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Introduction: The American College of Cardiology developed the National Cardiovascular Data Registry (NCDR) AFib Ablation Registry to assess prevalence, demographics, procedural management and outcomes of patients undergoing atrial fibrillation (AFib) ablation procedures. The objective of this registry’s first presentation is to summarize initial AFib Ablation Registry data regarding patient characteristics, procedural characteristics, and in-hospital adverse events and outcomes in those undergoing AFib ablation enrolled in the registry. Methods: Data were tabulated and summarized with simple proportions for categorical data, and mean ± standard deviation for continuous data. Results: A total of 31,345 patients who underwent AFib ablation were included in the registry (mean age 65.0 ± 10.4, 65.2% male, 57.6% paroxysmal AFib), with ablations performed at 120 hospitals, by 501 physicians. The mean CHA2DS2-VASc score of was 2.6 ± 1.6, and mean HAS-BLED score was 1.1 ± 0.9. Successful pulmonary vein isolation was able to be achieved in all veins in 94.5% of patients. The prevalence of any complication during procedural admission was 3.1%; the most common were hematoma (0.5%) and pericardial effusion requiring intervention (0.42%). The mean length of stay during the index admission (days) was 1.4 ± 7.6, and in-hospital death prevalence was 0.04% [FIGURE] . Conclusions: In conclusion, the NCDR AFib Ablation Registry is a large, national, multi-center, prospective cohort study to describe events outside the clinical trial or research setting, demonstrating successful pulmonary vein isolation in the majority of patients and a low prevalence of adverse events. Future studies from the registry will be able to help support and develop evidence-based guidelines for AFib ablation treatment and optimize patient outcomes.

INTRODUCTION Nearly 20% of patients (pts) are ≥ 80 years (yrs) of age at the time of CLL diagnosis. Increased medical comorbidities, as well as declining functional status and organ function, often accompany advanced age. However, clinical trials often enroll a disproportionally low number of people from this age demographic due to stringent eligibility parameters, which provides limited evidence to guide decisions. Here, we report clinical characteristics and outcomes of pts ≥ 80 yrs of age at the time of CLL diagnosis. METHODS Between 1/1995 and 3/2020, we identified previously untreated CLL pts from the Mayo Clinic CLL Database who were ≥ 80 yrs of age at the time of CLL diagnosis, and seen within 3 yrs of diagnosis. Baseline characteristics, treatments administered, and time to first treatment (TFT) were analyzed for all pts. Overall survival (OS) was measured from diagnosis date in all pts and separately from treatment date for treated pts. Time to next treatment (TTNT) was measured in treated pts from first treatment date to date of second treatment or last known treatment. TFT and TTNT were analyzed accounting for competing risk of death. Standardized mortality ratio (SMR) compared observed survival to survival in a Minnesota population. Cox multivariable regression models were used to determine which factors were associated with OS (treatment as a time-dependent covariate) and TTNT (accounting for competing risk of death). RESULTS Among the 213 pts identified, the median age was 83 yrs (range 80-97); baseline characteristics are summarized in Table 1. The median number of medical comorbidities among all pts was 4 (range 0-8). At least 1 comorbidity was present in 97% of pts, most commonly hypertension (57%), genitourinary (n=48%), rheumatological (46%), and dyslipidemia (n=43%). Median follow-up was 3.8 yrs. A total of 56 pts received treatment and the median TFT was 6.7 yrs. Over this 25 year interval, the treatment approaches included monoclonal antibody alone (n=17), chemotherapy alone (n=17), or chemoimmunotherapy (n=14), novel agents (n=3), and multi-agent Richter's transformation regimens (n=5). Chlorambucil-based treatments (chlorambucil n=13, chlorambucil, prednisone n=3) were the most frequently used chemotherapy regimens. The most common chemoimmunotherapy regimens included rituximab, cyclophosphamide, prednisone (RCP) with (n=3) or without vincristine (n=6), and chlorambucil with an anti-CD20 monoclonal antibody (n=4; obinutuzumab n=2, rituximab n=2). Among 17 pts who received monoclonal antibody alone, treatments included rituximab alone (n=11), rituximab plus methylprednisolone (n=2), obinutuzumab (n=2), and rituximab with alemtuzumab (n=2). Frontline novel agent treatments included ibrutinib alone (n=1), acalabrutinib alone (n=1), and ibrutinib, obinutuzumab, venetoclax (n=1). Five pts with Richter's transformation (diagnosed concurrent with CLL n=4) received multi-agent chemotherapy. Among the 56 treated pts, 22 pts received second line therapy after a median TTNT of 2.8 yrs from start of first therapy. The most common second line treatments were chlorambucil-based (n=6) and rituximab-based (n=6) regimens. Age, sex, absolute lymphocyte count, del17p, and unmutated IGHV were not predictive of TTNT. The median OS among all 213 pts was 4.6 yrs. SMR was 1.2 (p=0.008) when excluding pts with Richter's transformation. In univariable analyses, receipt of therapy (HR 3.92, 95%CI 2.11-7.28; p<0.001), age (per 5 year increase; HR 1.84, 95%CI 1.43-2.38; p<0.001) and unmutated IGHV (HR 2.03, 95%CI 1.12-3.67; p=0.02) were predictors of shorter OS. The OS among pts who received treatment (Figure 1) was similar when compared across time periods of treatment start year grouped as 1995-2001 (n=10), 2002-2013 (n=32), and 2014-2020 (n=14). Treatment categories by time period are shown in Table 2. CONCLUSIONS In this study of 213 pts ≥ 80 yrs of age at time of CLL diagnosis, the median time to first therapy was ~7 yrs. Although survival outcomes did not differ by era across the 25-year span of this study, pts treated with novel agents were underrepresented and are a patient population which warrants additional evaluation. The finding that pts with CLL in this cohort had a 20% higher risk of death compared to an age- and sex-matched population emphasize the need for ongoing efforts to improve clinical outcomes for CLL pts in this demographic category. Disclosures Kenderian: Torque: Consultancy; Mettaforge: Patents & Royalties; Humanigen: Consultancy, Patents & Royalties, Research Funding; Novartis: Patents & Royalties, Research Funding; Kite: Research Funding; Gilead: Research Funding; Juno: Research Funding; BMS: Research Funding; Tolero: Research Funding; Sunesis: Research Funding; MorphoSys: Research Funding; Lentigen: Research Funding. Wang:Incyte: Research Funding; Innocare: Research Funding; Novartis: Research Funding. Shanafelt:Genentech, Pharmacyclics LLC, an AbbVie Company, AbbVie, GlaxoSmithKline, and Merck: Research Funding; Mayo Clinic: Patents & Royalties: and other intellectual property. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Kay:Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acerta Pharma: Research Funding; Dava Oncology: Membership on an entity's Board of Directors or advisory committees. Ding:alexion: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; Octapharma: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; Astra Zeneca: Research Funding; Beigene: Membership on an entity's Board of Directors or advisory committees; DTRM: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding. Parikh:Merck: Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; MorphoSys: Research Funding; Pharmacyclics: Honoraria, Research Funding; AbbVie: Honoraria, Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; Verastem Oncology: Honoraria; GlaxoSmithKline: Honoraria; TG Therapeutics: Research Funding.

Background: Cytogenetic abnormalities detected by fluorescence in situ hybridization (FISH) are known to differentially impact prognosis in patients with chronic lymphocytic leukemia (CLL). Deletion6q23 (del6q23) has been reported in ~2-3% of CLL patients at diagnosis; however, its prognostic significance remains indeterminate. Methods: We identified previously untreated CLL patients from the Mayo Clinic CLL Database seen between 1/1995 - 2/2020 who had FISH performed. The clinical characteristics, concomitant cytogenetic abnormalities, time to first therapy (TTFT), and overall survival (OS) were compared between patients with del6q23 to those without this abnormality. TTFT was analyzed from FISH date until treatment or last known untreated date, accounting for competing risk of death. OS was analyzed using Kaplan-Meier methods from FISH date. In a matched (CLL-IPI risk score and sex) analysis, Cox regression analysis was used to determine the association between TTFT/OS and presence of del6q23. The Mayo Clinic IRB approved this study. Results: A total of 3101 CLL patients were identified; their median age at diagnosis was 64 years and 66% were male. Of these patients, 42 (1.3%) had evidence of del6q23 by FISH. Compared to patients without del6q23, patients with del6q23 carried a more adverse clinical and cytogenetic profile at the time of diagnosis including advanced Rai stage (22% Rai III/IV vs. 9% Rai III/IV; P = 0.002), unmutated IGHV genes (91% vs. 43%; P < 0.001), and high/very high risk CLL-IPI (50% vs. 27%; P < 0.001). Del6q23 was the sole abnormality in 13 patients (31%). Of the remaining patients, del6q23 was concomitantly present with del13q in 8 patients (19%), and with either del11q or del17p in 21 patients (50%). Of the 42 patients with del6q23, 29 received first line therapy: chemoimmunotherapy in 16 patients, BTK inhibitor in 7 patients, and an anti-CD20 monoclonal antibody alone in 6 patients. The median follow-up of the entire cohort was 7.3 years. The median TTFT in patients with del6q23 was 0.7 years while the 5-year OS was 76%. Patients with del6q23 demonstrated significantly shorter TTFT and reduced OS when compared to patients without del6q23 (Figures 1A and 1B). The shorter TTFT was also evident for patients with del6q23 compared to patients without del6q23 divided according to the Dohner risk category (Figure 2A), although there was no difference in OS between these groups of patients (Figure 2B). Because del6q23 patients were enriched for other unfavorable prognostic markers, we identified four control patients without del6q23, matched by CLL-IPI risk score and sex, to each treatment-naïve patient with del6q23. Patients with del6q23 showed a borderline statistically significant shorter TTFT (median 0.7 years versus 3.9 years; P = 0.07) and a nonsignificant association with OS (median 10.9 years versus 10.3 years; P = 0.28). Conclusion: Del6q23 by FISH in previously untreated CLL represents an uncommon abnormality. When compared to patients without del6q23, patients with del6q23 tend to have more adverse clinical and cytogenetic profiles at baseline, which likely confer a worse prognosis as reflected by shorter TTFT compared to patients without this abnormality. Figure Disclosures Ding: Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; DTRM: Research Funding; Beigene: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; Octapharma: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; Astra Zeneca: Research Funding. Wang:Novartis: Research Funding; Innocare: Research Funding; Incyte: Research Funding. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Kay:Sunesis: Research Funding; Abbvie: Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Acerta Pharma: Research Funding; MEI Pharma: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees. Kenderian:Juno: Research Funding; Gilead: Research Funding; Lentigen: Research Funding; MorphoSys: Research Funding; Sunesis: Research Funding; Kite: Research Funding; Novartis: Patents & Royalties, Research Funding; Torque: Consultancy; Humanigen: Consultancy, Patents & Royalties, Research Funding; Mettaforge: Patents & Royalties; Tolero: Research Funding; BMS: Research Funding. Parikh:Ascentage Pharma: Research Funding; Merck: Research Funding; AbbVie: Honoraria, Research Funding; AstraZeneca: Honoraria, Research Funding; TG Therapeutics: Research Funding; Pharmacyclics: Honoraria, Research Funding; GlaxoSmithKline: Honoraria; Janssen: Honoraria, Research Funding; MorphoSys: Research Funding; Genentech: Honoraria; Verastem Oncology: Honoraria.

Abstract Buffered lidocaine is a local anesthetic option during percutaneous needle-directed procedures in the breast. At our institution, sodium bicarbonate (the buffer) is dispensed in volumes that frequently lead to medical waste and shortages. In this study, we describe how moving the buffering of lidocaine from the procedure room to our clinical hospital pharmacy results in a reduction in costs and improves satisfaction across the breast radiology department. While cost savings are difficult to tease out in practices that opt for bundled payments, we were able to access pricing and supply data and coordinate with our pharmacy to change our practice. Making these changes saves our practice $26 000 a year and allows us to continue to offer buffered lidocaine even during sodium bicarbonate shortages. This manuscript describes how these changes came about and their economic impact.

Background Current adverse event reporting practices do not document longitudinal characteristics of adverse effects, and alternative methods are not easily interpretable and have not been employed by clinical trials. Introducing time parameters in the evaluation of safety that are comprehensive yet easily interpretable could allow for a better understanding of treatment quality. In this study, we developed and applied a novel adverse event reporting method based on longitudinal adverse event changes to aid describing, summarizing, and presenting adverse event profile. We termed it the “Adverse Event Load, Onset, and Maximum Grade” method. Methods We developed two adverse event summary metrics to complement the traditional maximum grade report. Onset time indicates the time period in which the maximum grade for a specific adverse event occurred and was defined as “early” (i.e. maximum grade happened for the first time before 6 weeks) or “late” (i.e. after the 6 th week). Adverse event load indicates the overall severity of a specific adverse event over the entire treatment. Higher adverse event load indicates a worse overall experience. These metrics can be calculated for adverse events with different maximum grades, in treatments with planned changes (e.g. dosage changes), using data sets with different number of adverse event data points between treatments (e.g. treatments with longer cycle lengths may have less adverse event data points) and on data sets with different adverse event data availability (e.g. cycle basis and patient-outcome reports). We tested the utility of this method using individual patient data from two major backbone therapies (“Irinotecan” and “Oxaliplatin”) from the N9741 trial available in the Fondation ARCAD database ( fondationarcad.org ). We investigated profiles of diarrhea, neutropenia/leukopenia, and nausea/vomiting. Results Our method provided additional information compared to traditional adverse event reports. For example, for nausea/vomiting, while patients in Irinotecan had a higher risk of experiencing maximum grade 3–4 (15.6% vs 7.6%, respectively; p < 0.001), patients in both groups experienced similar severity over time (adverse effect load = 0.102 and 0.096, respectively; p = 0.26), suggesting that patients in Oxaliplatin experienced a lower-grade but more persistent nausea/vomiting. For neutropenia/leukopenia, more patients in Irinotecan experienced their maximum grade for the first time early in the treatment compared to patients in Oxaliplatin (67.9% vs 41.7%; p < 0.001), regardless of maximum grade. Longitudinal information can help compare treatments or guide clinicians on choosing appropriate interventions for low-grade but persistent adverse event or early adverse event onset. Conclusion We developed an adverse event reporting method that provides clinically relevant information about treatment toxicity by incorporating two longitudinal adverse event metrics to the traditional maximum grade approach. Future research should establish clinical benchmarks for metrics included in this adverse event reporting method.