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Background: The use of virtual care, defined as providing healthcare in ways other than in-person visits, dramatically increased during the COVID-19 pandemic to minimize infection risk and maintain care provision. However, the majority of studies evaluating the use of virtual care in oncology have focused on patients with solid tumor malignancies. Patients with hematologic malignancies represent a unique population related to their underlying malignancy and treatment, but the extent to which virtual care has been successfully integrated into clinical practice is unknown. As the demand for incorporating virtual care into routine clinical practice grows, understanding how to safely and effectively use virtual care in this specific patient population is essential. Methods: A scoping review was conducted to describe the use of virtual care in the management of patients with hematologic malignancies and to examine physician- and patient-reported outcomes based on its use. A comprehensive search strategy was used to identify articles on the use of virtual modalities (digital applications, phone visits, or video visits) to deliver routine clinical care to patients with hematologic malignancies published in English between January 2000 and April 2021 in the following databases: PubMed, Medline, EMBASE, CINAHL and Scopus. No restrictions were applied to the phase of care (surveillance, active treatment, and or survivorship). A combination of search terms to encompass hematologic malignancies and virtual care was used. Screening and data abstraction were performed by two independent reviewers (AS, AV) and conflicts were adjudicated by two additional reviewers (DR, AP). Data were extracted to assess the study design, population, setting, patient characteristics, virtual care methodology and study results. Results: A total of 350 abstracts were screened and 60 studies underwent full-text review. Of these, 15 studies met inclusion criteria (13 retrospective studies, 1 prospective pilot study, and 1 randomized controlled trial). The majority of papers were published from 2020 onward (Figure 1). Three studies found that app-based tools were effective in monitoring patient symptoms and triggering alerts which resulting in sooner follow-up if indicated. One app-based tool was also used in a resource limited country during the COVID-19 pandemic to allow for communication between hematologists and their patients. Four studies described the use of phone-based interventions for new consults and follow-up visits. Phone visits for used even in high-risk patients (for example, in 29 out of 50 patients with chronic graft-versus-host disease after allogeneic stem cell transplant). Five studies found that videoconferencing, with both physicians and oncology nurses, was highly rated by patients. Emerging themes included high levels of patient satisfaction across all domains of virtual care. Provider satisfaction scores were rated lower than patient scores, with concerns about technical issues leading to challenges with virtual care. Four studies found that virtual care allowed providers to promptly respond to patient concerns, specifically when patients were experiencing side-effects or had questions about their treatment. One study also suggested a decreased risk of death when telemedicine was used to monitor symptoms in patients with diffuse large B-cell lymphoma. There were no data addressing the impact of virtual care on costs or resource utilization. Conclusion: The use of virtual care in patients with hematologic malignancies appears to be feasible, even in high-risk populations. Patients were highly satisfied with virtual care, although providers had some challenges related to the specific technologies. More research is needed to evaluate the optimal method of integrating virtual care, informed by a wider range of patient-related outcomes, as well as the downstream consequences of this integration for patient, providers, and health systems. Figure 1 Figure 1. Disclosures Prica: Astra-Zeneca: Honoraria; Kite Gilead: Honoraria.

Outcomes for patients with hematologic malignancy infected with COVID-19 have not been aggregated. The objective of this study was to perform a systematic review and meta-analysis to estimate the risk of death and other important outcomes for these patients. We searched PubMed and EMBASE up to 20 August 2020 to identify reports of patients with hematologic malignancy and COVID-19. The primary outcome was a pooled mortality estimate, considering all patients and only hospitalized patients. Secondary outcomes included risk of intensive care unit admission and ventilation in hospitalized patients. Subgroup analyses included mortality stratified by age, treatment status, and malignancy subtype. Pooled prevalence, risk ratios (RRs), and 95% confidence intervals (CIs) were calculated using a random-effects model. Thirty-four adult and 5 pediatric studies (3377 patients) from Asia, Europe, and North America were included (14 of 34 adult studies included only hospitalized patients). Risk of death among adult patients was 34% (95% CI, 28-39; N = 3240) in this sample of predominantly hospitalized patients. Patients aged ≥60 years had a significantly higher risk of death than patients, Key Points • Adult patients with hematologic malignancy and COVID-19 found a 34% risk of death, whereas pediatric patients had a 4% risk of death. • Patients on systemic anticancer therapy had a similar risk of death to patients on no treatment (RR, 1.17; 95% CI, 0.83-1.64).

Background In many jurisdictions, cancer patients were prioritized for COVID-19 vaccination because of increased risk of infection and death. To understand sociodemographic disparities that affected timely receipt of COVID-19 vaccination among cancer patients, we undertook a population-based study in Ontario, Canada. Methods Patients older than 18 years and diagnosed with cancer January 2010 to September 2020 were identified using administrative data; vaccination administration was captured between approval (December 2020) up to February 2022. Factors associated with time to vaccination were evaluated using multivariable Cox proportional hazards regression. Results The cohort consisted of 356 535 patients, the majority of whom had solid tumor cancers (85.9%) and were not on active treatment (74.1%); 86.8% had received at least 2 doses. The rate of vaccination was 25% lower in recent (hazard ratio [HR] = 0.74, 95% confidence interval [CI] = 0.72 to 0.76) and nonrecent immigrants (HR = 0.80, 95% CI = 0.79 to 0.81). A greater proportion of unvaccinated patients were from neighborhoods with a high concentration of new immigrants or self-reported members of racialized groups (26.0% vs 21.3%, standardized difference = 0.111, P Conclusions Despite funding of vaccines and prioritization of high-risk populations, marginalized patients were less likely to be vaccinated. Differences are likely due to the interplay between systemic barriers to access and cultural or social influences affecting uptake.

The objective of this study was to perform a systematic review of the literature on vaccine responsiveness in patients who have received anti-CD20 therapy. PubMed and EMBASE were searched up to 4 January 2021 to identify studies of vaccine immunogenicity in patients treated with anti-CD20 therapy, including patients with hematologic malignancy or autoimmune disease. The primary outcomes were seroprotection (SP), seroconversion (SC), and/or seroresponse rates for each type of vaccine reported. As the pandemic influenza vaccine (2009 H1N1) has standardized definitions for SP and SC, and represented a novel primary antigen similar to the COVID-19 vaccine, meta-analysis was conducted for SC of studies of this vaccine. Pooled estimates, relative benefit ratios (RBs), and 95% confidence intervals (CIs) were calculated using a random-effects model. Thirty-eight studies (905 patients treated with anti-CD20 therapy) were included (19 studies of patients with hematologic malignancies). Patients on active (

Introduction: Previous studies have demonstrated that frailty is associated with mortality among patients with non-Hodgkin lymphoma including diffuse large B-cell lymphoma (DLBCL). However, no studies have examined frailty in an unselected population-based sample of patients with DLBCL, nor have data on health care utilization been considered as a potential mediator of this relationship. Objective: To determine whether frailty is associated with one-year survival in an unselected population of patients with DLBCL, and examine whether its impact is mediated by health care utilization during chemotherapy treatment. Methods: A retrospective cohort study was conducted using population-based health care data in Ontario, Canada. Patients >65 years diagnosed with DLBCL or transformed follicular lymphoma between January 2006 and December 2017 and receiving first-line chemo-immunotherapy were included. Frailty was defined by modifying a previously validated score developed for use with population-based data in Ontario, comprising 30 multidimensional variables (McIsaac, Ann Surg. 2019;270(1):102-108). Patients were categorized as "frail" (score >0.21) vs. "non-frail" (score ≤0.21). Covariates included age, number of comorbidities based on the Johns Hopkins Aggregated Diagnosis Groups (ADGs), and health care utilization during chemotherapy (defined as emergency department (ED) visit or inpatient hospitalization not resulting in death during treatment, and analysed as a time-varying covariate). Cox regression was performed to examine the association between frailty and one-year mortality (primary outcome). Secondary outcomes included health care utilization, chemo-immunotherapy exposure, and cause of death. Results: 5,527 patients were included in the study. 5,216 patients (94%) had de novo DLBCL, and 311 (6%) of patients had transformed follicular lymphoma. The median age was 75 years (IQR 70-80), and 48% (N=2672) were female (Table 1). 2,699 (49%) of patients were classified as frail (Table 2). Frail patients tended to be older (median age 76 (IQR 71-81) vs. 74 years (IQR 70-79)). The difference in mortality between frail and non-frail patients was most pronounced in the initial year following start of treatment (Figure 1a). Within 90 days of first-line rituximab, 14% (N=370) of frail vs. 7% (N=185) of non-frail patients had died (p The relationship between frailty and survival remained consistent when measured in quartiles (HR 1.6 (95% CI 1.3-1.9) for Q2, 2.0 (95% CI 1.7-2.4) for Q3, 2.7 (95% CI 2.3-3.2) for Q4, p Conclusion: Frailty is significantly associated with one-year mortality in patients with newly diagnosed DLBCL, even after adjusting for age, comorbidities, and health care utilization. Frailty appears to be associated with poor tolerability of chemotherapy and a higher likelihood for requiring acute hospital-based care, and future analyses will explore whether this is related to patients suffering increased treatment-related toxicity. Future analyses of these data will also address whether frail patients who die within one-year of first-line treatment have different clinical characteristics compared to frail patients who survive beyond one year. Future prospective studies may help clinicians understand whether any frailty-related variables are modifiable and the role of alternative treatment strategies for vulnerable patients. Disclosures Prica: astra zeneca: Honoraria; seattle genetics: Honoraria; Gilead: Honoraria.

Editor's letter for in:cite Journal vol. 3.

Objective: To describe interventions that target patient, provider, and system barriers to sedative-hypnotic (SH) deprescribing in the community and suggest strategies for healthcare teams. Data Sources: Ovid MEDLINE ALL and EMBASE Classic + EMBASE (March 10, 2021). Study Selection and Data Extraction: English-language studies in primary care settings. Data Synthesis: 20 studies were themed as patient-related and prescriber inertia, physician skills and awareness, and health system constraints. Patient education strategies reduced SH dose for 10% to 62% of participants, leading to discontinuation in 13% to 80% of participants. Policy interventions reduced targeted medication use by 10% to 50%. Relevance to Patient Care and Clinical Practice: Patient engagement and empowerment successfully convince patients to deprescribe chronic SHs. Quality improvement strategies should also consider interventions directed at prescribers, including education and training, drug utilization reviews, or computer alerts indicating a potentially inappropriate prescription by medication, age, dose, or disease. Educational interventions were effective when they facilitated patient engagement and provided information on the harms and limited evidence supporting chronic use as well as the effectiveness of alternatives. Decision support tools were less effective than prescriber education with patient engagement, although they can be readily incorporated in the workflow through prescribing software. Conclusions: Several strategies with demonstrated efficacy in reducing SH use in community practice were identified. Education regarding SH risks, how to taper, and potential alternatives are essential details to provide to clinicians, patients, and families. The strategies presented can guide community healthcare teams toward reducing the community burden of SH use.

Introduction: Clinical outcomes for patients with hematologic malignancy and COVID-19 have not been aggregated. We completed a systematic review and meta-analysis to estimate the risk of death and other important outcomes for these patients. Methods: We searched Pubmed and EMBASE up to July 25, 2020, to identify reports of patients with hematologic malignancy and COVID-19 (including papers where the patients with hematologic malignancy were a subset of the total study population). The primary outcome was a pooled mortality estimate, considering all patients and only hospitalized patients. Secondary outcomes included pooled estimates for the risk of ICU admission, mechanical ventilation, and non-invasive ventilation in hospitalized patients. Mortality data were stratified by age, treatment status, and malignancy subtype. For treatment status, "systemic anti-cancer therapy (SACT)" was defined as patients on any anti-cancer therapy. "Cytotoxic SACT" was defined as patients on cytotoxic therapy only. "Not on treatment" was defined as patients on observation or those who were at least 28 days beyond their last active treatment. Sensitivity analyses were conducted on the primary outcomes limiting to studies with low risk of bias, and to studies including both outpatients and hospitalized patients. Due to data limitations, only the primary outcome was assessed for pediatric studies. Pooled prevalence and risk ratios (RR) and 95% confidence intervals (CI) were calculated using a random-effects model using MetaXL and Revman 5.4 software. Results: A total of 25 adult studies and 4 pediatric studies comprising 1847 patients from China, Europe, the United Kingdom, and North America were included (Figure 1 and Table 1). The majority of patients were hospitalized (83%). The overall risk of death amongst all patients was 36% (95% CI 31-41, N=1763), and amongst hospitalized patients was 40% (95% CI 36-45, 24 studies with 1295 patients) (Figure 2). Patients aged >60 years had a significantly higher risk of death than patients The risk of ICU admission among hospitalized adult patients was 23% (95% CI 17-29, N=1165); mechanical ventilation 16% (95% CI 12-21, N= 826); and non-invasive ventilation 16% (95% CI 9-26%, N=373). The estimated RR of death among patients on SACT compared to no treatment was 1.22 (95% CI 0.84-1.78; N=457, Figure 3a). The RR of death among patients on cytotoxic SACT versus no treatment was similar at 1.29 (95% CI 0.78-2.15; N=176, Figure 3b). All subgroups of hematologic malignancy had high risks of overall mortality: acquired bone marrow dysfunction syndromes 57% (95% CI 42-72, 11 studies, 42 patients); leukemias 44% (95% CI 31-58, 15 studies, 159 patients), plasma cell dyscrasias 38% (95% CI 29-47, 18 studies, 387 patients); lymphomas (including CLL) 32% (95% CI 26-38, 16 studies, 696 patients); lymphomas (excluding CLL) 32% (95% CI 18-48, 11 studies, 156 patients); CLL 31% (95% CI 24-39, 13 studies, 457 patients); myeloproliferative neoplasms 37% (95% CI 25-49, 9 studies, 62 patients). Sensitivity analysis including only studies with a low risk of bias showed a similar estimate for risk of death among all patients (37% (95% CI 31-42, 20 studies with 1412 patients)) compared to all studies. Sensitivity analysis including only studies reporting on a combination of outpatients and hospitalized patients also showed a similar estimate for risk of death among all patients (38% (95% CI 32-44), 11 studies with 1214 patients) compared to all studies. Conclusion: Adult patients with hematologic malignancy and COVID-19, especially hospitalized patients, appear to experience a high risk of dying (pooled risk estimate 36%). Older patients experience higher mortality, and pediatric patients appear to be relatively spared. Importantly, based on the observational data available to date, recent cancer treatment does not appear to significantly increase the risk of dying. These data highlight the need for robust strategies to prevent patients with hematologic malignancy from contracting COVID-19, and may help inform discussions about prevention strategies, treatment, and goals of care. Disclosures Cook: Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; IQVIA: Research Funding; Sanofi: Consultancy; Amgen: Consultancy; Roche: Consultancy; Karyopharm: Consultancy. Zwicker:Dova: Honoraria, Other: Advisory board; Portola: Honoraria, Other: Advisory board; Incyte: Research Funding; Quercegen: Research Funding; Parexel: Consultancy; Sanofi: Consultancy; CSL: Consultancy; Pfizer/BMS: Honoraria, Other: Advisory board. Scarfo:Gilead: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Honoraria; Abbvie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Wood:Pfizer: Research Funding; Best Doctors/Teladoc: Consultancy; Koneksa Health: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; Elektra Labs: Current equity holder in private company, Membership on an entity's Board of Directors or advisory committees; ASH Research Collaborative: Honoraria; Genentech: Research Funding. Hicks:Gilead Sciences: Research Funding.

Limited data exist on predictors of intensive care unit (ICU) admission in patients with hematologic malignancy. The objective of this study was to identify predictors of ICU admission in hospitalized patients with hematologic malignancies. A retrospective cohort study was conducted on 820 consecutive admissions of patients with a malignant hematology diagnosis at our institution between March 2009 and December 2015. Backward stepwise selection procedure was conducted for multivariable logistic regression analyses. 820 patients were included, of whom 179 (22%) were admitted to the ICU. Types of hematologic cancers included 71% (N = 578) lymphoid cancer, 18% (N = 151) myeloid cancer, and 10% (N = 80) plasma cell neoplasms. 14% (N = 111) of patients had acute leukemia. Six predictors of admission to ICU were found in multivariable analysis, including disease-related (acute leukemia, curative intent chemotherapy), laboratory-related (platelet count p