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620 Predictors of Tyrosine Kinase Inhibitor Use Among Older Patients with Chronic Myeloid Leukemia in the United States

Program: Oral and Poster Abstracts
Type: Oral
Session: 903. Health Services Research—Malignant Conditions (Myeloid Disease): Treatment and Publication Patterns in Myeloid Malignancies
Hematology Disease Topics & Pathways:
Non-Biological, Diseases, CML, Therapies, Elderly, chemotherapy, Study Population, Myeloid Malignancies
Monday, December 7, 2020: 9:00 AM

Rong Wang, PhD1*, Rory M. Shallis, MD2, Jan Philipp Bewersdorf, MD3*, Amer M. Zeidan, MBBS, MHS4, Scott F. Huntington, MD, MPH5, Xiaomei Ma, PhD6* and Nikolai A. Podoltsev, MD, PhD7

1Yale University School of Public Health, New Haven, CT
2Yale Cancer Center, West Haven, CT
3Department of Internal Medicine, Section of Hematology, Yale University School of Medicine, New Haven, CT
4Yale University School of Medicine and Yale Cancer Center, New Haven, CT
5Hematology, Yale University School of Medicine, New Haven, CT
6Department of Chronic Disease Epidemiology, Yale School of Public Health, Yale University, New Haven, CT
7Department of Internal Medicine, Section of Hematology, Yale University School of Medicine and Yale Cancer Center, Madison, CT

Introduction: About half of all patients (pts) diagnosed with chronic myeloid leukemia (CML) are >65 years (yrs) old. Pts treated with a BCR-ABL1-directed tyrosine kinase inhibitor (TKI) are found to have life expectancy near that of the general population. Despite this, some CML pts never receive a TKI. Some providers may consider older age, decline in fitness, increased comorbidity burden and higher risk of treatment complications as precluding factors, but there are very limited real-world data on the characteristics of CML pts not receiving TKI therapy.

Methods: Using the Surveillance, Epidemiology, and End Results-Medicare linked database, we assembled a population-based cohort of older adults diagnosed with CML during 2007-2015 who: 1) were age 66-99 yrs at diagnosis, 2) had continuous Medicare Parts A/B and D coverage from 1 year and 3 months, respectively, before diagnosis to end of follow-up (change in Medicare status, death, or 12/31/2016, whichever came first) and 3) did not receive a TKI within 3 months before diagnosis. Pts were categorized as TKI users (imatinib, bosutinib, dasatinib, nilotinib, ponatinib) or non-users from CML diagnosis to end of follow-up. Comorbidities were identified using ICD-9/10 codes. Pearson’s Chi-Square tests were used to compare patient characteristics in a bivariate manner, and multivariable logistic regression was used to assess factors associated with TKI use. All statistical tests were two-sided and conducted with SAS (Version 9.4).

Results: The cohort included 1251 pts with a median age of 77 (interquartile range [IQR]: 71-83) yrs; 51% were female and 87% were white. During the median follow-up of 2.17 (IQR: 0.74-4.13) yrs, 828 pts (66.2%) received a TKI. Median overall survival was 6.6 yrs and 0.6 yrs among TKI users and non-users, respectively (log rank test p<.01, Figure 1a). Among 545 pts with known cause of death, the main causes of death were similar in both groups: CML (26.3% in TKI users, 24.3% in non-users, p=0.59) and heart disease (19.1% in TKI users, 15.9% in non-users, p=0.33).

Fewer TKI users had underlying cardiovascular diseases (26.1% vs. 33.6%; p<0.01), heart failure (13.5% vs. 19.6%; p<.01) and pulmonary diseases (2.5% vs. 5.2%; p=0.01) than those without TKI. Pts with baseline hyperlipidemia were more likely to receive a TKI (59.7% vs. 45.9%; p<0.01). However, in multivariable analysis (Figure 2), cardiovascular diseases, cardiovascular disease risk factors, and pulmonary diseases were not significantly associated with receipt of TKI therapy.

Compared with pts aged 66-69 yrs, those aged 70-74 (odds ratio [OR]=0.61, 95% confidence interval [CI]: 0.39-0.97; p=0.04), 75-79 (OR=0.51, 95% CI: 0.32-0.79; p<.01), 80-84 yrs (OR=0.29, 95% CI: 0.18-0.45; p<.01) and ≥85 yrs (OR=0.16, 95% CI: 0.10-0.25; p<.01) were less likely to receive a TKI (Figure 2). Being a male or unmarried was also associated with decreased likelihood of TKI treatment (OR=0.76, 95% CI: 0.57-0.99; p=0.04 and OR=0.75, 95% CI: 0.56-1.00; p=0.05, respectively), while increased number of comorbidities had a similar but borderline association (modified Elixhauser comorbidity index 2+ vs. 0, OR=0.72, 95% CI: 0.52-1.02; p=0.06). The likelihood of TKI use increased with year of diagnosis (2007-2009: reference; 2010-2012: OR=1.72, 95% CI: 1.24-2.40; p<.01 and 2013-2015: OR=2.04, 95% CI: 1.49-2.81; p<.01). Receipients of an influenza vaccine within 12 months prior to diagnosis (an indicator of healthcare access) were more likely to receive a TKI (OR=1.63, 95% CI: 1.25-2.13; p<.01). Race, previous cancer, income and geogrpahic region did not appear to be associated with TKI use.

We also conducted an analysis including only pts surviving >180 days after CML diagnosis. While the percentage of TKI non-used decreased from 33.8% in the overall cohort to 23.5% in the subset, TKI use was still associated with better survival (Figure 1b) and the predictors for TKI use were very similar.

Conclusions: We report one of the largest analyses of older CML pts demonstrating that one-third did not receive a TKI. Despite multiple TKIs available with differing toxicity profiles and the option to dose-reduce, increased age and male gender were associated with non-receipt of a TKI. Pts diagnosed in more recent years or with better access to healthcare were more likely to receive TKIs. These data highlight a potential area for improvement as a trial of TKI therapy should be strongly considered for most CML pts.

Disclosures: Wang: Celgene/BMS: Research Funding. Zeidan: Otsuka: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria, Research Funding; Jazz: Consultancy, Honoraria; Celgene / BMS: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Ionis: Consultancy, Honoraria; Aprea: Research Funding; Astellas: Consultancy, Honoraria; Acceleron: Consultancy, Honoraria; Taiho: Consultancy, Honoraria; Boehringer-Ingelheim: Consultancy, Honoraria, Research Funding; Daiichi Sankyo: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding; Cardiff Oncology: Consultancy, Honoraria, Other; BeyondSpring: Consultancy, Honoraria; Cardinal Health: Consultancy, Honoraria; Incyte: Consultancy, Honoraria, Research Funding; Trovagene: Consultancy, Honoraria, Research Funding; MedImmune/Astrazeneca: Research Funding; Astex: Research Funding; CCITLA: Other; Leukemia and Lymphoma Society: Other; Epizyme: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding; Agios: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; ADC Therapeutics: Research Funding. Huntington: Genentech: Consultancy; AbbVie: Consultancy; Astrazeneca: Honoraria; Bayer: Consultancy, Honoraria; DTRM: Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy; Pharmacyclics: Honoraria; TG Therapeutics: Research Funding; Flatiron Health: Consultancy; BeiGene: Consultancy. Ma: Bristol-Myers Squibb: Consultancy, Research Funding; Celgene: Research Funding. Podoltsev: Genentech: Research Funding; Astex Pharmaceuticals: Research Funding; CTI biopharma: Consultancy, Honoraria, Research Funding; Agios Pharmaceuticals: Consultancy, Honoraria; Blueprint Medicines: Consultancy, Honoraria; Daiichi Sankyo: Research Funding; Sunesis Pharmaceuticals: Research Funding; Jazz Pharmaceuticals: Research Funding; Alexion: Consultancy, Honoraria; AI Therapeutics: Research Funding; Samus Therapeutics: Research Funding; Bristol-Myers Squib: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Boehringer Ingelheim: Research Funding; Astellas Pharma: Research Funding; Kartos Therapeutics: Research Funding; Novartis: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; Arog Pharmaceuticals: Research Funding.

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