scholarly journals CARDIOVASCULAR OUTCOMES MEASURED BY ADJUDICATION VERSUS BILLING CODES IN REAL-WORLD PATIENTS TREATED WITH IMMUNE CHECKPOINT INHIBITORS

2021 ◽  
Vol 77 (18) ◽  
pp. 3321
Author(s):  
Lavanya Kondapalli ◽  
Jessica Thurston ◽  
Judith Hsia ◽  
Ronni Miller ◽  
Rita Dale ◽  
...  
Keyword(s):  
Real World ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Cardiovascular Outcomes

scholarly journals A real‐world data of Immune checkpoint inhibitors in solid tumors from India

2021 ◽  
Author(s):  
Vanita Noronha ◽  
George Abraham ◽  
Vijay Patil ◽  
Amit Joshi ◽  
Nandini Menon ◽  
...  
Keyword(s):  
Solid Tumors ◽  
Real World ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Real World Data ◽  
World Data

284 Real World Data of Sequencing Immune Checkpoint Inhibitors (ICI) after Initial ICI

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A310-A310
Author(s):  
Krishna Gunturu ◽  
Muhammad Awidi ◽  
Rojer Ranjit ◽  
Brendan Connell ◽  
Rachel Carrasquillo ◽  
...  
Keyword(s):  
Cell Death ◽  
Programmed Cell Death ◽  
Real World ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Medical Center ◽  
Clear Understanding ◽  
Real World Data ◽  
World Data

BackgroundICI revolutionized modern Oncology landscape and being utilized in metastatic to adjuvant and neo-adjuvant settings. As Oncologists, we are treating cancer patients with ICI every day, yet there is still a lot that is unknown about these drugs. We don’t have clear understanding of the efficacy and toxicity when sequencing one ICI for another. We conducted a retrospective review of real world data at Lahey Hospital and Medical Center to understand further and to pave path for prospective studies to understand this issue further to improve patient care.MethodsWe retrospectively reviewed Oncology patient charts who received ICI between January1, 2014 to December 18, 2018. Total 483 patients received ICI during this time frame and 22 of these patients received a second ICI either as monotherapy or in combination with other ICI or chemotherapy.ResultsA total of 22 patients received subsequent ICI after the initial ICI as showed in table 1. 15 of the 22 (68%) patients were transitioned from one ICI to another monotherapy. 11 of these patients were transitioned secondary to disease progression (73%), three had immune related adverse events and one was switched per standard of care. One patient had ICI re-challenge. Three patients had a transition from ICI monotherapy to combination ICI therapy. One patient went onto chemo-immunotherapy and 2 patients transitioned from combination ICI to chemo-immunotherapy.Abstract 284 Table 1Real world data of sequencing immune checkpoint inhibitors (ICI) after initial ICIConclusionsICI therapy is evolving and patients are being treated with multiple lines of ICI. In current practices, ICI is frequently being transitioned from cytotoxic T-lymphocyte antigen 4 (CTLA-4) and programmed cell death 1 (PD-1) or its ligand, programmed cell death ligand 1 (PD-L1) classes or combined with chemotherapy or targeted therapy. It would be prudent to explore the effects of sequencing these medications either as a monotherapy or in combination with other therapies to better serve our patients and to prevent financial toxicity.

Large scale adverse event data mining for targeted therapies development.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 2538-2538
Author(s):  
Mayur Sarangdhar ◽  
Bruce Aronow ◽  
Anil Goud Jegga ◽  
Brian Turpin ◽  
Erin Haag Breese ◽  
...  
Keyword(s):  
Data Mining ◽  
Clinical Trials ◽  
Adverse Event ◽  
Big Data ◽  
Real World ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Large Scale ◽  
Checkpoint Inhibitors ◽  
Anaplastic Lymphoma

2538 Background: Targeted anti-cancer small molecule drugs & immune therapies have had a dramatic impact in improving outcomes & the approach to clinical trials. Increasingly, regulatory approvals are expedited with small studies designed to identify strong efficacy signals. However, this may limit the extent of safety profiling. The use of large scale/big data meta-analyses can identify novel safety & efficacy signals in "real-world" medical settings. Methods: We used AERSMine, an open-source data mining platform to identify drug toxicity signatures in the FDA’s Adverse Event Reporting System of 8.6 million patients. We identified patients (n = 732,198) who received either traditional and targeted cancer therapy & identified therapy-specific toxicity patterns. Patients were classified based on exposures: anthracyclines (n = 83,179), platinum (117,993), antimetabolites (93,062), alkylators (81,507), antimicrotubule agents (97,726), HER2 inhibitors (40,040), VEGFis (79,144), VEGF-TKis (90,734), multi TKis (34,457), anaplastic lymphoma Kis (7,635), PI3K-AKT-mTOR inhibitors (33,864), Bruton TKis (9,247), MEKis (4,018), immunomodulatory agents (174,810), proteasome inhibitors (44,681), immune checkpoint inhibitors (20,287). Pharmacovigilance metrics [Relative Risks & safety signals] were used to establish statistical correlation & toxicity signatures were differentiated using the Kolmogorov–Smirnov test. Results: To validate the use of the AERSMine to detect AEs, we focused on cardiotoxicity. It identified classic drug associated AEs (e.g. ventricular dysfunction with anthracyclines, HER2is & VEGFis; VEGFi hypertension & vascular toxicity; multi TKIs vascular events). AERSMine also identified recently reported uncommon toxicities of myositis/myocarditis with immune checkpoint inhibitors. It indicated a higher frequency of myositis/myocarditis with combination immune checkpoint therapy, paralleling industry corporate safety databases. These toxicities were reported at higher frequencies in patients > 65 yrs. Conclusions: AERSMine “big data” analyses provide a sensitive tool to detect potential new patterns of AEs simultaneously across multiple clinical trials & in the real-world setting.

Risk factors for myocarditis associated with immune checkpoint inhibitors using real-world clinical data.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15100-e15100 ◽  
Author(s):  
Prantesh Jain ◽  
Jahir Gutierrez Bugarin ◽  
Avirup Guha ◽  
Chhavi Jain ◽  
Tingke Shen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Liver Disease ◽  
Cancer Patients ◽  
Real World ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Cox Proportional Hazards ◽  
Cancer Type ◽  
Real World Data

e15100 Background: Immune checkpoint inhibitors (ICIs) can cause unique, high-grade immune-related adverse events. Although rare, ICI related myocarditis has the highest fatality rate (~50%). Cardiovascular monitoring is not routinely performed in patients on ICI treatment, thus risk factors remain unknown. Characterizing rare but fatal cardiac toxicities requires integration of real-world data. Methods: U.S claims data (IBM MarketScan) of over 30 million commercially insured individuals was leveraged to identify 2,687,301 cancer patients between 2011-2018. Patients ≥18 years of age treated with ICIs (targeting CTLA4 (ipilimumab) and/or the PD1 (nivolumab, pembrolizumab)/PDL1 (atezolizumab, avelumab, durvalumab) alone or in combination with ICI and/or chemotherapy were identified and followed until disenrollment. Myocarditis, comorbidities, and treatment details were identified using diagnosis and billing codes. Analyses included descriptive statistics and Cox proportional hazards regression. Results: 16,541 ICI treated cancer patients were included (median age 60; 58% male). Myocarditis was identified in 252 (1.5%) patients, majority (90%) ≥50 years old (median 63) with 12,040 person-years of follow up. 62% received anti-PD1 monotherapy, 12% anti-CTLA4, and 15% received combination treatment with other ICIs and/or chemotherapy. Most common cancer types were lung (48%), melanoma (25%), and renal cancer (14%). Cumulative incidence of myocarditis at 1 year was 2.06%; 95% CI (1.78-2.37), median onset of 80.5 days, 42% occurring within 60 days of treatment. By univariate analyses, age, cancer type, diabetes (DM), hypertension (HTN), kidney, liver disease, atrial fibrillation (AF) were related to myocarditis. Risk was lower in patients who received anti-CTLA4 monotherapy (HR: 0.490; 95% CI: 0.26-0.92; p = 0.0251). On multivariable regression analyses only age, cancer type (renal, lung cancer), comorbidities DM and liver disease were significantly associated with myocarditis (Table). Conclusions: This is the largest real-world longitudinal study for ICI associated myocarditis showing higher than reported incidence and identifiable risk factors. [Table: see text]

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