Citations

AbstractAimsThe aims of the Critical Care Cardiology Trials Network (CCCTN) are to develop a registry to investigate the epidemiology of cardiac critical illness and to establish a multicentre research network to conduct randomised clinical trials (RCTs) in patients with cardiac critical illness.Methods and resultsThe CCCTN was founded in 2017 with 16 centres and has grown to a research network of over 40 academic and clinical centres in the United States and Canada. Each centre enters data for consecutive cardiac intensive care unit (CICU) admissions for at least 2 months of each calendar year. More than 20 000 unique CICU admissions are now included in the CCCTN Registry. To date, scientific observations from the CCCTN Registry include description of variations in care, the epidemiology and outcomes of all CICU patients, as well as subsets of patients with specific disease states, such as shock, heart failure, renal dysfunction, and respiratory failure. The CCCTN has also characterised utilization patterns, including use of mechanical circulatory support in response to changes in the heart transplantation allocation system, and the use and impact of multidisciplinary shock teams. Over years of multicentre collaboration, the CCCTN has established a robust research network to facilitate multicentre registry-based randomised trials in patients with cardiac critical illness.ConclusionThe CCCTN is a large, prospective registry dedicated to describing processes-of-care and expanding clinical knowledge in cardiac critical illness. The CCCTN will serve as an investigational platform from which to conduct randomised controlled trials in this important patient population.

ImportanceSelf-measured blood pressure (SMBP) with commercially available connected smartphone applications may help patients effectively use SMBP measurements.ObjectiveTo determine if enhanced SMBP paired with a connected smartphone application was superior to standard SMBP for blood pressure (BP) reduction or patient satisfaction.Design, Setting, and ParticipantsThis randomized clinical trial was conducted among 23 health systems participating in PCORnet, the National Patient-Centered Clinical Research Network, and included patients who reported having uncontrolled BP at their last clinic visit, a desire to lower their BP, and a smartphone. Enrollment and randomization occurred from August 3, 2019, to December 31, 2020, which was followed by 6 months of follow-up for each patient. Analysis commenced shortly thereafter.InterventionsEligible participants were randomly assigned to enhanced SMBP using a device that paired with a connected smartphone application (enhanced) or a standard device (standard). Participants received their device in the mail, along with web-based educational materials and phone-based support as needed. No clinician engagement was undertaken, and the study provided no special mechanisms for delivering measurements to clinicians for use in BP management.Main Outcomes and MeasuresReduction in systolic BP, defined as the difference between clinic BP at baseline and the most recent clinic BP extracted from electronic health records at 6 months.ResultsEnrolled participants (1051 enhanced [50.0%] vs 1050 standard [50.0%]; 1191 women [56.7%]) were mostly middle-aged or older (mean [SD] age, 58 [13] years), nearly a third were Black or Hispanic (645 [31%]), and most were relatively comfortable using technology (mean [SD], 4.1 [1.1] of 5). The mean (SD) change in systolic BP from baseline to 6 months was −10.8 (18) mm Hg vs −10.6 (18) mm Hg (enhanced vs standard: adjusted difference, −0.19 mm Hg; 95% CI, −1.83 to 1.44;P = .81). Secondary outcomes were mostly null, except for documented attainment of BP control to lower than 140/<90 mm Hg, which occurred in 32% enhanced vs 29% standard groups (odds ratio, 1.15; 95% CI, 1.01-1.34). Most participants were very likely to recommend their SMBP device to a friend (70% vs 69%).Conclusions and RelevanceThis randomized clinical trial found that enhanced SMBP paired with a smartphone application is not superior to standard SMBP for BP reduction or patient satisfaction.Trial RegistrationClinicalTrials.gov Identifier:NCT03796689

OBJECTIVES: To determine the association of prior use of renin-angiotensin-aldosterone system inhibitors (RAASIs) with mortality and outcomes in hospitalized patients with COVID-19. DESIGN: Retrospective observational study. SETTING: Multicenter, international COVID-19 registry. Subjects: Adult hospitalized COVID-19 patients on antihypertensive agents (AHAs) prior to admission, admitted from March 31, 2020, to March 10, 2021. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Data were compared between three groups: patients on RAASIs only, other AHAs only, and those on both medications. Multivariable logistic and linear regressions were performed after controlling for prehospitalization characteristics to estimate the effect of RAASIs on mortality and other outcomes during hospitalization. Of 26,652 patients, 7,975 patients were on AHAs prior to hospitalization. Of these, 1,542 patients (19.3%) were on RAASIs only, 3,765 patients (47.2%) were on other AHAs only, and 2,668 (33.5%) patients were on both medications. Compared with those taking other AHAs only, patients on RAASIs only were younger (mean age 63.3 vs 66.9 yr; p < 0.0001), more often male (58.2% vs 52.4%; p = 0.0001) and more often White (55.1% vs 47.2%; p < 0.0001). After adjusting for age, gender, race, location, and comorbidities, patients on combination of RAASIs and other AHAs had higher in-hospital mortality than those on RAASIs only (odds ratio [OR] = 1.28; 95% CI [1.19–1.38]; p < 0.0001) and higher mortality than those on other AHAs only (OR = 1.09; 95% CI [1.03–1.15]; p = 0.0017). Patients on RAASIs only had lower mortality than those on other AHAs only (OR = 0.87; 95% CI [0.81–0.94]; p = 0.0003). Patients on ACEIs only had higher mortality compared with those on ARBs only (OR = 1.37; 95% CI [1.20–1.56]; p < 0.0001). CONCLUSIONS: Among patients hospitalized for COVID-19 who were taking AHAs, prior use of a combination of RAASIs and other AHAs was associated with higher in-hospital mortality than the use of RAASIs alone. When compared with ARBs, ACEIs were associated with significantly higher mortality in hospitalized COVID-19 patients.

AbstractBackgroundPlatelet transfusion carries risk of transfusion‐transmitted infection (TTI). Pathogen reduction of platelet components (PRPC) is designed to reduce TTI. Pulmonary adverse events (AEs), including transfusion‐related acute lung injury and acute respiratory distress syndrome (ARDS) occur with platelet transfusion.Study designAn open label, sequential cohort study of transfusion‐dependent hematology‐oncology patients was conducted to compare pulmonary safety of PRPC with conventional PC (CPC). The primary outcome was the incidence of treatment‐emergent assisted mechanical ventilation (TEAMV) by non‐inferiority. Secondary outcomes included: time to TEAMV, ARDS, pulmonary AEs, peri‐transfusion AE, hemorrhagic AE, transfusion reactions (TRs), PC and red blood cell (RBC) use, and mortality.ResultsBy modified intent‐to‐treat (mITT), 1068 patients received 5277 PRPC and 1223 patients received 5487 CPC. The cohorts had similar demographics, primary disease, and primary therapy. PRPC were non‐inferior to CPC for TEAMV (treatment difference −1.7%, 95% CI: (−3.3% to −0.1%); odds ratio = 0.53, 95% CI: (0.30, 0.94). The cumulative incidence of TEAMV for PRPC (2.9%) was significantly less than CPC (4.6%, p = .039). The incidence of ARDS was less, but not significantly different, for PRPC (1.0% vs. 1.8%, p = .151; odds ratio = 0.57, 95% CI: (0.27, 1.18). AE, pulmonary AE, and mortality were not different between cohorts. TRs were similar for PRPC and CPC (8.3% vs. 9.7%, p = .256); and allergic TR were significantly less with PRPC (p = .006). PC and RBC use were not increased with PRPC.DiscussionPRPC demonstrated reduced TEAMV with no excess treatment‐related pulmonary morbidity.

Abstract Bortezomib, a proteasome inhibitor (PI), has shown efficacy in the treatment of newly diagnosed and relapsed light chain (AL) amyloidosis, and is often used in combination with cyclophosphamide and dexamethasone. Ixazomib is the first oral PI to be approved in routine practice but has not yet been evaluated in the upfront treatment setting. Newly diagnosed AL amyloidosis patients with measurable disease and adequate organ function were enrolled. The primary objective was to determine the hematologic response rate of ixazomib in combination with cyclophosphamide and dexamethasone. Treatment was given for 12 cycles, followed by ixazomib maintenance until progression. Thirty-five patients were included; their median age was 67 years, and 69% were male. Major organ involvement included heart (66%) and kidneys (54%). A median of 4 induction cycles (range, 1-12) were administered. The overall hematologic response to induction was 63% and included complete response in 11.4% and very good partial response in 37.1% of patients. One patient was upstaged to complete response during maintenance. The most common reason for going off study was the institution of alternate therapy (61%). With a median follow-up of 29.7 months for the living patients, the 2-year progression-free survival and overall survival were 74% and 78%, respectively. The median time to alternate therapy was 7.5 months. Grade ≥3 hematologic and nonhematologic adverse events occurred in 23% and 49% of patients. Given ixazomib’s favorable toxicity profile, which is an important advantage for the typically frail AL population, further evaluation of ixazomib in other combinations in the upfront setting is warranted. This trial was registered at www.clinicaltrials.gov as #NCT01864018.

Abstract Gallbladder cancer (GBC) is one of the deadliest cancers, with a 5-year-survival-rate of less than 5 percent for late-stage disease. The response rate to chemotherapy among GBC patients is generally poor. Recent research has attempted to identify diagnostic, prognostic, and predictive biomarkers, however, currently, no biomarkers can accurately diagnose GBC and predict patients’ prognosis. Integrative analysis of molecular and clinical characterization has not been fully established, and minimal improvement has been made to the survival of these patients, in part due to the heterogeneity of GBC. Machine learning techniques have been proven to empower analysis of big data in oncology, allowing for improvement in the generation of biomarkers to predict patient outcomes. Using machine learning, we can utilize high-throughput RNA sequencing with clinicopathologic data to develop a predictive tool for GBC prognosis. Current predictive models for GBC outcomes often utilize clinical data only, with the highest C-statistic reported being 0.71. C-statistic values over 0.7 generally indicate good models, however 0.8 is the threshold for strong predictive models. We aim to build a superior algorithm to predict overall survival in GBC patients with advanced disease, using machine learning approaches to prioritize biomarkers for GBC prognosis. We have identified over 80 fresh frozen GBC tissue samples from Mayo Clinic Rochester, Dongsan Medical Center in Daegu, Korea, University of the Witwatersrand, in Johannesburg, South Africa, Lithuanian University of Health Science in Vilnius, Lithuania, and University of Calgary in Calgary, Canada, from patients enrolled between 2012 and 2021. We will perform next-generation RNA sequencing on these tissue samples. The patients’ clinical, pathologic and survival data will be abstracted from the medical record uniformly across sites. Feature engineering and dimensionality reduction will be performed. Then random forests, support vector machines, and gradient boosting machines will be applied to train the data. Variable importance will prioritize multi-omic markers. Standard 5-fold cross validation will be used to assess performance of each ML algorithm. If overall survival can be better predicted with the addition patients’ transcriptional sequencing data compared to using clinical profiles alone, we can gain a greater understanding of key biomarkers driving the tumor phenotype. Citation Format: Linsey Jackson, Loretta Allotey, Valles Kenneth, Gavin Oliver, Asha Nair, Daniel O'Brien, Rondell Graham, Mitesh Borad, Arjun Athreya, Lewis Roberts. Prognostic biomarkers for gallbladder cancer: A machine learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1944.

Abstract Chronic lymphocytic leukemia (CLL) is characterized by multiple copy number abnormalities (CNVs) with prognostic value. Identifying these structural variations is central to defining CLL pathogenesis, risk stratification, and therapeutic approaches. Fluorescence in situ hybridization (FISH) is the clinical gold standard in detecting prognostic CNVs in CLL. However, next-generation sequencing (NGS) techniques have become more readily available for clinical genomic applications and can also be used to identify CNVs. Here we present bioinformatic methods to accurately identify CNVs in CLL using NGS data. We used the CNV-calling algorithm PatternCNV to detect clinically relevant CNVs: deletion 17p13 [del(17p)], deletion 11q23 [del(11q)], deletion 13q14 [del(13q)], and trisomy 12. PatternCNV was run on 2274 samples (1500 somatic and 774 germline samples) from six different sequencing batches, screened using a targeted sequencing panel that covers all exons of 59 recurrently CLL mutated genes and additional amplicons covering the minimal affected regions of relevant CNVs. To correct for potential batch effects, PatternCNV was initially run to quantify exon coverage behavior without the chromosomes containing recurrent CNV events, 11, 12, 13, and 17. Principal component analyses and correlation matrices were analyzed, grouping the samples into four distinct clusters that contain similar exon coverage patterns. Samples in each of the four clusters were then independently re-run through PatternCNV using all chromosomes. Visual analysis of CNV plots revealed a bias in normalization. To correct this, the log2ratios were corrected to center the log ratio on the median coverage. Sample noisiness was calculated from the difference in the median absolute deviation (DiffMAD) and samples with a DiffMAD score greater than 0.3 were excluded. All CNV analyses were blinded to clinical FISH results. The effectiveness of our CNV calling was evaluated in 522 CLL patients who had FISH conducted within three months of the sample date. We excluded samples with low tumor metrics identified by FISH (less than 20% of cells with either del(17p), del(11q), trisomy 12 or del(13q)). When we compared our CNV analyses with the FISH data, we found high concordance 99.6% for del(17p), 97.5% for del(11p), 99.1% for trisomy 12, and 93.7% for del(13q). N=46 total discordant pairs were identified, with the highest discordance for del(13q), N=28, followed by del(11q), N=12. These novel bioinformatic methods allow for accurate detection of CNVs across NGS sequencing batches. The high concordance in detecting CNVs between targeted NGS and the gold standard of FISH, suggest NGS is an accurate tool for calling CNVs in CLL. Further, NGS can infer clinically relevant CNVs in genomic locations not targeted by FISH. Citation Format: Chantal E. McCabe, Erik Jessen, Daniel R. O'Brien, Julia E. Wiedmeier-Nutor, Susan L. Slager, Esteban Braggio. Identifying copy number variations in chronic lymphocytic leukemia using targeted next generation sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3352.

Abstract In a cohort of 483 high-risk patients treated with nirmatrelvir/ritonavir for COVID-19, 2 patients (0.4%) required hospitalization by day 30. Four patients (0.8%) experienced rebound of symptoms, which were generally mild, at a median of 9 days after treatment, and all resolved without additional COVID-19–directed therapy.

e21134 Background: Therapies with monoclonal antibodies targeting PD-1 and its ligands are associated with remarkable outcomes and have revolutionized cancer treatment (Honey 2017). However, patients treated with PD-1/PD-L1 blockade may develop “a primary or secondary resistance” to therapy (Sharma, Hu-Lieskovan et al. 2017). Contrary to monoclonal antibodies, chimeric B-cell cancer vaccines have the advantage of producing polyclonal B-cell antibodies that can potentially induce memory B- and T-cell responses, while reducing immune evasion and suppression. The hypothesis is that a polyclonal induced B-cell antibody response will be more effective or as effective with improved safety over current monoclonal antibody therapy. IMU-201 (PD1-Vaxx) is being developed using an active immunization approach to treat cancers that overexpress PD-L1 by inducing the production of anti-PD-1 antibodies with a peptide epitope designed to stimulate polyclonal antibodies against PD-1 (Kaumaya et al. 2020). Methods: The IMPRINTER study is an ongoing open-label dose escalation study of IMU-201 as monotherapy (Phase 1) or in combination with atezolizumab (Phase 1b) for patients with PD-L1 expressing non-small cell lung cancer (NSCLC). All patients enrolled in Phase 1 of the study must have previously received an immune checkpoint inhibitor and experienced disease progression. The primary objective is to evaluate the safety and tolerability of IMU-201 and identify the optimal biological dose (OBD). The secondary objective is to evaluate the efficacy of IMU-201 as monotherapy and in combination with atezolizumab. Humoral and cellular immunogenicity data will be evaluated, including IMU-201 and PD-1 specific antibodies (IgG, IgM), vaccine-specific cytokine levels, and regulatory and effector T and B cells. IMU-201 is administered by intramuscular (IM) injection on Day 1, Day 15, and Day 29. Dose-limiting toxicity (DLT) assessment is completed after 29 days on treatment. Tumor progression is evaluated according to RECIST 1.1 at Day 43 then every 42 days until progression or withdrawal. Results: In Phase 1, four patients were enrolled into each of the three cohorts at 10 μg/dose, 50 μg/dose and 100 μg/dose IMU-201 with no DLTs observed. In the 10 μg/dose cohort, one patient achieved CR and one patient SD; in the 50 μg/dose cohort, four patients achieved SD; and in the 100 μg/dose cohort, one patient achieved PR and two patients achieved SD. Within the 100 μg/dose cohort, one patient experienced an immune related pneumonitis after two IMU-201 administrations and discontinued from study treatment. Conclusions: IMU-201 had no observed DLT and demonstrated preliminary signs of efficacy. The study will therefore move into Phase 1b with IMU-201 being assessed in combination with atezolizumab. Clinical trial information: NCT04432207.