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Abstract Background: The ECOG-ACRIN randomized phase 3 clinical trial E1912 established ibrutinib-rituximab (IR) as the standard of care for CLL patients <= 70 years in the frontline setting by improving the progression free (PFS) and overall survival (OS) compared to previous standard chemo-immunotherapy with fludarabine, cyclophosphamide and rituximab (FCR) (Shanafelt et al. NEJM 2019). Quality of life (QOL) was an important secondary endpoint on this trial comparing continuous (IR) vs time-limited (FCR) therapy due to the differing profile of treatment-related toxicities over the short and long-term. E1912 patient-reported outcomes (PROs) results are presented here. Methods: Patients enrolled on E1912 completed the Functional Assessment of Cancer Therapy-General (FACT-G) and Leukemia subscale at randomization (baseline), 3, 6, and 12 months post-randomization, and every 6 months for 2 years regardless of disease status. The primary outcome was the 31-item FACT-Leukemia Trial Outcome Index (FACT-Leu TOI), calculated by summing items from the FACT-G physical wellbeing (PWB), functional well-being (FWB), and leukemia subscales (score range: 0-124). The primary endpoint was defined as the difference in FACT-Leu TOI change scores from randomization to 12 months (at response evaluation) between patients treated with continuous therapy Arm A (IR) vs time-limited therapy Arm B (FCR). Mean change scores from baseline to each time point were calculated using all cases with data at baseline and the corresponding time point. Comparisons between treatment arms were performed using two-sample t tests. Linear mixed effects models were used to estimate the trajectories of PRO scores. Results: PRO data was extracted on 3/8/2021. PRO data was provided at baseline and 12 months for 233/354 (65.8%) patients on the IR arm and 118/175 (67.4%) patients on the FCR arm. At enrollment, there were no significant differences in the baseline FACT-Leu TOI scores (mean ± SE) between the two arms: IR (93.27 ± 1.03) vs FCR (92.68 ± 1.38; p=0.73). The FACT-Leu TOI score improved from baseline to 12 months in both arms (Table 1 and Figure 1). The change scores from baseline to 12 months, the primary outcome, were not significantly different between IR (7.59 ± 1.09) vs FCR (8.22 ± 1.44; p=0.73). Change in FACT-Leu TOI from baseline to 3 months was 5.77 ± 0.77 and 4.06 ± 1.18 (p=0.22); and from baseline to 6 months was 6.87 ± 0.87 and 8.01 ± 1.44 (p=0.50) in the IR and FCR arms, respectively. After the first 6 months of treatment, the improvement in FACT-Leu TOI scores was maintained in both treatment arms. There was no significant difference in total FACT-Leu TOI score in the continuous therapy (IR) vs time-limited therapy (FCR) arms over the first 36 months post registration. Analysis of FACT subscales showed PWB improved in the IR arm (0.37 ± 0.22) and declined in the FCR arm (-0.92 ± 0.39) from baseline to 3 months (p=0.004 for difference in PWB change between arms) but there was no significant difference between the two arms for change in PWB scores from baseline to 6 and 12 months. There were no significant differences between the two arms for change in FWB and FACT-Leu subscales from baseline to 3, 6 and 12 months. Conclusions: QOL improves in CLL patients age 70 and younger when treated in the frontline setting with IR or FCR. The improvement in QOL is maintained during continuous therapy with ibrutinib and there is no significant decline in QOL over time. Given the improvement in PFS and OS with IR over FCR seen in E1912, the results of this QOL analysis support the use of frontline ibrutinib in previously untreated younger CLL patients. Acknowledgement: This study was conducted by the ECOG-ACRIN Cancer Research Group (Peter J. O'Dwyer, MD and Mitchell D. Schnall, MD, PhD, Group Co-Chairs) and supported by the National Cancer Institute of the National Institutes of Health under award numbers: U10CA180794, U10CA180820, UG1CA189863, UG1CA190140, UG1CA232760, UG1CA233180, UG1CA233230, UG1CA233253, UG1CA233277, UG1CA233290, UG1CA233339, UG1CA189859, UG1CA233332. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Figure 1 Figure 1. Disclosures Kay: Oncotracker: Membership on an entity's Board of Directors or advisory committees; CytomX Therapeutics: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Research Funding; Acerta Pharma: Research Funding; Behring: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Juno Therapeutics: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Targeted Oncology: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Research Funding; Sunesis: Research Funding; Genentech: Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding. O'Brien: Amgen, Astellas, Celgene, GlaxoSmithKline, Janssen Oncology, Aptose Biosciences Inc., Vaniam Group LLC, AbbVie, Alexion, Verastem, Juno Therapeutics, Vida Ventures, Autolus, Johnson and Johnson, Merck, Bristol Myers Squibb, NOVA Research Company, Eli Lill: Consultancy; Kite, Regeneron, Acerta, Caribou, Gilead, Pharmacyclics, TG Therapeutics, Pfizer, Sunesis: Research Funding. Coutre: Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; AstraZeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees; Beigene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Data Safety Monitoring Committee, Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees; Acerta: Other: Data Safety Monitoring Committee, Research Funding. Barr: AstraZeneca: Consultancy; Gilead: Consultancy; Janssen: Consultancy; Genentech: Consultancy; Beigene: Consultancy; Bristol Meyers Squibb: Consultancy; Seattle Genetics: Consultancy; TG Therapeutics: Consultancy; Morphosys: Consultancy; Abbvie/Pharmacyclics: Consultancy. Cashen: Secura Bio, ADC Therapeutics: Consultancy. Mato: Adaptive Biotechnologies: Consultancy, Research Funding; DTRM BioPharma: Consultancy, Research Funding; Johnson and Johnson: Consultancy, Research Funding; Acerta/AstraZeneca: Consultancy, Research Funding; Sunesis: Consultancy, Research Funding; AstraZeneca: Consultancy; BeiGene: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; LOXO: Consultancy, Research Funding; MSKCC: Current Employment; Pharmacyclics LLC, an AbbVie Company: Consultancy, Research Funding; TG Therapeutics: Consultancy, Other: DSMB, Research Funding; Nurix: Research Funding; Janssen: Consultancy, Research Funding; Genmab: Research Funding. Erba: AbbVie Inc; Agios Pharmaceuticals Inc; Astellas; Bristol Myers Squibb; Celgene, a Bristol Myers Squibb company; Daiichi Sankyo Inc; Genentech, a member of the Roche Group; GlycoMimetics Inc; Incyte Corporation; Jazz Pharmaceuticals Inc; Kura Oncology; Nov: Other: Advisory Committee; AbbVie Inc: Other: Independent review committee; AbbVie Inc; Agios Pharmaceuticals Inc; ALX Oncology; Amgen Inc; Daiichi Sankyo Inc; FORMA Therapeutics; Forty Seven Inc; Gilead Sciences Inc; GlycoMimetics Inc; ImmunoGen Inc; Jazz Pharmaceuticals Inc; MacroGenics Inc; Novartis; PTC Therapeutics: Research Funding; AbbVie Inc; Agios Pharmaceuticals Inc; Bristol Myers Squibb; Celgene, a Bristol Myers Squibb company; Incyte Corporation; Jazz Pharmaceuticals Inc; Novartis: Speakers Bureau. Stone: Onconova: Consultancy; AbbVie: Consultancy; Boston Pharmaceuticals: Consultancy; Bristol Myers Squibb: Consultancy; Gemoab: Membership on an entity's Board of Directors or advisory committees; Elevate Bio: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Research Funding; Jazz: Consultancy; Syros: Membership on an entity's Board of Directors or advisory committees; Syndax: Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Research Funding; Takeda: Consultancy; BerGen Bio: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Foghorn Therapeutics: Consultancy; Innate: Consultancy; Janssen: Consultancy; GlaxoSmithKline: Consultancy; Amgen: Membership on an entity's Board of Directors or advisory committees; Arog: Consultancy, Research Funding; Actinium: Membership on an entity's Board of Directors or advisory committees; Syntrix/ACI: Membership on an entity's Board of Directors or advisory committees; Aprea: Consultancy; Celgene: Consultancy; Macrogenics: Consultancy. Litzow: Actinium: Research Funding; Amgen: Research Funding; AbbVie: Research Funding; Pluristem: Research Funding; Jazz: Other: Advisory Board; Omeros: Other: Advisory Board; Astellas: Research Funding; Biosight: Other: Data monitoring committee. Tallman: NYU Grand Rounds: Honoraria; Kura: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Innate Pharma: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Biosight: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Jazz Pharma: Membership on an entity's Board of Directors or advisory committees; Oncolyze: Membership on an entity's Board of Directors or advisory committees; KAHR: Membership on an entity's Board of Directors or advisory committees; Orsenix: Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Rafael Pharmaceuticals: Research Funding; Glycomimetics: Research Funding; Biosight: Research Funding; Orsenix: Research Funding; Abbvie: Research Funding; Mayo Clinic: Honoraria; UC DAVIS: Honoraria; Northwell Grand Rounds: Honoraria; NYU Grand Rounds: Honoraria; Danbury Hospital Tumor Board: Honoraria; Acute Leukemia Forum: Honoraria; Miami Leukemia Symposium: Honoraria; New Orleans Cancer Symposium: Honoraria; ASH: Honoraria; NCCN: Honoraria. Shanafelt: Genentech, Pharmacyclics: Research Funding. Wagner: Eli Lilly, Johnson & Johnson: Other: Spouse, previously held individual stocks;; Celgene-BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Athenex Inc: Consultancy.

Abstract Introduction Measures taken to mitigate infection spread during the 2020 COVID-19 pandemic are considered to have caused significant unintended consequences on other diseases. Large decreases in the numbers of symptomatic and asymptomatic people presenting for diagnosis of heart disease, diabetes and cancer have been observed. A recent analysis of solid tumors showed up to 70% reduction in the number of patients presenting for diagnosis. The potential exists for significantly increased morbidity and mortality for these missed or delayed presenting patients. Further, it is important to determine whether infection spread mitigation measures affected the diagnostic testing and treatment decisions for these patients. This study aimed to determine whether pandemic control measures affected presentation, testing and treatment of patients across eight different hematologic cancers. Methods CMS claims data were analyzed for the presence of diagnostic (DX) ICD 10 codes indicative of hematologic cancer. Patients with a DX code first appearing in 2019 or in 2020 were selected to provide newly diagnosed pre-COVID-19 and during COVID-19 cohorts for comparison, with unique patient counts being calculated for each month. A "COVID-19 dip" i.e. a decrease in the number of patients was calculated as the change in number of patients diagnosed in a given month relative to the number for JAN2020. Dip duration was calculated only when the decrease was >10% of the JAN2020 figure. Patients who received treatment via a "J" code Healthcare Common Procedure Coding System (HCPCS) code were extracted from the cohorts and the time taken from initial diagnosis to first treatment calculated. Results Eight hematologic cancers: AML, CLL, CML, HEME (a group of different hematologic cancers), Hodgkins (HOG), Myelodysplasia (MDS), Non-Follicular Lymphomas (NFL), and Non-Hodgkins Lymphoma (NHL) showed a decrease in the number of patients being diagnosed during the early part of 2020 (Fig.1) Fig.1. Change in new patient diagnoses for selected hematologic cancers as a proportion of their JAN2020 value There was some variation in the depth and duration of the COVID-19 dip (Table 1) with MDS having both the longest and deepest dip. Median depth and duration of the dip was 33% and 3.5 months, respectively, with all dips starting either in FEB or MAR2020. Table 1. Duration and depth of COVID-19 dips for selected hematological cancers The proportions of patients receiving therapy via J HCPCS code (JRX) are shown in Table 2 Table 2. Proportions of patients receiving J code therapy Conclusions The decline in new patient diagnoses for heme cancers during the period when COVID-19 control measures were implemented is similar to that seen with solid tumors, although the depth of the COVID-19 dip was generally larger in the latter. There is no evidence of "catch up" diagnosis occurring i.e. patients missing from Q2 2020 are not reappearing en masse in subsequent quarters. The decline for MDS patients has, except for SEP to OCT2020, remained. Collectively, (depending on the calculation method), the COVID-19 dip for these eight heme cancers represents 16,584-33,671 patients who will likely have significantly increased rates of morbidity and mortality due to delayed diagnosis. Analysis of J code treatments show little difference between the proportions of patients receiving these treatments in 2020 compared to 2019 suggesting that at least some aspects of treatment e.g. infused chemotherapy, IO drugs for these patients was relatively unchanged by pandemic control measures. It also suggests that the main cause for decreased patient numbers treated is due to decreased testing for diagnosis, rather than not being treated once diagnosed. This aligns with findings from studies in the US and UK. The results of this study indicate that there may be a "backlog" of tens of thousands of people with cancer whose diagnosis has been significantly delayed and who urgently need to be identified in order to get on proper treatment to lessen the impact of that delay. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Abstract BACKGROUND PCV+RT substantially prolongs survival in AO patients, but long-term CF and QOL implications are unclear. We compared CF and QOL by treatment arm in RTOG 9402 participants and evaluated the impact that baseline characteristics had on CF, QOL, and survival. METHODS CF and QOL were evaluated using the Mini Mental State Exam (MMSE) and Brain-Quality of Life (B-QOL) scale at baseline and annually. Scores were analyzed between treatment arms at each time point for patients with ≥ 10 years of follow-up data. Shared parameter models evaluated MMSE and B-QOL scores and survival for all patients. RESULTS 42/148 (28.4%) participants in PCV+RT and 20/143 (14%) in RT alone arms survived ≥ 10 years. 35/42 and 39/42 (PCV+RT) and 18/20 and 17/20 (RT) participants completed baseline B-QOL and MMSE assessments, respectively. B-QOL scores did not differ between treatment groups at any time-point. Among 16 patients (10 PCV+RT, 6 RT) who completed year 10 MMSE evaluations, mean MMSE score at 10 years was higher in the RT arm (29.83 [95% CI 22.1, 30.0] vs. 26.50 [95% CI 29.4, 30.0], P= 0.04). Change in MMSE and B-QOL scores from baseline did not differ significantly between treatment groups at any time. In shared parameter models including all patients with baseline assessments, MMSE and B-QOL scores decreased over time (MMSE P= 0.0189, B-QOL P= 0.0005), but this did not differ by treatment group (MMSE P= 0.5727, B-QOL P= 0.3592). Younger age and higher KPS predicted better scores (MMSE P < 0.0001, P = 0.0002; B-QOL P = 0.0043, P = 0.0007). PCV+RT predicted better survival in both models. CONCLUSIONS PCV+RT improves survival in AO. Shared parameter models show decrease in MMSE and B-QOL over time. However, relative to RT alone, the addition of PCV did not impact change in CF and QOL over time.

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes—termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence–enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.

ObjectivePsoriatic arthritis (PsA) and ankylosing spondylitis (AS) are chronic inflammatory diseases associated with a higher risk of cardiometabolic comorbidities compared to the general population. Individual studies examining mortality in these patients have produced conflicting results. The present study was undertaken to perform a systematic review and meta‐analysis to analyze the all‐cause and cause‐specific mortality in PsA and AS from the available literature.MethodsA comprehensive database search was performed for studies reporting all‐cause or cause‐specific mortality in patients with PsA and AS compared with the general population. Pooled relative risks (RRs) were calculated using a random‐effects model.ResultsWe included 19 studies (11 of PsA, 7 of AS, 1 of both). In PsA studies, there was no increased mortality compared to the general population (RR 1.12 [95% confidence interval (95% CI) 0.96–1.30]; n = 10 studies). We found a higher all‐cause mortality in female (RR 1.19 [95% CI 1.04–1.36]) but not in male (RR 1.02 [95% CI 0.66–1.59]) PsA patients. Cardiovascular‐, respiratory‐, and infection‐specific mortality risks were significantly higher for PsA patients (RR 1.21 [95% CI 1.06–1.38], RR 3.37 [95% CI 1.30–8.72], and RR 2.43 [95% CI 1.01–5.84], respectively), but not cancer‐related mortality (RR 1.01 [95% CI 0.91–1.11]). In AS, we found a higher risk of death from all causes (RR 1.64 [95% CI 1.49–1.80]; n = 6 studies) and cardiovascular causes (RR 1.35 [95% CI 1.01–1.81]; n = 3 studies) compared to the general population. All‐cause mortality was high in both male (RR 1.56 [95% CI 1.43–1.71]) and female (RR 1.85 [95% CI 1.56–2.18]) AS patients. The included AS studies did not report mortality data for non‐cardiovascular causes.ConclusionThis systematic review and meta‐analysis showed a significantly increased risk of overall mortality in AS but not PsA. Cardiovascular‐specific mortality was higher for both PsA and AS, which emphasizes the importance of early screening and management of cardiovascular risk factors.

Abstract The management of chronic lymphocytic leukemia (CLL) has undergone unprecedented changes over the last decade. Modern targeted therapies are incorporated into clinical practice. Unfortunately, patients have begun to develop resistance or intolerance to multiple classes. Symptomatic patients previously treated with a BTK inhibitor (BTKi) and venetoclax represent a new and rapidly growing unmet need in CLL. Here, we define unmet needs in a modern treatment context. We also critically review the literature for PI3K inhibitors and chemoimmunotherapy and lack of data to support their utility following BTKis and venetoclax. Finally, we suggest opportunities to ensure the continued innovation for patients with CLL.

Purpose To generate an evidence‐based opioid‐prescribing guideline by assessing the pattern of total opioid consumption and the factors that may predict opioid consumption following arthroscopic release of elbow contracture and to investigate whether the use of continuous passive motion (CPM), as compared to physical therapy (PT), was associated with a decrease in pain and opioid consumption after arthroscopic release of elbow contracture. Methods Data collected from a randomized controlled trial that compared continuous passive motion (CPM) ( n = 24) to physical therapy (PT) ( n = 27) following arthroscopic release of elbow contracture was analyzed for opioid use. Fifty‐one participants recorded their daily opioid consumption in a postoperative diary for 90 days. Multivariate analysis was performed to identify factors associated with opioid use. Recommended quantities for postoperative prescription were generated using the 50th percentile for patients without and the 75th percentile for patients with factors associated with higher opioid use. Results The median total opioid prescription was 437.5‐mg morphine milligram equivalents (MMEs) (58 pills of 5 mg oxycodone) and the median total opioid consumption was 75 MMEs (10 pills of 5‐mg oxycodone). Twenty‐two percent of patients took no opioid medication, 53% took ≤10 pills, 69% took ≤20 pills and 75% took ≤30 pills. Predictors of higher opioid use were preoperative opioid use, age <60 years and inflammatory arthritis. The total opioid consumption appeared similar between the CPM and the PT group. Seventy‐five percent of patient’s home opioid requirements would be satisfied using the following guideline: Patients undergoing contracture release for osteoarthritis or post‐traumatic contracture should be given a prescription for 10 pills of 5 mg oxycodone or its equivalent at discharge. Patients with inflammatory conditions or those taking preoperative opioids should be prescribed 30 pills of 5 mg oxycodone or its equivalent. Conclusion This study suggests that most patients undergoing arthroscopic release of elbow contracture use relatively few opioid pills after surgery. Use of an evidence‐based guideline could decrease opioid prescriptions substantially, while still effectively treating patients’ pain.

Carotid plaque vulnerability features beyond the degree of stenosis may play a key role in the pathogenesis and recurrence of ischemic cerebrovascular events. This study sought to compare intraplaque hemorrhage (IPH) as a marker of plaque vulnerability in symptomatic patients with mild (<50%), moderate (50%–69%), and severe (≥70%) carotid artery stenosis. We included patients who experienced ischemic cerebrovascular events with no other identifiable sources and underwent carotid endarterectomy for mild (n=32), moderate (n=47), and severe (n=58) carotid artery stenosis. The degree of stenosis and imaging hallmarks were assessed by computed tomography angiography or magnetic resonance angiography. Plaque specimens were stained with hematoxylin and eosin and Movat pentachrome staining. Carotid plaques of patients with mild stenosis had a higher extent of IPH (%) on tissue analysis compared with patients with moderate (mild, 15.7% [interquartile range, 7.8%–26.7%]; moderate, 3.9% [0.0%–9.2%]; P <0.001) and severe carotid artery stenosis (mild, 15.7% [interquartile range, 7.8%–26.7%]; severe, 2.5% [interquartile range, 0.0%–11.2%]; P <0.001). When considering the degree of carotid artery stenosis as a continuous variable, a lower lumen narrowing was associated with higher extent of IPH ( P <0.001; R, −0.329). Our major finding is the association of IPH with mild carotid artery stenosis based on histological analysis. The current study may suggest that IPH potentially plays a role in the mechanism of stroke in patients with nonobstructive carotid stenosis.