The NCCN Guidelines for Management of Immunotherapy-Related Toxicities provide interdisciplinary guidance on the management of immune-related adverse events (irAEs) resulting from cancer immunotherapy. These NCCN Guidelines Insights describe symptoms that may be caused by an irAE and should trigger further investigation, and summarize the NCCN Management of Immunotherapy-Related Toxicities Panel discussions for the 2020 update to the guidelines regarding immune checkpoint inhibitor–related diarrhea/colitis and cardiovascular irAEs.

The aim of the NCCN Guidelines for Management of Immunotherapy-Related Toxicities is to provide guidance on the management of immune-related adverse events resulting from cancer immunotherapy. The NCCN Management of Immunotherapy-Related Toxicities Panel is an interdisciplinary group of representatives from NCCN Member Institutions, consisting of medical and hematologic oncologists with expertise across a wide range of disease sites, and experts from the areas of dermatology, gastroenterology, endocrinology, neurooncology, nephrology, cardio-oncology, ophthalmology, pulmonary medicine, and oncology nursing. The content featured in this issue is an excerpt of the recommendations for managing toxicities related to CAR T-cell therapies and a review of existing evidence. For the full version of the NCCN Guidelines, including recommendations for managing toxicities related to immune checkpoint inhibitors, visit NCCN.org.

PURPOSE Patients with cancer experience high rates of morbidity and unplanned health care utilization and may benefit from new models of care. We evaluated an adult oncology hospital at home program's rate of unplanned hospitalizations and health care costs and secondarily, emergency department (ED) use, length of hospital stays, and intensive care unit (ICU) admissions during the 30 days after enrollment. METHODS We conducted a prospective, nonrandomized, real-world cohort comparison of 367 hospitalized patients with cancer—169 patients consecutively admitted after hospital discharge to Huntsman at Home (HH), a hospital-at-home program, compared with 198 usual care patients concurrently identified at hospital discharge. All patients met clinical criteria for HH admission, but those in usual care lived outside the HH service area. Primary outcomes were the number of unplanned hospitalizations and costs during the 30 days after enrollment. Secondary outcomes included length of hospital stays, ICU admissions, and ED visits during the 30 days after enrollment. RESULTS Groups were comparable except that more women received HH care. In propensity-weighted analyses, the odds of unplanned hospitalizations was reduced in the HH group by 55% (odds ratio, 0.45, 95% CI, 0.29 to 0.70; P < .001) and health care costs were 47% lower (mean cost ratio, 0.53; 95% CI, 0.39 to 0.72; P < .001) over the 30-day period. Secondary outcomes also favored HH. Total hospital stay days were reduced by 1.1 days ( P = .004) and ED visits were reduced by 45% (odds ratio, 0.55; 95% CI, 0.33 to 0.92; P = .022). There was no evidence of a difference in ICU admissions ( P = .972). CONCLUSION This oncology hospital at home program shows initial promise as a model for oncology care that may lower unplanned health care utilization and health care costs.

BackgroundRandomized trials evaluating programmed cell death protein 1 (PD-1) inhibitors in metastatic melanoma either permitted treatment for 2 years (pembrolizumab) or more (nivolumab). The optimal duration of therapy is currently unknown due to limited data, and shorter therapies may be effective.MethodsData of patients with metastatic cutaneous melanoma treated with single-agent PD-1 inhibitors at Huntsman Cancer Institute from January 1, 2015, to December 31, 2018, was reviewed to identify a continuous series of patients who made the joint decision with their provider to electively discontinue therapy at 1 year (>6 months and <18 months) in the setting of ongoing treatment response or disease stability. Patients were excluded if they received PD-1 inhibitors with other systemic therapy, had prior exposure to PD-1 therapy, or discontinued treatment due to disease progression or immune-related adverse event. Best objective response (BOR) per RECIST V.1.1 at treatment discontinuation, progression-free survival (PFS), and retreatment characteristics was analyzed.ResultsOf 480 patients who received PD-1 inhibitors, 52 met the inclusion criteria. The median treatment duration from first to the last dose was 11.1 months (95% CI 10.5 to 11.4). BOR was complete response in 13 (25%), partial response in 28 (53.8%), and stable disease in 11 (21.2%) patients. After a median follow-up of 20.5 months (range 3–49.2) from treatment discontinuation, 39 (75%) patients remained without disease progression, while 13 (25%) had progression (median PFS 3.9 months; range 0.7–30.9). On multivariable analysis, younger age, history of brain metastasis, and higher lactate dehydrogenase at the time of anti-PD-1 discontinuation were associated with recurrence. Patients with recurrent melanoma were managed with localized treatment, anti-PD-1 therapies, and BRAF-MEK inhibitors. All patients except one were alive at data cutoff.ConclusionIn this large real-world, observational cohort study, the majority of patients with metastatic melanoma after 1 year of anti-PD-1 therapy remained without progression on long-term follow-up. The risk of disease progression even in patients with residual disease on imaging was low. After prospective validation, elective PD-1 discontinuation at 1 year may reduce financial and immunotherapy-related toxicity without sacrificing outcomes.

PURPOSE Patients with advanced solid tumors may receive intensive treatments near the end of life. This study aimed to create a machine learning (ML) model using limited features to predict 6-month mortality at treatment decision points (TDPs). METHODS We identified a cohort of adults with advanced solid tumors receiving care at a major cancer center from 2014 to 2020. We identified TDPs for new lines of therapy (LoTs) and confirmed mortality at 6 months after a TDP. Using extreme gradient boosting, ML models were developed, which used or derived features from a limited set of electronic health record data considering the literature, clinical relevance, variability, availability, and predictive importance using Shapley additive explanations scores. We predicted and observed 6-month mortality after a TDP and assessed a risk stratification strategy with different risk thresholds to support communication of chance of survival. RESULTS Four thousand one hundred ninety-two patients were included. Patients had 7,056 TDPs, for which the 6-month mortality increased from 17.9% to 46.7% after starting first to sixth LoT, respectively. On the basis of internal validation, models using both 111 (Full) or 45 (Limited-45) features accurately predicted 6-month mortality (area under the curve ≥ 0.80). Using a 0.3 risk threshold in the Limited-45 model, the observed 6-month survival was 34% (95% CI, 28 to 40) versus 81% (95% CI, 81 to 82) among those classified with low or higher chance of survival, respectively. The positive predictive value of the Limited-45 model was 0.66 (95% CI, 0.60 to 0.72). CONCLUSION We developed and validated a ML model using a limited set of 45 features readily derived from electronic health record data to predict 6-month prognosis in patients with advanced solid tumors. The model output may support shared decision making as patients consider the next LoT.

7000 Background: Unplanned hospitalizations and emergency department (ED) visits are common during cancer care. Providing acute hospital level care at home may add value by decreasing hospital and ED use. We conducted the first evaluation of an oncology Hospital-at-Home program, Huntsman at Home (H@H). Methods: The Huntsman Cancer Institute began H@H services in 2018 and accepts referral of cancer patients for acute-medical or post-surgical care at home. Patients are admitted who require continued acute level medical care after hospitalization or have emergent unstable symptoms related to treatment or disease progression that would otherwise require ED evaluation or hospitalization. Prospectively, patients referred to H@H from 8/2018 through 10/2019 were compared to a usual care comparison group (UC) drawn concurrently from patients living within the Salt Lake City metropolitan area who qualified for admission to H@H, but lived outside the service zip codes. Probability of H@H enrollment propensity scores were constructed via random forest from patient descriptors and health care utilization at admission. We used an intent-to-treat approach for analysis. Primary outcomes were hospitalizations, length of stay (LOS), ED visits and cumulative charges over 30 and 90 days post admission to either group. Comparisons were made by generalized linear models, stratified by tertiles of H@H vs. UC propensity score. Results: 367 patients, 169 H@H and 198 UC, were evaluated. The average age was 62 yrs, 85% were Caucasian, and 77% had stage IV cancer. Propensity score distributions were overlapping, demonstrating group comparability. A variety of cancers were represented; the most common being colon, gynecologic, prostate and lung cancers. Compared to UC, H@H patients were more likely to be female (61% vs 43%) and during the month prior to admission, showed a trend towards longer LOS if hospitalized (6.7 vs 5.5 days). During the first 30 days after admission, propensity stratified comparisons showed H@H patients with lower hospital LOS (mean reduction 1.19 days, p=0.022), 56% lower odds of unplanned hospitalizations (OR 0.44, p=0.001), 45% lower odds of ED visits (OR 0.55, p=0.037) and 50% lower cumulative charges (mean ratio 0.50, p<0.001) compared to UC. Results over 90 days were similarly robust. Conclusions: In the first reported trial of an adult oncology Hospital at Home program, there was strong evidence for reduced hospitalizations, ED visits, and cost. Oncology Hospital at Home programs show promise for increased patient-centered care while simultaneously improving value.

303 Background: Aggressive medical interventions and associated high costs of care for cancer pts near the EOL are common. Addressing this issue at the local level requires an accurate, automated process to merge real-time clinical EHR data with cost data for performance reporting. Methods: This was a single-center, observational cohort study of decedents treated with anticancer therapy (antiCT) in the last 6 months of life from January 2016 to October 2017. Pts were stratified by antiCT use in the last 30 days of life. The primary outcome measure was total cost of care. Secondary outcome measures (hospitalizations, ER and ICU utilization, antiCT use, and hospice referral) were obtained through Flatiron Health EHR-based automated data processing. Cost data were merged from the Value-Driven Outcomes analytics framework. Results: 650 pts were included. 228 (35.1%) received antiCT in the 30 days before death. Non-drug costs for pts who received antiCT in the last 30 days of life were higher than those who did not (p < 0.01, median 38X higher). A higher proportion of pts who received antiCT in the last 30 days of life had ≥1 ER visit (29.4% vs 9.5%, p < 0.01) and hospital admission (58.8% vs 27.3%, p < 0.01) during the last 30 days. In addition, more of these pts received ICU care (35.5% vs 11.4%, p < 0.01). AntiCT in the last 30 days was associated with shorter median time from first hospice referral to death (1.4 vs 4.7 weeks; IQR 0.7-2.0 vs 3.14-7.7 weeks, p < 0.01). Distribution of antiCT types administered to pts in the last 30 days versus those given antiCT > 30 days from the EOL was significantly different, with the most substantial difference seen in the proportion of pts receiving immunotherapy (20.2% vs 12.6%, p = 0.04). Conclusions: Real-time assessment of EOL outcomes shows antiCT in the last 30 days of life is associated with aggressive medical interventions and increased total cost of care. Future research should identify pts who are unlikely to benefit from aggressive care, and whether performance reporting to oncologists will reduce futile interventions near the EOL.

Urinary elimination of parent belinostat was minimal, although a combined 36.7 % of belinostat metabolites were excreted in urine. Since these metabolites are primarily inactive, belinostat may not require dosage adjustment in renal dysfunction.