Fellow University of North Carolina Chapel Hill Durham, North Carolina, United States
Poster Abstract:
Background: Survival for patients with CLL/SLL has improved over the past decade, largely due to novel oral anticancer agents (OAAs). Investigations utilizing population-based sources such as the Surveillance, Epidemiology, and End Results (SEER) Program indicate much of that benefit has occurred primarily in younger populations, with much more modest, or no improvement in older patient groups. The use of OACs in older adults is complicated by clinical and financial toxicities potentially compromising medication adherence and clinical outcomes. In clinical trials, patients with dose intensity (DI) >95% experienced longer median progression-free survival (PFS) compared with those with lower DI. This study utilizes data from the SEER-Medicare linkage to characterize adherence and persistence to ibrutinib among older adults with CLL/SLL.
Methods: We utilized the most recent years of data available from the SEER-Medicare linkage to describe ibrutinib adherence (using proportion of days covered, PDC) and persistence (using days until discontinuation) and identify patient-level predictors of “high” adherence (>95% PDC) during the study period: 2013-2017 (using Medicare claims from 2013-2019).
Patients with any ibrutinib claim were identified from a cohort of patients with primary diagnosis of CLL ≥66 years of age at diagnosis with continuous enrollment in both Medicare Parts A and B 12 months prior to diagnosis. To measure adherence and persistence to ibrutinib, we assigned initiation of ibrutinib as the date of the patient’s first record indicating an ibrutinib administration or prescription fill. Days supply and dosage information from prescription claims were used to construct a chart with daily entries for whether the patient had ibrutinib on-hand. To calculate 2-year PDC, the denominator was number of days during follow-up; the numerator was number of days the patient had ibrutinib on-hand. Persistence was calculated by patient’s time to first 30-day period of non-persistence to ibrutinib. Time to discontinuation was 30 consecutive days without ibrutinib on hand. We examined demographic and clinical factors associated with mortality among cohort including comorbidities, year of diagnosis, race/ethnicity, socioeconomic variables, geographic region, age, and sex.
Anticipated
Results: The number of patients receiving ibrutinib having a diagnosis of CLL/ SLL and the demographic makeup of the cohort will be reported. The percentage of patients with optimal adherence rates for the cohort (PDC>95%) and mean duration of therapy will be reported. Significant predictors of non-adherence while on treatment will be reported. Factors influencing persistence will be reported.
Conclusions: Anticipated.
References (must also be included in final poster): 1. Rai KR, Jain P. Chronic lymphocytic leukemia (CLL)-Then and now. Am J Hematol. 2016 Mar;91(3):330-40.
2. Surveillance, Epidemiology, and End Results (SEER) Program Populations (1969-2020) (www.seer.cancer.gov/popdata), National Cancer Institute, DCCPS, Surveillance Research Program, released February 2022.
3. McBean AM, Warren JL, Babish JD. Measuring the incidence of cancer in elderly Americans using Medicare claims data. Cancer. 1994 May 1;73(9):2417-25.
4. Huang SJ, Gerrie AS, Young S, et al. Impact of age and treatment institution type on outcomes of patients treated for chronic lymphocytic leukemia in British Columbia, Canada. Leuk Res. 2021 Apr;103:106538.
5. Muluneh B, Deal A, Alexander MD, et al. Patient perspectives on the barriers associated with medication adherence to oral chemotherapy. J Oncol Pharm Pract. 2018 Mar;24(2):98-109.
6. Barr PM, Brown JR, Hillmen P, et al. Impact of ibrutinib dose adherence on therapeutic efficacy in patients with previously treated CLL/SLL. Blood. 2017; 129(19):2612-2615.
7. Collins J 4th, Stump SE, Heiling H, et al. Impact of adherence to ibrutinib on clinical outcomes in real-world patients with chronic lymphocytic leukemia. Leuk Lymphoma. 2022 Mar 6:1-8.
8. Wierda WG, Brown J, Abramson JS, et al. NCCN guidelines insights: chronic lymphocytic leukemia/small lymphocytic leukemia, version 3.2022. J Natl Compr Canc Netw. 2022;18(2):185-217.
9. Richardson DB, Kinlaw AC, Keil APAP, Naimi AI, Kaufman JS, Cole SR. Inverse Probability Weights for the Analysis of Polytomous Outcomes. American Journal of Epidemiology. 2018;187(5):1125-1127
10. Yost K, Perkins C, Cohen R, Morris C, Wright W. Socioeconomic status and breast cancer incidence in California for different race/ethnic groups. Cancer Causes Control. 2001;12(8):703-711.
11. Hargrove JL, Pate V, Casteel CH, et al. Antihypertensive Adherence Trajectories Among Older Adults in the First Year After Initiation of Therapy. American Journal of Hypertension 2017;30:1015-23.
12. Schneeweiss S, Rassen JA, Brown JS, et al. Graphical Depiction of Longitudinal Study Designs in Health Care Databases. Annals of Internal Medicine 2019;170:398-406.
13. Matthews A, Herrett E, Gasparrini A, et al. Impact of statin related media coverage on use of statins: interrupted time series analysis with UK primary care data. BMJ 2016;353:i3283.
14. C.N. Klabunde, J.M. Legler, J.L. Warren, et al. A refined comorbidity measurement algorithm for claims-based studies of breast, prostate, colorectal, and lung cancer patients. Ann Epidemiol, 17 (2007), pp. 584-590
15. Mehta HB, Sura SD, Adhikari D, et al. Adapting the Elixhauser comorbidity index for cancer patients. Cancer. 2018 May 1;124(9):2018-2025.
16. van Walraven C, Austin PC, Jennings A, et al. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care. 2009;47:626–633.
17. Faurot KR, Jonsson Funk M, Pate V, Brookhart MA, Patrick A, Hanson LC, Castillo WC, Sturmer T. Using claims data to predict dependency in activities of daily living as a proxy for frailty. Pharmacoepidemiol Drug Saf. 2015;24(1):59–66.