Yusuf, Akeem

Introduction: Lenalidomide is increasingly used for the treatment of newly diagnosed multiple myeloma. However, most patients experience relapse during treatment or after prolonged exposure to lenalidomide, therefore, there is an emerging unmet need for new, highly efficacious lenalidomide-sparing regimens at relapse. CANDOR is an ongoing phase 3 randomized clinical trial (RCT) that compares the lenalidomide-free KdD regimen with carfilzomib 56mg/m2 twice-weekly with dexamethasone (Kd) in patients with relapsed and/or refractory multiple myeloma (RRMM). Based on the primary analysis, KdD was associated with a 37% reduction in the risk of progression or death compared to Kd (HR: 0.63, p=0.001).(Dimopoulos et al. 2020 Lancet) Besides the trial-based comparator Kd, DVd is considered as an additional relevant comparator. DVd is the most similar lenalidomide-free triplet regimen comprising a proteasome inhibitor, daratumumab, and dexamethasone. DVd was assessed in the phase 3 CASTOR RCT and demonstrated superior PFS versus 8 cycles (24 weeks) of bortezomib in combination with dexamethasone (Vd) (HR: 0.31, p<0.001).(Weisel et al. 2019 ASH) In the absence of a direct head-to-head trial comparing these treatments and an assessment suggesting that network meta-analysis is not feasible, matching-adjusted indirect comparisons (MAIC) were used (Signorovitch et al. 2010) to assess the efficacy of KdD relative to DVd. MAIC leverages all available data and compares outcomes across balanced populations. Method: Baseline characteristics and PFS data from the CANDOR and CASTOR trials were used in the analyses. Outcomes were compared in three steps. First, after aligning patient selection criteria of both trials, KdD patients in CANDOR were matched to the average characteristics of DVd patients in CASTOR using matching-adjusted indirect treatment comparisons methodology. The matching variables reflected tumor stage, disease burden, and treatment history, and were generally consistent with validated prognostic markers used in stratifying patients into different risk groups. Second, the published Kaplan-Meier PFS curves for DVd were digitized to generate virtual patient-level data that reconstructed the curves. Third, Cox regression models were fitted to the matched CANDOR data and the reconstructed virtual patient-level CASTOR data to estimate the efficacy of KdD versus DVd. The outcome measure was the hazard ratio (HR) for PFS. Outcomes were also compared for the first 24 weeks and for the period beyond 24 weeks to assess the impact of stopping Vd in DVd after 24 weeks on the HRs. Scenario and subgroup analyses were conducted to assess the robustness of results. Results: KdD patients in the CANDOR trial who were proteasome inhibitor-refractory (n=102) were removed from the data so that the KdD population was aligned with that of DVd in the CASTOR trial. Following the matching procedure, the effective sample size for KdD reduced to 82. In matched populations, PFS at 12 months was 73.1% for KdD and 59.9% for DVd. The MAIC results showed that treatment with KdD decreases the risk of progression or death versus DVd, with an overall HR of 0.61 (95% CI: 0.45-0.82). The time-dependent analysis demonstrated similar PFS for the two treatments for the first 24 weeks and a larger benefit for KdD over DVd after 24 weeks. Similar results were obtained in scenario analyses; in bortezomib-exposed patients the overall HR was 0.46 (95% CI: 0.33-0.64). Conclusions: After adjusting for cross-trial differences, KdD was associated with a significant reduction in the risk of progression or death compared with DVd. Although based on an approach in agreement with methods guide, the results are associated with inherent limitations due to the decreased sample size for KdD, and due to known (longer follow-up for DVd, different duration of proteasome inhibition) or unobserved differences in the trials (potential unobserved heterogeneity in patient characteristics). A comparison of overall survival was not considered due to immature data in CANDOR. The results are in line with those of the head-to-head ENDEAVOR trial that directly compared carfilzomib with bortezomib (HR for PFS for Kd vs Vd 0.53; 95% CI: 0.44-0.65) supporting the clinical validity of this study. The present analysis suggests that KdD improves outcomes compared with DVd in patients with RRMM and may provide a rationale for a preferential treatment. Disclosures Weisel: Roche: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria; GlaxoSmithKline: Honoraria; Adaptive: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Nooka:Sanofi: Consultancy, Honoraria; Spectrum Pharmaceuticals: Consultancy; Oncopeptides: Consultancy, Honoraria; Karyopharm Therapeutics, Adaptive technologies: Consultancy, Honoraria, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Other: Personal Fees: Travel/accomodations/expenses, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Adaptive Technologies: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding. Terpos:Janssen: Honoraria, Research Funding; Genesis pharma SA: Honoraria, Other: travel expenses , Research Funding; Amgen: Honoraria, Research Funding; Takeda: Honoraria, Other: travel expenses , Research Funding; Celgene: Honoraria; Sanofi: Honoraria; BMS: Honoraria. Spencer:AbbVie, Amgen, Celgene, Haemalogix, Janssen, Sanofi, SecuraBio, Specialised Therapeutics Australia, Servier and Takeda: Honoraria; AbbVie, Celgene, Haemalogix, Janssen, Sanofi, SecuraBio, Specialised Therapeutics Australia, Servier and Takeda: Consultancy; Amgen, Celgene, Haemalogix, Janssen, Servier and Takeda: Research Funding; Celgene, Janssen and Takeda: Speakers Bureau. Goldschmidt:Merck Sharp and Dohme (MSD): Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; Johns Hopkins University: Other: Grants and/or provision of Investigational Medicinal Product; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Dietmar-Hopp-Foundation: Other: Grants and/or provision of Investigational Medicinal Product:; Molecular Partners: Research Funding; Novartis: Honoraria, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Chugai: Honoraria, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Grants and/or provision of Investigational Medicinal Product:, Research Funding; University Hospital Heidelberg, Internal Medicine V and National Center for Tumor Diseases (NCT), Heidelberg, Germany: Current Employment; GlaxoSmithKline (GSK): Honoraria; Incyte: Research Funding; Mundipharma GmbH: Research Funding. Majer:Amgen (Europe) GmbH: Current Employment, Current equity holder in publicly-traded company. DeCosta:Amgen Ltd: Current Employment, Current equity holder in publicly-traded company. Medhekar:Amgen Inc.: Current Employment, Current equity holder in publicly-traded company. Yusuf:Amgen Inc.: Current Employment, Current equity holder in publicly-traded company. Kumar:Karyopharm: Consultancy; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Novartis: Research Funding; Sanofi: Research Funding; Carsgen: Other, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Dr. Reddy's Laboratories: Honoraria; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; BMS: Consultancy, Research Funding; Kite Pharma: Consultancy, Research Funding; Adaptive Biotechnologies: Consultancy; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Cellectar: Other; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genecentrix: Consultancy; MedImmune: Research Funding; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Tenebio: Other, Research Funding.

AbstractThe purpose of this analysis was to develop and validate computable phenotypes for heart failure (HF) with preserved ejection fraction (HFpEF) using claims‐type measures using the Rochester Epidemiology Project. This retrospective study utilized an existing cohort of Olmsted County, Minnesota residents aged ≥ 20 years diagnosed with HF between 2007 and 2015. The gold standard definition of HFpEF included meeting the validated Framingham criteria for HF and having an LVEF ≥ 50%. Computable phenotypes of claims‐type data elements (including ICD‐9/ICD‐10 diagnostic codes and lab test codes) both individually and in combinations were assessed via sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with respect to the gold standard. In the Framingham‐validated cohort, 2,035 patients had HF; 1,172 (58%) had HFpEF. One in‐patient or two out‐patient diagnosis codes of ICD‐9 428.3X or ICD‐10 I50.3X had 46% sensitivity, 88% specificity, 84% PPV, and 54% NPV. The addition of a BNP/NT‐proBNP test code reduced sensitivity to 35% while increasing specificity to 91% (PPV = 84%, NPV = 51%). Broadening the diagnostic codes to ICD‐9 428.0, 428.3X, and 428.9/ICD‐10 I50.3X and I50.9 increased sensitivity at the expense of decreasing specificity (diagnostic code‐only model: 87% sensitivity, 8% specificity, 56% PPV, 30% NPV; diagnostic code and BNP lab code model: 61% sensitivity, 43% specificity, 60% PPV, 45% NPV). In an analysis conducted to mimic real‐world use of the computable phenotypes, any one in‐patient or out‐patient code of ICD‐9 428/ICD‐10 150 among the broader population (N = 3,755) resulted in lower PPV values compared with the Framingham cohort. However, one in‐patient or two out‐patient instances of ICD‐9 428.0, 428.9, or 428.3X/ICD‐10 150.3X or 150.9 brought the PPV values from the two cohorts closer together. While some misclassification remains, the computable phenotypes defined here may be used in claims databases to identify HFpEF patients and to gain a greater understanding of the characteristics of patients with HFpEF.