comorbidities, baseline blood counts, sex and tobacco use history were similar between the two groups. The CH group demonstrated significant EpiAgeAccel by all three clocks: Horvath (p=0.001), Hannum (p<0.001), and PhenoAge (p<0.001); with an increase in biological age of 4.9 years (95% CI 2-7.7) by Horvath clock, 4.8 years (95% CI 2.2-7.5) by Hannum clock, and 7 years (95% CI 2.9-10.5) by PhenoAge, after adjusting for chronologic age, sex, smoking status, and cancer history. CH was also associated with increased DunedinPACE of aging by 0.13 biological year per chronologic year (bioyear/chronoyear) (95% CI 0.06-0.2, p<0.001), and shortened telomere lengths by 0.24kb (95% CI 0.11-0.35, p<0.001) after adjusting for chronologic age and excluding patients with telomere biology disorders. CH patients were then further classified into ARCH-63 (59%) and CRCH-43 (41%). While the median chronological age (69 vs 67 years) was similar between the 2 groups, CRCH patients had lower Hgb [10.9g/dL vs. 13g/dL (p=0.0001)] , lower platelet [146x109/L vs. 192x109/L (p=0.0132)] and lower WBC [4.4x109/L vs. 5.2x109/L (p=0.0345)]. The median BMI, comorbidities, and tobacco use history were similar between the 2 groups. The 3 most common ARCH mutations were TET2 (20%), DNMT3A (19%), and ASXL1 (14%), while, PPM1D (27%), TET2 (18%), and DNMT3A (14%) were frequent in CRCH. There was no significant difference found in median EpiAge via Horvath (68 vs 67.5years p=0.2826), Hannum (65 vs 66 years p=0.9333), or PhenoAge (55 vs 56 years p=0.618) between ARCH and CRCH. However, DunedinPACE of aging was higher in CRCH (1.27 vs 1.18 bioyear/chronoyear p=0.0148) and median telomere length was shorter in CRCH (6.35 vs 6.49kb p=0.0247), in comparison to ARCH. Multivariable regression analysis was completed for both ARCH and CRCH compared to non-CH, adjusting for chronologic age, smoking status, and male sex. While both ARCH and CRCH demonstrated EpiAgeAccel via Horvath(5.8years p=0.0001, 3.4 years p=0.051), Hannum(5years p=0.001, 4.5 years p=0.007), and PhenoAge (6.7years p=0.002, 7.6 years p=0.002) clocks, CRCH more than doubled DunedinPACE (0.2 bioyear/chronoyear, p<0.001) of aging compared to ARCH (0.08 bioyear/chronoyear, p=0.026). CRCH also demonstrated double the decrease in telomere length (0.34kb, p<0.001) compared to ARCH (0.17kb, p=0.015). Conclusion: Our prospective CH cohort study validates the association between CH and EpiAgeAccel using several different methylation clocks and for the first time, highlights increased pace of aging and shorter telomere lengths in CRCH versus ARCH. Correlations with cardiovascular disease and mortality outcomes are ongoing.

e23199 Background: Psychological distress and diminished health related quality of life (HRQOL) is associated with multiple myeloma (MM). Complementary and alternative medicine (CAM) is an adjunct to traditional medicine. T-cells are essential to the adaptive immune system. The complementarity determining region (CDR3) of the T cell receptor formed by combination of the V,D,J regions is highly variable and critical for antigen specificity. PCR is used to analyze the TCR-CDR3 to monitor T-cell clonotypes as a measure of antigen specific T cell responses and thereby cancer immunity. Reiki is a Japanese energy healing technique involving noninvasive light touch to modulate energy fields. The primary aim was feasibility and acceptability of Reiki within a structured medical paradigm. Secondary aims were changes in HRQOL and T-cell receptor repertoires with Reiki. Methods: Adult MM patients (pts) on a stable regimen were eligible. Pts randomized in a 1:1:1 to weekly True Reiki (Treiki), Simulated Reiki (Sreiki) or no intervention (NI). The PROMIS-29® assessed HRQOL. A “Was it Worth it” (WIWI) questionnaire was administered post intervention to assess the experience. TCR-CDR3 sequencing was performed on PBMCs at enrollment and end of therapy to determine immune repertoire changes. Samples underwent library preparation and next generation sequencing for TCR repertoire analysis by iRepertoire® Results: Thirty pts were randomized into 3 arms between 12/2020 to 4/2022. Median age 62.5 (range 27-76), 18/30 (60%) female, median time to diagnosis was 48 mo (Range 7-221), 50% had high risk FISH,(90%) had prior ASCT and received median 2 (range 1-13) lines of therapy prior to enrollment. No pts relapsed or progressed during study period. Using WIWI 100% of pts in Treiki and 75% in Sreiki arms found the therapy useful and would do it again; 100% Treiki pts and 87.5% in the Sreiki arm would recommend the study to others. No statistical difference between groups in any HRQOL domains. Among TCR repertoires, no statistically significant difference was observed between groups with respect to Richness, Shannon entropy, Clonality, Diversity 50 (D50) and Inverse Simpson indexes. Each pt showed substantial variability in certain TCR-CDR3 clones and clonal diversity. Conclusions: We successfully demonstrated acceptability and feasibility of Reiki among MM pts, although no impact on HRQOL was shown. Reiki intervention is an easy non-invasive additional option for pts’ to their conventional therapies. The TCR-CDR3 repertoires showed no difference with the reiki intervention but had significant variability. Further research comparing TCR repertoires between MM and age/sex matched healthy controls is ongoing. T-cell directed therapies are evolving in MM, we plan to analyze if changing diversity of TCR-CDR3 repertoires could define how best to sequence these treatments. Clinical trial information: NCT04783038 .

AbstractSeveral tools exist for assessing frailty in multiple myeloma (MM), but most are based on clinical trial datasets. In this study, we evaluate the prognostic impact of frailty based on the cumulative deficit frailty index (FI), and the prognostic impact of relationship and socioeconomic status (SES) in newly diagnosed MM patients. This is a retrospective study including 515 patients with MM seen at Mayo Clinic (Rochester, MN) at diagnosis between 2005–2018. The FI was calculated using patient-reported data on activities of daily living and comorbidity data. Items were scored as 0/0.5/1, with 1 indicating deficit. The FI was calculated by adding individual scores and dividing that by the number of non-missing items. Frailty was defined as FI ≥ 0.15; 61% were non-frail and 39% were frail. Frailty and non-married/relationship status were associated with higher disease stage, decreased likelihood of early transplantation, and independently associated with decreased survival. SES was not independently associated with survival. Frail patients reported worse scores for fatigue, pain, and quality of life. In conclusion, a cumulative deficit FI was associated with higher symptom burden and decreased survival in a real-world cohort of newly diagnosed MM patients. Social support has prognostic value and should be assessed in clinical practice.

Background: Frailty is a state of decreased reserve affecting multiple organ systems, thereby increasing vulnerability to stressors. Several tools have been proposed to measure frailty in multiple myeloma (MM), such as the IMWG frailty index, and the Revised Myeloma Comorbidity Index. However, these are based on clinical trial datasets, and include chronological age as one of the indicators of frailty. A frailty index based on the principle of accumulation of health deficits (Rockwood doi:10.1093/gerona/62.7.738), provides a measure of physiologic age. This index can be readily calculated using various combinations of health deficits and has been shown to predict outcomes in several populations. Social support has been associated with improved outcomes in various populations, however its impact on survival in patients with MM has not been well studied. The objective of this study was to assess the impact of frailty measured by a deficit accumulation frailty index and the impact of relationship status on outcomes in patients with newly diagnosed MM. Methods: This is a retrospective study including patients diagnosed with MM between January 6 th, 2005, and September 19 th, 2018. We calculated a deficit accumulation frailty index using previously published methods (McNallan et. al doi.org/10.1016/j.ahj.2013.07.008, Searle et al. doi.org/10.1186/1471-2318-8-24). We used the electronic medical record system at Mayo Clinic in Rochester, MN, to extract data on activities of daily living (14 items) provided by patient-completed questionnaires, and data on comorbidities as recorded by treating providers (16 items), within 6 months prior to diagnosis. Each item was scored as 0, 0.5, or 1, with 1 indicating the presence of deficit (Table 1). The frailty index was defined as the sum of individual scores divided that by the total number of non-missing items. Patients who had N >2 missing items were excluded. Patients with a frailty index ≥0.15 were considered frail. We also extracted data on self-reported relationship status within 6 months prior to diagnosis. Variables were compared between groups using Fisher's exact test and Wilcoxon signed-rank test for categorical and continuous variables, respectively. Univariate and multivariate analysis were performed using Cox proportional hazard models. Overall survival (OS) was estimated using the Kaplan-Meier method and compared between groups using the Log-rank test. P values <0.05 were considered statistically significant. Results: We included 578 patients. Median age was 67 (interquartile range: 59-74) years, and 40% were female. Among all patients, 226 (39%) were frail. Compared to fit patients, patients who were frail were older (median age: 70 vs. 65 years, P<0.001) and more likely to have International Staging System (ISS) III (vs. I/II) disease (47% vs. 27%, P<0.001) and ECOG performance status ≥2 (vs. 0 or 1) (45% vs. 17%, P<0.001). There was no difference in R-ISS III disease or presence of high-risk cytogenetic abnormalities between fit and frail patients. Median OS was 7.6 and 4.1 years in fit and frail patient, respectively (P<0.001). Physician assessed ECOG performance status was available for 268 patients. Among patients with ECOG performance status 0 or 1 (193 patients), 136 (70%) were fit, and 57 (30%) were frail. The OS was 8.3 and 4.6 years in the 2 groups, respectively (P=0.006) (table 2). On multivariate analysis including age, frailty status, and R-ISS stage (III vs. I/II), all were associated with OS with frailty's hazard ratio (HR): 1.4 (95%CI [1.1-1.8]), P=0.004; the HR was 1.4 (95%CI [1.1-1.9]), P=0.02, when analysis was restricted to patients ≥60 years. Among all patients, those who were married or in a committed relationship (474 patients) had improved survival (median OS: 7.0 vs. 3.7 years, respectively, P<0.001) compared to patients who were divorced/separated, widowed, or single (96 patients). On multivariate analysis, which included age, R-ISS III, frailty status, and relationship status, all were associated with OS. The HR for relationship status was 1.4 (95%CI [1.0-1.8]), P=0.0465. Conclusion: Frailty, defined by the deficit accumulation frailty index, is independently associated with decreased survival in patients with newly diagnosed MM. Being married or in a committed relationship is associated with improved survival independent of age, disease stage, and frailty.

Abstract Background: Modern clinical practice places time restrictions on the amount of information and processing that can be done in a typical office visit. Meeting patient needs within the healthcare system is challenging, contributing to patient (pt) and clinician dissatisfaction and potential suboptimal outcomes. We developed an electronic point-of-care case management system for cancer patients. The system's goal is to obtain patient-reported quality of life (PROQOL) outcomes and provide clinicians with information regarding QOL issues to address. We tested this system in a prospective randomized study against usual care (UC). The primary objective was to determine if PROQOL system would improve mean QOL over time. Methods: Eligible pts were adults with multiple myeloma (MM) or light amyloidosis (AL), seen at Mayo Clinic. Pts were randomized 2:1 to PROQOL system or usual care. PROQOL system was offered prior to every visit. Pts select from various categories about their single biggest concern, and receive a printed list of actionable resources based on selected concern. Clinicians also receive the PROQOL results to review with pts. Providers and pts randomized to PROQOL completed a "was it worth it survey" (WIWI). An 8 item Linear Analogue Self-Assessment was used to assess QOL, the primary endpoint. Secondary endpoints included distribution of choices of most important concern and their relationship to demographic and clinical variables. The study was powered to detect a 0.5 standard deviation difference in QOL between groups. Results: There were 233 patients enrolled (171 MM, 62 AL) in this study between July 1, 2016 and April 17, 2018. Median age was 65 years (range 31-87), and 59% were male. Median time from diagnosis to study consent was 37 months (PROQOL 36.3 versus UC 39.6). Median follow up for all patients was 15 months (range: 0.7-22 months), and median follow up for PROQOL pts was 16 months and 14 months for UC pts (p=0.027). There were 25 pts that have died since enrollment and 208 alive. No statistical significant difference in QOL between PROQOL and UC (median QOL 7 vs 7, p=0.75). The most prevalent main concerns selected were Physical Health (37%) and Cancer and Diagnosis (27%), the pattern remained even when pts were given the option to pick a second major concern 26% and 20% respectively. No other concern was listed more than 8% at baseline. All category selections and percentages are shown in the Figure. The average time to complete the instrument was 6 minutes. Pts found the PROQOL system was worth their time (75%), they would choose it again (87%) and recommend it to others (82%). Eighty-two percent of clinicians did not think the PROQOL tool negatively impacted their practice and 75% believed they may see improvement in pt wellbeing. Discussion: The PROQOL system was well received by pts and clinicians but did not improve QOL relative to UC in this study. The PROQOL demonstrates that majority of patients strongly desire more information regarding ongoing therapy, clinical trials, prognosis and toxicity. Further investigation is needed to understand whether communication is limited by time or mechanism of information delivery, such as verbal or written, or whether it should be undertaken in a separate allied health care visit. Figure. Figure. Disclosures Gertz: spectrum: Consultancy, Honoraria; Abbvie: Consultancy; Research to Practice: Consultancy; annexon: Consultancy; Medscape: Consultancy; Physicians Education Resource: Consultancy; Amgen: Consultancy; janssen: Consultancy; Alnylam: Honoraria; Teva: Consultancy; Apellis: Consultancy; Ionis: Honoraria; celgene: Consultancy; Prothena: Honoraria. Kumar:Novartis: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding. Dingli:Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.; Millennium Takeda: Research Funding; Millennium Takeda: Research Funding; Alexion Pharmaceuticals, Inc.: Other: Participates in the International PNH Registry (for Mayo Clinic, Rochester) for Alexion Pharmaceuticals, Inc.. Lacy:Celgene: Research Funding. Dispenzieri:Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding.