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e18029 Background: Patients with recurrent or metastatic nasopharyngeal cancer (R/M NPC) who failed platinum-based chemotherapy have poor prognoses. We report the clinical activity and safety of bintrafusp alfa, a first-in-class bifunctional fusion protein composed of the extracellular domain of the transforming growth factor (TGF)-βRII receptor fused to a human IgG1 antibody blocking programmed death-ligand 1 (PD-L1), in patients with heavily pretreated R/M NPC. Tumor and plasma-based biomarkers were investigated in an exploratory analysis. Methods: Eligible patients had histologically confirmed NPC that had recurred at distant sites and were not amenable to curative treatment. All patients received at least one prior line of platinum-based chemotherapy for recurrent disease. Patients were treated with bintrafusp alfa (1200mg every 2 weeks) until disease progression. The sample size was estimated to assume a 40% objective response rate (ORR) to bintrafusp alfa compared with 20% for checkpoint inhibitors. Modified Simon two-stage optimal design was used (power, 80%; a = 0.05; P0 = 0.20; P1 = 0.40; n1 = 18; n = 33 with an additional five patients to allow for ineligibility or other reasons). The primary endpoint was ORR and secondary endpoints included survival and toxicity. Expression of PD-L1 in archived tumors, plasma clearance of Epstein-Barr virus (EBV) DNA, plasma clearance of TGF-β, and exosomal PD-L1 were assessed for a potential correlation with ORR. (NCT 04396886). Results: Out of 43 patients screened, 38 patients were enrolled. After a median follow-up of 14.9 months (range: 1.6-23.3 months), the confirmed ORR was 23.7% (95% CI: 12.4-38.8%) (complete response, n = 1; partial response, n = 8). The median treatment duration was 1.8 months (range: 0.5-14.3 months). 8 patients (21.1%) and 2 patients (5.3%) received bintrafusp alfa for > 6 months and > 12 months respectively. The 1-year overall survival (OS) rate was 57.5% (95% CI, 40.2% to 71.5%) and 1-year progression-free survival rate was 23% (95% CI, 10.1% to 39.4%). ORR was higher in patients with a decreasing trend in EBV-DNA at week 4 (40% vs. 6.3%, p = 0.02), whereas high exosomal PD-L1 levels at week 4 were predictive of worse ORR (5.3% vs. 41.7%, p = 0.012). There were no associations between clinical outcome and tissue PD-L1 expression (p = 0.952) or plasma TGF-β clearance (p = 0.28). 16 patients (42.4%) experienced ≥ grade 3 treatment-related adverse events, most commonly anemia (n = 9, 23.7%) and secondary malignancies (n = 4, 10.5%). Conclusions: Bintrafusp alfa has promising activity in heavily pretreated R/M NPC and a favorable 1-year OS rate, though the observed activity was not as high as the study initially aimed. The biomarker results warrant validation in larger cohorts. Clinical trial information: NCT04396886.

7057 Background: Cancer patients (pts) are at high risk of unplanned hospital readmissions. Predicting which cancer patients are at higher risk of readmission would improve post-discharge follow-up/navigation, decrease cost, and improve pt outcomes. Methods: We conducted a retrospective cohort study of non-surgical cancer pts hospitalized at our center between 12/2014 to 7/2018. A machine learning algorithm was trained on 348 medical, sociodemographic and cancer-specific variables with a total of 1,801,944 data points. The cohort was randomly divided into training (80%) and validation (20%) subsets. Prediction performance was measured by area under the receiver operator characteristic curve (AUC). Results: A total of 5,178 hospitalizations were included, of which 45.1% were women, and 27.6% experienced an unplanned readmission within 30 days. The most frequently represented cancers were hematologic malignancies (30.5%), followed by GI (18.1%), lung (13.7%), and GU (10.9%). Significant variables that impacted the algorithm decision are ranked from the most to the least important, including: days from last admission; planned index chemotherapy admission; number of vascular access lines, drains, and airways in use; length of stay; cancer diagnosis; total ED visits in past 6 months; age; discharge lab values (sodium, albumin, alkaline phosphatase, bilirubin, platelets); number of prior admissions; and discharge disposition. The AUC for the validation subset was 0.80. To ease the translation of this model into the clinic, we developed a web application whereby users can supply the aforementioned variables to the model and receive a personalized prediction that highlights those variables most affecting a subject’s readmission risk status: www.Cancer-Readmission.com. Conclusions: A cancer-specific readmission risk model with high AUC for 30-days unplanned readmission has been developed. The model is embedded in a freely available web application that provides personalized, patient-specific predictions. Programs that integrate this model can identify cancer patients with a greater risk for unplanned hospital readmission, thus providing a personalized approach to prevent future unplanned readmissions.

3119 Background: Resistance pattern and biological mechanism of immune checkpoint inhibitor (ICI) has been poorly understood. Sine suggested resistance mechanisms would be either innate resistance caused by lack of immune recruitment or acquired immune evasion after durable response of ICI treatment, we hypothesized that resistance pattern of tumor microenvironment would be distinct according to duration of ICI response in non-small cell lung carcinoma (NSCLC). In the current study, we applied deep-learning-based classification of three immune phenotypes (3IP): inflamed, excluded, and desert, to objectively assess the immunologic status of tumor microenvironment. Methods: Deep-learning algorithm of H&E Whole-Slide Images (WSI), called Lunit-SCOPE, was trained with 1,824 H&E WSI of NSCLC from Samsung Medical Center (SMC). WSI was divided into patches and each patch (~10 high-power fields) was classified as inflamed, excluded and desert, based on both quantity and localization of immune cells. Among NSCLC patients treated with ICI in SMC, 87 paired treatment-naïve (Pre, patch N = 15,415) and post-progression (Post, patch N = 18,197) tumor tissues were analyzed for Lunit-SCOPE. Results: In 87-paired samples, proportions of excluded and desert phenotypes were increased in post-progression tumor tissues (excluded; Pre 26.8% versus Post 32.5%, desert; Pre 19.5% versus Post 25.3%). Focused on 29 patients classified as inflamed in treatment-naïve, proportion of immune phenotypes of post-progression were clearly different according to duration of response, divided by median progression-free survival (PFS) of 3.7 m. Patients with rapid progression without ICI response (PFS < 3.7 m) turned into desert type (46.2%), whereas durable responder (PFS ≥ 3.7 m) either still remained on inflamed phenotype (42.9%) or turned into excluded phenotype (21.4%). Patients who remained on inflamed phenotype had favorable overall survival after progression on ICI, compared to turned into desert type (median survival not reached versus 6.6 m, P= 0.0296). Conclusions: Resistance patterns of ICI are distinct according to duration of response in patients with inflamed phenotype. Rapid progressor turns off immune into desert phenotype whereas most durable responder keeps immune recruitment into tumor microenvironment, which needs tailored strategy to overcome ICI resistance.

6062 Background: Malignant Bowel Obstruction (MBO) is one of the most common and devastating complications in women with gynecological cancer (GC). There is currently no consensus guideline to improve patient (pt) care in this setting. MAMBO (NCT03260647) is an ongoing prospective study evaluating the clinical implementation of a novel management algorithm for multidisciplinary management of MBO in GC pts. We report preliminary patient outcomes. Methods: All GC pts at Princess Margaret Cancer Centre with a confirmed diagnosis of or are at risk of MBO are eligible for enrollment. Participants follow a low fiber diet titrated by severity of symptom and their monthly weight and albumin levels are recorded, along with standardized patient-reported outcome measures (PROMs) at different time points. For pts who develop MBO, inpatient and ambulatory management algorithms are applied using a multidisciplinary and interprofessional care model consisting of nurses, surgeons, oncologists, radiologists, nutritionists, total parenteral nutrition team, social work, and palliative care. Decisions regarding most optimal management strategies are made by this team with regular MAMBO rounds. A retrospective analysis of pts hospitalized with MBO between 2012 and 2017 was performed in order to have a historical comparison for outcome and survival analysis using Kaplan Meier methods. Results: Since August 2017, 70 pts have been enrolled in MAMBO. Most had high-grade serous ovarian carcinoma (75%), of whom 68% are platinum-resistant. So far, 36 (51%) developed MBO, 6 of whom had multiple sequential episodes. Mean number of days in hospital with MBO was 10 days (median 7, range 0-45), compared to 18 days (median 9, range 0-134) for historical control (p = 0.009). There was no significant loss in weight 6 months from MBO diagnosis but a significant reduction in albumin level by 2.75 g/L after 3 months (p = 0.005). PROMs suggest fatigue and general lack of wellbeing were the symptoms with highest distress. Most patients (78%) received chemotherapy following MBO and most received weekly paclitaxel (36%). Median time from first MBO to death was 219 days (95% CI: 101-not reached) for all-comers in MAMBO and 174 days (95% CI: 98-363) for MBO requiring hospitalization, compared to 108 days (95% CI: 79-160) for historical controls (p = 0.007 and p = 0.062, respectively). Conclusions: Patient care and outcomes from MBO seem to be improved in GC pts enrolled in MAMBO compared to historical controls. Clinical trial information: NCT03260647.

3120 Background: Discovery of predictive biomarker to enrich the responder of immune checkpoint inhibitor (ICI) in PD-L1-low ( < 50%) non-small cell lung cancer (NSCLC) is still challenging. Recent study showed that loss of heterozygosity (LOH) of HLA led to immune evasion. In the current study, we hypothesized that 3 immune phenotype (3IP): inflamed, excluded and desert would be reliably classified by deep-learning algorithm of H&E image, called Lunit-SCOPE, which would dictate the responder in PD-L1-low NSCLC patients and discover a unique resistance pathway in excluded phenotype. Methods: Lunit-SCOPE was trained with 1,824 H&E Whole-Slide Image (WSI) of NSCLC from Samsung Medical Center (SMC). WSI was divided into patches (~10 high-power fields) which was classified for 3IP, based on both quantity and localization of immune cells. The 3IP was trained and optimized by considering clinical outcome of 119 NSCLC patients with PD-(L)1 inhibitor (training cohort, patches = 25,897), and validated in 62 patients enrolled in LC-biomarker study (NCT03578185, validation cohort, patches = 8,929). Tumor Proportion Score (TPS) of PD-L1 22C3 immunohistochemistry was assessed by pathologists. Tumor Mutational Burden (TMB) was calculated as number of nonsynonymous alterations throughout whole-exome and HLA LOH was called by LOHHLA algorithm. Results: Interactive analysis to classify 3IP in training cohort showed that 8,726 (33.7%), 10,965 (42.3%), and 6,206 (24.0%) patches were classified as inflamed, excluded, and desert, respectively. In validation cohort, median progression-free survival (mPFS) of inflamed phenotype was 10.1 m, significantly prolonged compared to either excluded phenotype (3.0 m, P= 0.0053) or desert phenotype (1.4 m, P= 0.0011). Inflamed phenotype independently dictated favorable ICI outcome in PD-L1-low (TPS < 50%, mPFS of inflamed: 14.3 m vs excluded/desert: 1.4 m, P= 0.0233) as well as in PD-L1-high (TPS≥50%, 10.1 m vs 4.2 m, P= 0.0361), respectively. Excluded phenotype had higher TMB compared to inflamed phenotype had (median 177 vs 107), and HLA LOH was also enriched in excluded phenotype (31.0%) compared to inflamed (17.6%) and desert (16.7%) phenotypes. Conclusions: Lunit-SCOPE based 3IP classification can predict ICI outcome especially in PD-L1-low ( < 50%) patients. Excluded phenotype showed poor ICI outcome even with high TMB, partially explained by HLA LOH resulting in loss-of-target, as a novel resistance mechanism of ICI.

64 Background: In the era of increasing therapeutic options and complexity of disease, some cancer patients (pts) continue aggressive treatment even within days of death. Previous studies report 30-66% of pts do not receive hospice or palliative services in the last month of life and many are enrolled in hospice < 3 days before death. Quality measures are endorsed by ASCO, National Quality Forum, and Oncology Care Model to increase hospice utilization. It is shown that pts enrolled in hospice have increased survival time with reduced in-hospital resources. These benefits increase the longer pts are in hospice. We hypothesize early identification of high risk pts by a multidisciplinary group and formulation of a care plan will prompt early discussion for hospice eligibility, increasing referrals to hospice and the number of days spent in hospice. Methods: As reported (ASCO 2018, Abst 6547), an Interdisciplinary Care Team (ICT) was created with palliative medicine and oncology physicians, nurses, and social workers. Twice monthly pts with high utilization over a 60-day period were identified. Care plans (CP) were created using a team based approach with parallel input from outpatient teams. CP was communicated back to the primary team. Results: 112 pts were discussed over 24 months; 39 pts died with a solid tumor malignancy and this was our study cohort. 85% pts (33/39) were referred to or had a hospice discussion and 82% pts (27/33) enrolled. 6 pts declined and 6 acutely died. Of the 27 pts that entered hospice 78% (21/27) were enrolled > 3 days and 22 % (6/27) < 3 days. Average number days in hospice was 19.7 (median 11) for all who entered hospice. In the subgroup that were enrolled for > 3 days, average number days was 25 (median 21). 62% pts (17/27) entered hospice within 60 days of ICT meeting and CP. Conclusions: Early identification of high-utilizing cancer patients along with review by ICT may correlate with early recognition of hospice eligibility, enrollment, and therefore greater number days spent in hospice. This increases hospice utilization allowing patients and families to experience the full benefit of hospice-directed care. Further interventions should be explored in optimizing transitions of care.

65 Background: Current ASCO and IMWG guidelines recommend all patients (pts) on active myeloma therapy receive concurrent supportive care with a bone modifying agent (BMA) to decrease the risk of skeletal related events (SRE). Unfortunately, recent data shows only 51% of Medicare pts with myeloma received a BMA within 90 days of first chemotherapy. We implemented a quality improvement project to identify the average time to BMA initiation at all Cleveland Clinic affiliated sites, with the primary goal to improve time to start BMA in newly diagnosed multiple myeloma (NDMM) pts by 4 weeks at Cleveland Clinic Main Campus (MC). Methods: Barriers to start BMA were identified using quality improvement tools developed at ASCO Quality Training Program. The first PDSA cycle ran from Sept 2018–Jan 2019 with an emphasis on education. This included review of updated guidelines, literature review of risks and toxicities associated with BMAs, and strategies for choosing BMA based on pt factors. Baseline data on time to start BMA and data to evaluate impact of PDSA intervention was done by chart review. Results: 161 NDMM pts were evaluated from 2015–2018 at all sites. The average time difference between the start of myeloma therapy and the start date of BMA in NDMM pts was 10.5 weeks. Subset analysis based on whether pts were treated at MC vs affiliate site was 10.6 weeks vs 9.1 weeks, respectively. During the first PDSA cycle, 14 NDMM pts were treated at MC. 86% (12/14) pts were treated with BMA. The average time between cycle 1 day 1 of first line treatment and first dose BMA was 4.3 weeks (range 4-12 weeks). Conclusions: With increased physician education and awareness of internal baseline data, we achieved our initial goal and observed a significant improvement in time to start BMA from 10.5 weeks to 4.3 weeks. Obstacles regarding effective communication with patients on the benefit of BMAs as well as need for dental clearance were barriers identified early on. We plan to incorporate BMA guidelines in our institutional care path with the goal to decrease time to initiation at all affiliated practices. Further mechanisms to ensure reinforcement of BMA initiation in NDMM patients is warranted to maintain therapeutic benefit.

6528 Background: Current ASCO and IMWG guidelines recommend that all patients (pts) on active anti-myeloma therapy receive concurrent supportive care treatment with a bone modifying agent (BMA) to decrease the risk of skeletal related events (SRE). Unfortunately, recent data shows only 51% of Medicare pts with myeloma received a BMA within 90 days of first chemotherapy. We implemented a quality improvement project to identify the average time to BMA initiation at all Cleveland Clinic affiliated sites, with the primary goal to improve time to initiation of BMA in newly diagnosed multiple myeloma (NDMM) pts by 4 weeks at Cleveland Clinic Main Campus (MC). Methods: Barriers with BMA initiation were identified using quality improvement tools developed at the ASCO Quality Training Program. The first PDSA cycle was implemented between September 2018 – January 2019 with an emphasis on education. This included review of updated guidelines, literature review of risks and toxicities associated with BMAs, and strategies for choosing BMA based on pt factors. Baseline data on time to start BMA and data to evaluate impact of PDSA intervention was completed via chart review. Results: 161 NDMM pts were evaluated between 2015 to 2018 at all sites. The average time difference between the start of anti-myeloma therapy and the start date of a BMA in NDMM pts was 10.5 weeks. Subset analysis based on whether pts were treated at MC vs affiliate sites was 10.6 weeks vs 9.1 weeks, respectively. During the first PDSA cycle, 14 NDMM pts were treated at MC. 86% (12/14) pts were treated with a BMA. The average time between cycle 1 day 1 of first line treatment and first dose BMA was 4.3 weeks (range 4-12 weeks). Conclusions: With increased physician education and awareness of internal baseline data, we achieved our initial goal and observed a significant improvement in time to initiation of BMA from 10.5 weeks to 4.3 weeks. Obstacles regarding effective communication with patients on the benefit of BMAs as well as need for dental clearance were barriers identified early on. We plan to incorporate BMA guidelines in our institutional care path with the goal to decrease time to initiation at all affiliated practices. Further mechanisms to ensure reinforcement of BMA initiation in NDMM patients is warranted to maintain therapeutic benefit.

9094 Background: In the era of immunotherapy, immune checkpoint inhibitor (ICI) has changed the treatment paradigm in metastatic non-small cell lung cancer (NSCLC). Along with clinical trials, there is an ongoing investigation to discover the predictive biomarker of ICI which so far has unsatisfactory reliability. As an effort to enhance the predictive value of ICI treatment, we applied deep learning and developed artificial intelligent (AI) score (range from 0 to 1) to analyze the specific context of immune-tumor microenvironment (TME) extracted by scanned images from H&E slides. Methods: As a ground work, deep learning-based H&E image analyzer, Lunit SCOPE, has been trained with H&E images (n = 1824) from ICI naive NSCLC samples. For the calculation of AI score, training was conducted using responder/non-responder labeled ICI treated samples from the exploratory cohort. The ICI responder was defined as the patient with a best overall response of partial or complete response and stable disease for more than 6 months. The positivity of PD-L1 immunohistochemistry (IHC) was assessed manually by pathologists. Results: The exploratory cohort is composed of NSCLC patients treated with ICI (n = 189) in Samsung Medical Center, and response to ICI was observed in 72 (38.1%) patients. Median follow-up duration was 6.8 months (6.6~8.2). Samples with PD-L1 IHC positive, defined by ≥ 1%, was observed in 138 (73.0%) patients. AI score was significant higher in the responder group (median: 0.391 vs 0.205, P = 6.14e-5), and the patients with AI score above the cut-off (0.337) showed a better response to ICI (odds ratio [OR] 3.47 P = 7.34e-5) which is higher than patients with PD-L1 ≥ 1% (OR 1.92, P = 0.069). High AI score group (n = 83) showed significantly favorable PFS compared to low AI score group (n = 106, median PFS: 5.1m vs 1.9m, hazard ratio [HR] 0.51, P = 9.6e-5) and this outcome was independent with PD-L1 status (P = 6.0e-5). In subgroup analysis, PFS of PD-L1 high / AI score high group (n = 63) had longer median PFS (6.7m) compared to both PD-L1 high / AI score low group (n = 70, 4.0m, P = 0.001) and PD-L1 low/AI score low group (n = 35, 1.9m, P = 4.0e-6). Tumor infiltrating lymphocyte (TIL) density of cancer epithelium was significantly correlated with AI score (Pearson’s r = 0.310, P = 1.43e-5), which suggests that AI score may partly reflect TME represented by TIL. Conclusions: The AI score by machine-learned information, extracted from H&E images without additional IHC stain, could predict responsiveness and PFS of ICI treatment independent of PD-L1 IHC positivity.

e21621 Background: Palliative and end of life care is essential for lung cancer pts. We examined palliative care delivery for lung cancer pts at 2 VA medical centers. Methods: In an IRB approved protocol, we reviewed medical records of matched pts diagnosed with liver or lung cancer between 2006 and 2012 who were seen by palliative care at 2 VA medical centers (S1, S2). Veterans were compared by 1) demographics, 2) palliative interventions, and 3) outcomes: ED visit within 1 month (mo) of death, ED visit within 6 mo of death, and ICU within 30 days of death. Analyses were performed with SAS Studio Version 3.5. Results: We analyzed 69 male pts (29 at S1and 40 at S2), with a mean age of 63 and 66 years respectively. Thirty (43%) pts were Caucasian and 39 (56.5%) African American. Cancer stages were I (1%), Stage II (3%), Stage III0 (29%), Stage IV (58%), and unknown (8%). Median Karnofsky performance status was 60%. By site, differences were seen in DNR/DNI (S1, S2) [ 21 (72%), 12 (30%) p = 0.0007). For palliative care interventions, significant differences were seen in treatments for pain (S1, S2) [17 (59%), 37 (93%) p = 0.0011], constipation (S1, S2) [10 (34%), 35 (88%) p < 0.0001], and dyspnea (S1, S2) [8 (28%), 29 (73%) p = 0.0003], but not for holding goals of care discussions. We observed differences between sites in evaluations by palliative social work (S1, S2) [15 (51%), 39 (98%) p < 0.0001], mental health (S1, S2) [21 (88%), 10 (25%) p < 0.0001], and chaplain visits (S1, S2) [ 19 (66%), 36 (90%) p = 0.0167]. Finally with outcome, there was a difference between consultations by other medical specialties (S1, S2) [ 19 (66%), 39 (98), p = 0.0005] but not for ED visits, admissions, procedures or ICU stays at the end of life . Conclusions: There are site specific differences between VA medical centers and may reflect local practice patterns, Additional sites should be studied.

e21642 Background: Little is known about palliative care for liver cancer. In this study, we examined palliative care for liver cancer and lung cancer patients. Methods: In an IRB approved protocol, we reviewed medical records of patients diagnosed with liver cancer who were seen by the palliative care service between 2006 and 2012 at 2 VA medical centers, and matched them to patients with lung cancer by year, KPS, and stage. Veterans were compared by symptom prevalence with the CMSAS and by palliative care interventions. Statistical analyses were performed with SAS Studio Version 3.5. Results: We analyzed a total of 138 male patients at 2 VA centers; 69 with lung cancer and 69 with liver cancer. The mean age in both groups was 65 years and 60% of patients self-identified as a minority. There was a high prevalence of symptoms in both groups. Symptom prevalence differed significantly between liver and lung cancer pts for weight loss and dyspnea at one site, and for feeling drowsy, constipation, at the other site. These differences disappeared in a pooled analysis. During palliative care evaluation, more lung cancer patients received treatment for constipation (n = 44 in lung, 29 in liver, p = 0.0107) and dyspnea treatment (n = 37 in lung, 22 in liver, p = 0.0167). More patients with lung cancer were evaluated by physical therapy (n = 41 in lung, n = 28 in liver, p = 0.0276) and psychiatry (n = 31 in lung, n = 20 in liver, p = 0.032). Conclusions: Differences between lung and liver cancer patients’ symptom prevalence and treatment by palliative care can vary by site. This reinforces the importance of local surveys of symptom prevalence . Further studies should be replicated in other sites.

164 Background: Palliative care interventions and their effect on EOL outcomes for liver cancer pts have not been described. We investigated the association between palliative care intervention and EOL care outcomes. Methods: We reviewed the charts of pts with hepatocellular carcinoma and who were seen by palliative care at 2 VA medical centers from 2006 to 2012. We investigated the association between EOL outcomes (number of ER visits, ICU visits, chemotherapy, place of death, number of hospitalizations during their last 30 days), and interventions such as early referral (within 30 days of diagnosis), defined goals of care, holding a family meeting, and symptom assessment and management. The protocol was reviewed by the IRB of both VAs and analyses were done with SAS Studio v3.4. Results: 82 charts were reviewed (30 from EOVA and 52 from BVA). All Pts were men; 30 (39%) were white and 47(61%) African American. Interval from diagnosis to palliative care consult was 68 days (median). 72 (90%) pts were not aware of their diagnosis. 48 (65%) were not aware of prognosis. 65 (88%) had decision making capacity. 32 (39%) pts were DNR/DNI. 60 (86%) pts chose symptom management and 10 (14%) life prolongation. 47 pts (50%) had family meeting. In the last month, 35 pts (48%) had ER visit. 9 pts (12%) had ICU visits, 4 pts (5%) had chemotherapy and 48 pts (71%) had at least 1 hospitalization. 68 (85%) underwent symptom assessment. Most frequently treated symptoms were pain (78%), nausea (27%), constipation (46%), dyspnea (34%). Significant associations were found for early referral and ER admissions (OR = 3.2, p = 0.0178), family meeting and site of death (OR = 0.136 p = 0.0037), family meeting and number of hospitalizations (OR = 0.2652, p = 0.0169). Pts choosing symptom management and who had family meetings associated with hospice/home as place of death (chisq = 5.7368, p = 0.016). Knowledge of prognosis was not associated with site of death. Conclusions: In this population, many palliative care interventions were performed. Early referral, family meetings were significantly associated with site of death, ER admissions, and number of hospitalizations. These results should be replicated in larger studies.

e15644Background: Liver cancer is a leading cause of death. Lack of data exists on palliative care in this group, and care varies by location. We aim to determine if there are differences in pallia...

e14053Background: CNS disease is common in multiple neoplasias, but clinical trials tend to exclude this patient population due to the increase in morbidity/mortality associated with CNS metastasis...

e21514Background: Palliative care interventions and their effect on EOL outcomes for liver cancer pts have not been described. We investigated the association between palliative care intervention a...

2529Background: Early phase clinical trials (EPCT) evaluate the potential benefits of new regimens and help guide the care of oncology patients (pts).The inclusion/exclusion criteria in EPCT are us...

e14691 Background: Advances in cancer screening and treatment in the past 20 years have improved cancer specific survival rates, raising questions regarding second primary malignancies and the path...

9512 Background: In 2012, 1.5-1.6 million patients received hospice care in United States. The majority received care at home. Unfortunately caregiving has been reported as an independent risk fact...