Background:Neoadjuvant chemotherapy (NACT) is increasingly being used in the management of locally advanced biliary tract cancer (BTC). The evidence suggests a contributing role of tumor infiltrating immune cells in the prognosis and response. We set out to characterize immune modulation of tumor immune microenvironment in BTC following NACT. Case Description:Patients with BTC who underwent diagnostic biopsy, then NACT then resection between 2014-2018 were identified. Multiplexed immunohistochemical consecutive staining on single slide (MICSSS) analysis was performed with a series of immune markers to characterize T-cells, immune checkpoints etc. on pre- & post-NACT tumor tissue. Density was calculated for each marker. The final analysis included five patients. Median age was 48 (range, 41-56) years, with 4 female, 4 intrahepatic cholangiocarcinoma and 1 gallbladder. All patients received gemcitabine/cisplatin as NACT (median of 5 cycles). Median time from diagnosis to surgery was 4.3 (range, 1.4-7.8) months. All patients were mismatch repair proficient (pMMR). NACT on average produced a depletion of all immune markers. Given small sample size, each patient was considered their own control and changes in mean cell densities post-NACT were calculated. Patient #2 with a 40-fold increase in PD-L1 expression & 5-fold decrease in CD8:FOXP3 ratio after NACT notably had the shortest disease-free interval (DFI). Patient #3 with the longest DFI had the largest increase in CD8:FOXP3 by about 8-fold with a decrease in PD-L1. Conclusions:Preliminary results suggest NACT may differentially modulate various compartments of the immune tumor contexture despite overall cell depletion. Future studies should focus on strategies to expand immune modulation of tumor microenvironment, including immune-oncology agents to augment the effects of chemotherapy.

e13664 Background: The burden of prior authorizations (PAs) required for costly oncologic treatments is high. Upon PA denials, peer-to-peer requests are performed. These requests consume time and effort of physicians and advanced care providers (APPs) contributing to burnout and may have a negative impact on patient care. We aimed to assess the degree to which the PA process contributed to burnout among oncology clinicians prior to implementation of a new streamlined workflow aimed at improving clinician wellness. Methods: A baseline wellness survey was sent to all hematology-oncology faculty and APPs at the largest clinical site of our academic cancer center. Burn out, our primary outcome, was measured using the work-related Copenhagen Burnout Inventory. We included selected questions from the American Medical Association (AMA) surveys regarding physician perceived impact of PAs on clinical workload and care delivery. We asked physicians and APPs to rate the impact of PAs on burnout on a scale of 0-100. Analysis was conducted using descriptive statistics. This study was sponsored by our Office of Well-Being and Resilience. Results: The wellness survey was completed by 35 physicians and 21 APPs with a response rate of 53% in both. The median Copenhagen burnout score for physicians was 46.43 (35.71-60.71) with higher burn out scores among those identifying as females, in practice less than 15 years and with more than 60% time in direct patient care. The median burn out score for APPs was 64.28 (50-83.03). Similar to physicians, APPs with less clinical experience had higher burn out scores. When asked about the impact of PAs on burnout on a scale of 0-100, physicians at the main site reported a median of 13.5, while APPs reported a median of 53.5. Conclusions: Physicians and APPs report noteworthy baseline rates of burnout and dedicate a significant portion of the week to completing PAs. Importantly, APPs perceived the PAs to have a high impact on their burn out compared to physicians. We will use these data to determine the efficacy of a new process that utilizes a single EMR platform for finance and cancer registry teams to abstract and document all of the required elements to streamline the PA process for intravenous/injection cancer treatments with the goal to reduce the number of PAs and improve wellness. [Table: see text]

TPS759 Background: Neoadjuvant therapy is now a standard strategy for localized PDAC, and this preoperative window provides an excellent opportunity in which to test novel therapeutic approaches. Trials using IO in PDAC have largely been unsuccessful, and immune tolerance is implicated as a major mechanism of IO resistance. The gut and tumor microbiome have emerged as key modulators of response to both IO and chemotherapy. High tumor microbial diversity has been linked to longer survival in PDAC, and gut microbiota may have the ability to colonize pancreatic tumors. There is preclinical evidence that endogenous microbiota promotes the immunosuppressive tumor microenvironment characteristic of PDAC through stimulation of pro-tumor regulatory T cells and myeloid-derived suppressor cells at the expense of anti-tumor activated CD4+ and CD8+ T cells. Further, preclinical data show that ablation of the gut microbiota may induce T cell activation, improve immune surveillance, and increase sensitivity to IO. We hypothesize that ablative antibiotics (abx) will activate tumor infiltrating T cells and enhance IO activity in PDAC. Methods: This is a multi-center, single-arm, open-label pilot study of pre-operative chemotherapy followed by abx and pembrolizumab to evaluate overall immune response to abx + IO. Eligible patients will have histologically confirmed, resectable PDAC, without probiotic consumption or use of immunosuppressive agents. Patients will be enrolled at diagnosis after undergoing a baseline biopsy. They will then receive mFOLFIRINOX every 2 weeks for 5 cycles. After completion of chemotherapy, ciprofloxacin 500 mg PO BID and metronidazole 500 mg PO TID will be administered for 21 days, and pembrolizumab 200 mg IV x1 will be given 7 days after initiation of abx. Patients will then undergo surgical resection and adjuvant therapy at the investigators’ discretion. On-treatment biopsy will be obtained prior to cycle 5 of mFOLFIRINOX. Blood and stool will be collected at baseline, during mFOLFIRINOX therapy, before and after pembrolizumab administration, and postoperatively. The primary endpoint is the overall immune response, which will be measured as activation of one or more of the T cell markers HLA-DR, CD38, CD25, Ki67, and CD69, defined as an increase in expression level of at least 20% from the on-treatment specimen to the surgical specimen, before and after abx + IO. Key secondary endpoints will be the evaluation of adverse events, R0 resection rate, histologic regression score, objective response rate, and overall survival rate. Correlative studies will be carried out to evaluate immune and microbiome changes in the blood and tissue following abx and pembrolizumab. These findings will be correlated with clinical endpoints. The target study accrual is 25 patients. Clinical trial information: NCT05462496 .

573 Background: Prior authorizations (PAs) for systemic cancer treatments are a barrier to timely quality cancer care delivery. The high administrative burden to complete peer to peer’s (P2P) and appeals leads to care delays and revenue losses. At our tertiary academic cancer center, in partnership with our cancer registry and Epic teams, we shifted the clerical work required for PAs from clinicians to certified tumor registrars (CTRs) to increase authorization efficiency and decrease financial losses from denials. Methods: Clinicians place the treatment plan order which goes to the CTR work queue. We leveraged the Epic staging smart form to consolidate and auto-populate common elements needed for PA. The CTRs then complete and validate the form prior to the authorization specialists obtaining the PA. We compared the pre (1/2022-9/2022) and post-implementation (11/2022-5/2023) periods on our primary outcomes: average monthly number of PAs pending review (including P2P, appeals and authorizations requiring clarifying clinical documentation) and average time spent on PAs by the authorization specialists. We compared the same post implementation period to the same time the year before on the financial loss from denied PAs. Analyses were conducted using Welch's t-test. Results: There was an 11% reduction in the average monthly number of PAs pending review (350 vs 313; p=0.27) from pre to post implementation. There was an average improvement of 6 hours spent obtaining a final approval/per authorization (100 vs 106 hours; p=0.64) from pre to post implementation. In addition, there was a trend toward improvement in financial losses from PA denials from Dec 2022 to March 2023 and a cost savings of ~ 7 million dollars from the same time period pre-intervention; p=0.01 (Table). Conclusions: By leveraging the EHR and optimizing existing non-clinical staff workflows, we demonstrated a sustained decrease in the number of ambulatory PAs pending review, time spent on the PAs and significantly decreased financial losses from denials. Future work will include treatment and disease specific optimizations and evaluating the impact on clinician well-being.[Table: see text]

1531 Background: Prior authorizations (PAs) for systemic cancer treatments are increasingly becoming a barrier to timely quality cancer care delivery. There is an extraordinary administrative burden placed on clinicians to complete peer to peers (P2P) and appeals when authorization specialists are unable to identify appropriate clinical data in the EHRs. This leads to delays in care, staff burnout and loss of revenue. At our tertiary academic cancer center, in partnership with our cancer registry and Epic teams, we propose shifting the clerical work required for intravenous chemotherapy PAs from clinicians to certified tumor registrars (CTRs) to improve documentation needed for authorization specialists and decrease PA associated clinician burnout. Methods: Clinicians first place the treatment plan order which goes to the CTR work queue. We leveraged the Epic staging smart form to consolidate and auto-populate a list of common data elements needed for PA (performance status, cancer biomarkers, stage, line of treatment, and goals of treatment). The CTRs then complete and validate all elements necessary for authorization specialists to obtain the PA. Our primary outcomes include: average monthly number of intravenous chemotherapy authorizations pending review (including P2P, appeals and authorizations requiring other clinical interventions from the primary team) during a pre (9/1/2021-9/30/22) and post-implementation (11/14/22-1/30/23) period. Analyses were conducted using descriptive statistics. Results: The average monthly number of authorizations pending review was 332 pre-implementation and 227 post-implementation, which is a 32% reduction. To account for temporal changes, when comparing December 2021 to January 2022 vs December 2022 and January 2023, the average number of referrals pending review was 276 vs 227 which is an 18% reduction. First line treatment regimens accounted for most regimens requiring review; 28% and 27% pre-implementation and post-implementation, respectively. Non-chemotherapeutics like lanreotide and feraheme had the highest pending review status pre-and post-implementation respectively. Among chemo/immunotherapeutics, Nivolumab and Trastuzumab deruxtecan had the highest pending review status pre and post implementation, respectively. GI regimens had the highest number of regimens requiring review accounting for 17% vs 22% of all referrals pre and post implementation, respectively. Conclusions: By leveraging technology and increasing efficiencies of existing non-clinical staff workflows, we significantly decreased the number of ambulatory chemotherapy PAs that required further clinical review including P2P and appeals thereby improving efficient care delivery. Next steps include evaluation of sustained improvement, time to final authorization, revenue analysis and the impact on the well-being of clinicians.

e16221 Background: Biliary Tract Cancers (BTC) are aggressive malignancies. Treatment of early-stage disease involves surgical resection, but whether adjuvant therapy offers a clear survival benefit remains inconclusive. This study aims to explore the impact of demographic factors and adjuvant therapy on survival outcomes at a population level through the lens of the latest SEER (Surveillance, Epidemiology, and End Results) registries. Methods: Cases were filtered by the following criteria: histologic type (adenocarcinoma), anatomic location (gallbladder, intrahepatic, extrahepatic), stage (local and regional), microscopically confirmed diagnosis, and confirmed surgical resection. Cases with incomplete entries were excluded. A Cox proportional hazard model was used to analyze survival in the context of race, gender, income, and type of adjuvant therapy (none, chemotherapy (AC), chemo-radiotherapy (CRT), and radiation (RT)). Data analysis was performed in RStudio. Results: Between 2005 and 2019, 3565 patients were identified. Patients with regional BTC demonstrated improved median survival with AC (27 vs 23 months, HR 0.72, CI 0.63-0.82, p ≤.05) and CRT (28 vs 23 months, HR 0.63, CI 0.56-0.72, p ≤.05). Median survival in localized BTC was 52 months. Adjuvant therapy of any type did not impact survival for localized BTC. Analysis of race as a covariate in localized BTC showed that all minority groups had improved survival compared to non-Hispanic whites (Asian, HR 0.33; Black, HR 0.50; Hispanic, HR 0.38, p≤.05). In regional gallbladder BTC, this survival benefit was limited to Asians only (HR 0.52, p≤.05). Conclusions: Our data show that AC and CRT may improve survival in patients with regional BTC across all anatomic subtypes. In our cohort, adjuvant therapy had no impact on survival in localized BTC. Differences in outcomes may exist among racial groups for different subtypes of BTC. Future adjuvant clinical trials for BTC should consider stratifying for regional and localized disease. [Table: see text]

e16151 Background: NACT is increasingly being used in the management of locally advanced BTC. Emerging evidence suggests a potential key contributing role of tumor infiltrating immune cells in the prognosis & response to therapy. We set out to characterize immune modulation of tumor immune microenvironment composition in BTC following NACT. Methods: Patients (pts) with locally advanced BTC who underwent a diagnostic biopsy, then NACT followed by resection between 2014 & 2018 were identified & consented after IRB approval. MICSSS, a sample-sparing chromogenic consecutive multiplex tissue staining method, was performed with a series of immune markers (Table), to characterize T cell subsets, B cells, macrophages, mature dendritic cells (DCs), and immune checkpoints on pre & post NACT formalin-fixed paraffin-embedded tumor tissue sections. Density was calculated for each marker (+ve cells/mm2) following annotation of tissues by tumor, fibrosis, necrosis, stromal & tumor infiltrating lymphocyte-enriched areas. Results: Nine pts were enrolled. Final analysis included 5 pts with adequate tissue. Median age = 48 (41-56), with 4 female, 4 intrahepatic cholangiocarcinomas & 1 gallbladder. All pts received Gemcitabine/Cisplatin as NACT with a median of 5 (4-7) cycles. Median time from diagnosis to surgery was 4.3 (1.4-7.8) months & last cycle to surgery was 0.9 (0.6-1.5) month. All pts were MMR proficient, 1 Her2+ & 2 with FGFR2 amplification. NACT on average produced a depletion of all immune markers (Table). Given the small N, each pt was considered their own control & changes in mean cell densities post NACT were calculated. Pt2 with a 40-fold increase in PDL1 expression & 5-fold decrease in CD8:FOXP3 ratio notably had the shortest disease-free interval (DFI). Pt3 with the longest DFI had the largest increase in CD8:FOXP3 by about 8-fold combined with a decrease in PDL1. Conclusions: Preliminary results suggest NACT may modulate immune microenvironment despite overall immune cell depletion. Future studies should focus on strategies to expand immune modulation of the tumor microenvironment in BTC by NACT, including immune oncology agent priming prior to or after NACT.[Table: see text]

433 Background: We previously reported the implementation of a machine learning (ML) model for mortality prediction that was integrated into a CDSS encouraging clinicians to have a SIC with at-risk cancer patients. The clinical utility of a ML model can change after implementation due to fluctuations in the organization’s patient population and clinical practices. It is important to establish a workflow to monitor and continually reinforce ML-powered CDSS to ensure that it continues to benefit patients. We report a workgroup structure that incorporates data driven evaluation of ML model performance and feedback from CDSS end users to optimize the acceptability of the CDSS. Methods: The workflow was piloted in the gastrointestinal (GI) oncology clinic from 11/2021-5/2022. A workgroup including members of the implementation team and end-users of the CDSS met monthly to review 1) a dashboard that displays model performance, 2) an electronic health record (EHR) report that summarizes use of the CDSS, 3) feedback from end users regarding their opinion of the CDSS and any barriers to implementation. We evaluated the accuracy of model predictions among subgroups as defined by mortality and unplanned hospital admissions or ED visit rates. Fisher’s Exact Test was used to identify differences between categorical variables. Numeric values including incidence rate ratios (IRRs) adjusted for age, sex, race, and gender with 95% confidence intervals (CIs) were calculated using Poisson regression. Results: 119 patients were evaluated by the model and 50 (42%) were assessed as high-risk. In the high-risk group, the oncology team evaluated 39 (78%) patients for appropriateness of a SIC; SIC was completed with 5 (10%) patients. During workgroup meetings, physicians shared that some of the high-risk predictions were for patients undergoing curative intent therapy. 0 out of 24 patients who received curative treatment died and 5 out of 26 patients who receive palliative treatment died. The log-rank p-value of 0.03 indicates that the survival distribution differs significantly over time between two groups. The adjusted IRR for unplanned hospital visits (palliative vs curative) was 2.55 (1.3-5.0). Adjusted mean hospital visits per month were 0.34 (0.21-0.51) vs 0.13 (0.06-0.21). Conclusions: The workgroup format is a feasible method to continuously review acceptability of a ML-powered CDSS. It may evaluate critical feedback from end users in a holistic manner that can augment a data driven evaluation of the model performance. The data implies that patients undergoing curative therapy have a decreased risk for mortality and unplanned hospital admissions or ED visits. The CDSS may be optimized by excluding these patients; however, longer follow up of this sub-population is needed to confirm that they have no additional risk factors.

OBJECTIVES:Currently, there is no guidance for optimal adjuvant chemotherapy selection after pancreatectomy with a partial or poor response to neoadjuvant therapy. This study seeks to describe an institution's practice patterns of adjuvant chemotherapy selection after neoadjuvant therapy.METHODS:Patients at a single institution receiving neoadjuvant chemotherapy followed by pancreatectomy for pancreatic cancer were reviewed. Patients enrolled in trials or without follow-up were excluded. Types of chemotherapy, the College of American Pathologists pathologic tumor response, and medical oncology plans were recorded.RESULTS:Forty-one patients met inclusion criteria. Pathologic review of treatment effect demonstrated that 3 patients (7.3%) had complete pathologic response, 3 (7.3%) had near complete pathologic response, 16 (39%) had partial response, and 14 (34.1%) had poor/no response to neoadjuvant chemotherapy. Fourteen of the 30 patients with partial or poor response (46.7%) received an alternate adjuvant regimen. Pathologic response to neoadjuvant chemotherapy specifically guided therapy in 11 (30.5%) patients.CONCLUSIONS:Despite 73.1% of patients with partial or poor response to neoadjuvant chemotherapy, only 46.7% received a different adjuvant regimen. Medical oncologists infrequently considered treatment effect when choosing adjuvant therapy. Pathologic response to neoadjuvant chemotherapy should be considered when selecting adjuvant chemotherapy.

Abstract Background: ERY974, a bispecific T cell-redirecting antibody, redirects T cells to tumor cells by engaging the CD3 antigen on T cells and the glypican 3 (GPC3) antigen selectively expressed on tumors. ERY974 demonstrates T cell-dependent cellular cytotoxicity in vitro and transient cytokine elevations in preclinical toxicology studies (Ishiguro et al. 2017). The primary objective of this dose escalation (DE) study was to determine ERY974's maximum tolerated dose in patients with locally advanced or metastatic solid tumors expressing GPC3. Methods: The study included adult patients with advanced or metastatic solid tumors not amenable to standard therapy, histologically confirmed, with measurable disease and a life expectancy ≥ 3 months, including patients with ≤ 1cm and ≤ 1 brain metastasis. Patients with interstitial lung disease, or acute/active chronic infection were excluded. ERY974 was administered IV and dosed weekly. DE was initiated with an accelerated titration design of single patient cohorts followed by three patient cohorts. To mitigate for the toxicity of cytokine release syndrome (CRS), steroid prophylaxis and a flexible study design was implemented which included a two-step intra-patient escalation (regimen A), and a three-step intra-patient escalation (regimen B). Results: 29 patients were enrolled in dose levels ranging from 0.003 μg/kg to 0.81 μg/kg. Treatment-related adverse events that occurred in greater than 20% of patients included CRS and pyrexia. Dose level 0.81 μg/kg (regimen A) was confirmed not tolerable due to DLTs of Grade 3 CRS and Grade 2 CRS in two out of three patients (assessed according to Lee, et al. 2014). The Grade 3 CRS was associated with Grade 3 transaminitis and a Grade 3 elevation of bilirubin. Both CRS events led to dose delay and dose reduction. Increases in IL-6, IL-8 and IL-10 were observed in patients with the CRS. The severity and frequency of CRS in regimen B were similar to those observed in regimen A at the same dose level. One partial response (per modified RECIST criteria) was observed in a patient with esophageal cancer treated with 0.54 μg/kg (regimen B) and having 40% of the tumor tissue staining positive for GPC3 via immunohistochemistry. Stable disease lasting 3 months or longer was observed in four patients. Conclusions: The observed responses and CRS side effects are markers of ERY974 biologic activity. At doses below 0.81 μg/kg (regimen A), ERY974 was generally well tolerated with a manageable toxicity profile, including ERY-induced CRS which was manageable with steroid administration and anti-IL6R therapy. Further research is required to determine if combined prophylactic anti-IL6R and steroid therapy is a more effective strategy for managing CRS. References: 1. Ishiguro, Takahiro, et al. Science translational medicine, 2017, 9.410: eaal4291. 2. Lee, Daniel W., et al. Blood, 2014, 124.2: 188-195. Citation Format: Howard Safran, Mihaela Druta, Michael Morse, Filipa Lynce, Sofya Pintova, Khaldoun Almhanna, Daniel Weiss, Athos Gianella-Borradori, Yoshitaka Ogita, Roland Morley, Mikiko Nakamura, Junnosuke Matsushima, Takahiro Ishiguro. Results of a phase 1 dose escalation study of ERY974, an anti-glypican 3 (GPC3)/CD3 bispecific antibody, in patients with advanced solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr CT111.

PURPOSE: To describe the length of encounter during visits where goals-of-care (GoC) discussions were expected to take place. METHODS: Oncologists from community, academic, municipal, and rural hospitals were randomly assigned to receive a coaching model of communication skills to facilitate GoC discussions with patients with newly diagnosed advanced solid-tumor cancer with a prognosis of < 2 years. Patients were surveyed after the first restaging visit regarding the quality of the GoC discussion on a scale of 0-10 (0 = worst; 10 = best), with ≥ 8 indicating a high-quality GoC discussion. Visits were audiotaped, and total encounter time was measured. RESULTS: The median face-to-face time oncologists spent during a GoC discussion was 15 minutes (range, 10-20 minutes). Among the different hospital types, there was no significant difference in encounter time. There was no difference in the length of the encounter whether a high-quality GoC discussion took place or not (15 v 14 minutes; P = .9). If there was imaging evidence of cancer progression, the median encounter time was 18 minutes compared with 13 minutes for no progression ( P = .03). In a multivariate model, oncologist productivity, patient age, and Medicare coverage affected duration of the encounter. CONCLUSION: Oncologists can complete high-quality GoC discussions in 15 minutes. These data refute the common misperception that discussing such matters with patients with advanced cancer requires significant time.

PURPOSE:To study factors that have an impact on the conduct of high-quality goals of care (GoC) discussions and productivity of oncologists among four different practice settings in patients with advanced cancer.METHODS:Solid-tumor oncologists from community, academic, municipal, and rural hospitals were randomly assigned to receive a coaching model of communication skills to help them facilitate a GoC discussion with newly diagnosed patients with advanced cancer who had a less-than-2-year prognosis. Patients were surveyed after the first restaging visit regarding the quality of the GoC discussion on a scale of 0 to 10 (0, worst; 10, best) with a score of 8 or better indicating a high-quality GoC discussion. Productivity was measured by work revenue value units (wRVUs) per hour for the day each oncologist saw the study patient after imaging.RESULTS:The four sites differed significantly in the socioeconomic patient populations they served and in the characteristics of the oncologists who cared for the patients. Overall median productivity across the four sites was 3.6 wRVU/hour, with the highest observed in the community hospital (4.3 wRVU/hour) and the lowest in the rural setting (2.9 wRVU/hour; P < .001). There was no significant difference in productivity observed when high-quality GOC discussion occurred versus when it did not (3.6 v 3.7 wRVU/hour; P = .86).CONCLUSION:Despite differences in patient populations and oncologists' characteristics between the four practice settings, the conduct of high-quality GoC discussions did not affect productivity.

PURPOSE:Patients with advanced cancer often have a poor understanding of cancer incurability, which correlates with more aggressive treatment near the end of life (EOL). We sought to determine whether training oncologists to elicit patient values for goals-of-care (GoC) discussions will increase and improve these discussions. We explored its impact on use of aggressive care at EOL.METHODS:We enrolled and used block randomization to assign 92% of solid tumor oncologists to 2-hour communication skills training and four coaching sessions. We surveyed 265 patient with newly diagnosed advanced cancer with < 2-year life expectancy at baseline and 6 months. We assessed prevalence and quality of GoC communication, change in communication skills, and use of aggressive care in the last month of life.RESULTS:Intervention (INT) oncologists' (n = 11) skill to elicit patient values increased (27%-55%), while usual care (UC) oncologists' (n = 11) skill did not (9%-0%; P = .01). Forty-eight percent (n = 74) INT v 51% (n = 56) UC patients reported a GoC discussion (P = .61). There was no difference in the prevalence or quality of GoC communication between groups (global odds ratio, 0.84; 95% CI, 0.57 to 1.23). Within 6 months, there was no difference in deaths (18 INT v 16 UC; P = .51), mean hospitalizations (0.47 INT v 0.42 UC; P = .63), intensive care unit admissions (5% INT v 9% UC; P = .65), or chemotherapy (26% INT v 16% UC; P = .39).CONCLUSION:Use of a coaching model focused on teaching oncologists to elicit patient values improved that skill but did not increase prevalence or quality of GoC discussions among patients with advanced cancer. There was no impact on high care utilization at EOL.

Epidemiologic and preclinical data suggest isoflavones have anticancer activity in colorectal malignancy prevention and treatment. This is the first clinical trial assessing safety and tolerability of Genistein in combination with chemotherapy in metastatic colorectal cancer. Patients who had histologically confirmed metastatic colorectal cancer and had not received previous treatment were eligible to enroll. Subjects were treated with FOLFOX or FOLFOX–Bevacizumab as per the investigator choice. Genistein was administered orally for 7 days every 2 weeks, beginning 4 days prior to chemotherapy and continuing through days 1–3 of infusional chemotherapy. Primary endpoint was safety and secondary endpoints included cycle 6 response rate, best overall response rate (BOR), and median progression-free survival (PFS). Thirteen patients received chemotherapy with Genistein in this trial. The most common adverse events related to Genistein alone were mild and included headaches, nausea, and hot flashes. One subject was observed to have grade 3 hypertension. No increase in chemotherapy-related adverse events was observed when Genistein was added. BOR and median PFS were 61.5% and 11.5 months, respectively. We observed that adding Genistein to FOLFOX or FOLFOX–Bevacizumab was safe and tolerable. Efficacy results are notable and warrant verification in larger clinical trials. The study was registered at ClinicalTrials.gov Identifier: NCT01985763.

•Describe important components of goals of care conversations•Recognize the importance of time on recall of goals of care conversations Goals of Care discussions (GoC) should include information about cancer treatment, prognosis and elicit patients’ value. To determine the impact of communication skills training, it is imperative to assess concordance of patient report and physician conduct of GoC. Evaluate whether patients perceptions of GoC are an accurate measure of oncologist performance. We randomized solid tumor oncologists and their cancer patients with <2 year prognosis to receive a communication skills and coaching model at 4 hospitals. We audio-recorded the 3 month post-imaging visit following 1st line chemotherapy and surveyed patients. GoC were considered to have occurred if the oncologist elicited values and discussed prognosis or treatment. We define patient perception of a GoC conversation as patient report that their oncologist talked about the likely outcome of cancer and clarified things most important to them. To assess the impact of recall, we assessed the number of days from recording to survey. We enrolled 22 oncologists and recorded 135 visits. On average, oncologists were 44 years old (32-66) and in practice 14.5 years (5-40). Patients’ mean age was 65 years (25-89); 46% female, 44% White, 9% Latino & 32% black. Overall, GoC were reported by 47% of patients compared with 15% assessed on audio recordings. Sixty-seven percent of patients were surveyed within one day of the recorded visit (0-49). For patients surveyed within 1 day, 54% reported having GoC compared with 33% for those with later survey completion (p=0.02). Kappa between perceived GoC and practice was 0.02. Overall rates of GoC are low. There is little agreement between patient perception and actual practice. Patients overestimate conduct of GoC and this association diminishes over time.

19 Background: Studies show minority patients have inadequate discussions about treatment, prognosis, and goals of care (GoC) which translate into substandard treatment, worse quality of life, and poorer survival than whites. However, there is a paucity of data on the quality of communication among minority patients with advanced cancer. We studied factors impacting the oncologists’ time spent during GoC discussion visits with their minority and non-minority patients. Methods: At community, academic, municipal, and rural hospitals, we recruited and randomized solid tumor oncologists and their newly diagnosed advanced cancer patients with <2-year prognosis to participate in a RCT, testing a coaching model of communication skills training. Patients were surveyed after post-imaging visits. These visits were audiotaped and median encounter time recorded. We define GoC discussions as patients report that their doctor talked about preferences for cancer treatment and clarified things most important to them given their illness. Comparisons were made using non-parametric tests. We used mix-effect models for risk adjustment. Results: For 22 randomized oncologists in the study,142 post-imaging encounters were audiotaped. Of these, 38% were non-Hispanic White, 32% non-Hispanic Black and 19% Hispanic. The median face to face time oncologists spent during a GoC encounter with an advanced cancer minority patient was 12 minutes compared to 17 minutes for non-minorities (p=0.002). Median encounter times varied between the four sites, ranging from 10 minutes to 18 minutes, p=0.009. For visits that took place after progression of disease, duration of visit was 18 minutes versus 13 minutes if there were no progression, p=0.007. After controlling for clustering of the patients within the hospitals and progression of disease, time spent with minority patients remained less than with non-minority patients (15 min vs. 18 min, p=0.02). Conclusions: Oncologists' time spent conducting GoC conversations with minority cancer patients is significantly less than with non-minority patients. Evaluating factors that contribute to this disparity is critically important to ensure minority patients receive high-quality cancer care. Clinical trial information: NCT02374255.

10107 Background: Oncologists report that time to conduct a goals of care (GoC) discussion is a barrier. We studied the relationship of GoC discussion and visit times at different type hospitals. Methods: At community, academic, municipal and rural hospitals, we recruited & randomized solid tumor oncologists & their newly diagnosed advanced cancer patients with < 2 year prognosis to participate in a RCT, testing a coaching model of communication skills training. Patients were surveyed after post-imaging visits. These visits were audiotaped and median encounter time recorded. We define GoC discussions as patient report that their doctor talked about preferences for cancer treatment and clarified things most important to them given their illness. Analyses were done with Kruskal-Wallis and Wilcoxon tests. Results: For 22 randomized oncologists in the study, 137 post-imaging encounters were audiotaped. The median face-face time oncologists spent during a GoC encounter with an advanced cancer patient was 15 minutes. Encounter times when GoC discussions were expected varied between the four sites, ranging from 9.5 minutes to 18 minutes, p = 0.05. The encounters where no GoC discussions occurred were longer, taking 16.5 minutes vs 13 minutes, p = 0.05. Visits that took place after progression of disease took longer, 18 minutes vs 13 minutes, p = 0.006. Conclusions: Visit times vary by hospital type and average 15 minutes. With disease progression, visit time is longer. Despite physician perceptions, GoC discussions do not lengthen visits. Clinical trial information: NCT02374255.Hospital (N) Encounter Time* Minutes (range) Encounter Time GoC Discussion Occurred (+) Minutes (range) Encounter Time No GoC Discussion Occurred (-) Minutes (range) p value Encounter Time Progression of Disease Minutes (range) Encounter Time No Progression of Disease Minutes (Range) p value Overall (137) 15 (4-40) 13 (4-40) 16.5 (4-40) 0.05 18 (12-22) 13 (10-19) 0.006 Community (10) 9.5 (4-38) 9.5 (4-38) NA - 18 (9-38) 9 (5-12) 0.14 Academic (98) 15 (4-40) 13 (4-40) 15 (4-33) 0.18 17 (12-20) 13 (10-20) 0.16 Municipal (15) 12 (6-40) 10 (6-35) 14.5 (7-40) 0.42 24 (19-28) 10 (7-17) 0.09 Rural (14) 18 (10-34) 18 (10-34) 18 (10-34) 0.75 22 (22-34) 17 (12-23) 0.12 *p = 0.05