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622 Treatment Facility Volume and Outcomes in Waldenstrom MacroglobulinemiaClinically Relevant Abstract

Program: Oral and Poster Abstracts
Type: Oral
Session: 904. Outcomes Research—Malignant Conditions: Outcomes in Lymphoid Malignancies and Stem Cell Transplant
Hematology Disease Topics & Pathways:
B-Cell Lymphoma, Clinically relevant
Monday, December 3, 2018: 7:45 AM
Room 24B (San Diego Convention Center)

Jonas Paludo, MD1, Jithma Prasad Abeykoon, MD1, Stephen M. Ansell, MD, PhD1, Morie A. Gertz, MD1, Prashant Kapoor, MD1, Aneel Paulus, MD2, Sikander Ailawadhi2, Craig B. Reeder, MD3, Thomas E. Witzig, MD1, Thomas M. Habermann, MD1, Anne J Novak, PhD1, Francis K. Buadi, MB, CHB1*, Angela Dispenzieri, MD1, Yi Lin, MD, PhD1, Martha Q. Lacy, MD1, Shaji K. Kumar, MD1, S. Vincent Rajkumar, MD1, Robert A. Kyle, MD1 and Ronald S. Go, MD1

1Division of Hematology, Mayo Clinic, Rochester, MN
2Division of Hematology-Oncology, Mayo Clinic, Jacksonville, FL
3Division of Hematology and Oncology, Mayo Clinic, Scottsdale, AZ

Introduction

Waldenstrom macroglobulinemia (WM) is a rare disease accounting for only 1-2% of all hematologic malignancies and with an overall annual, age-adjusted incidence of approximately 3.5 to 5.5 cases per million-person years. With approximately 13,000 active hematologist/oncologists in the US, most of who are in the community oncology setting, it’s estimated that a hematologist/oncologist will only diagnose a WM case approximately every 8 years. Therefore, the clinical experience of a general hematologist/oncologist in the management of WM is likely very limited compared to more prevalent malignances. Previous studies in the setting of rare cancers suggest a correlation between higher volume of care and improved outcomes. Therefore, we explored the volume-outcome relationship in WM using the National Cancer Data Base (NCDB).

Methods

Patient-level data from the NCDB, a nationwide, facility-based, database covering approximately 70% of all newly diagnosed cancer cases in the US, was queried for all new WM cases diagnosed between 2004 and 2014. Only patients requiring treatment were included. Treatment facilities were divided into quartiles based on the average annual volume of newly diagnosed cases of WM seen. Cox regression was used to analyze the association between facility WM volume and survival, adjusted by demographics (sex, age, race), socioeconomic status (income, education, insurance type), geography (area of residence, treatment facility type, travel distance), comorbidity factors (Charlson-Deyo score), and year of diagnosis. Time-to-event analysis was calculated from frontline therapy initiation date using the Kaplan-Meier method and the log-rank test.

Results

A total of 3,732 patients with WM treated in 831 facilities were included. The median age at diagnosis was 70 years and 75% of the patients were treated within 20 miles from their residency zip code. Patient characteristics per treatment facility volume quartile are shown in table 1. The median annual facility volume was 1 new WM patient/year (range 0.1 to 21). The median follow-up from frontline treatment was 5 years.

The unadjusted median OS by facility volume was: Q1: 6.5 years (95% CI: 5.7-7.4), Q2: 7 years (95% CI: 6.3-8.2), Q3: 8.2 years (95% CI: 7.1-8.9), and Q4: NR (95% CI: 8.5-NR), p<0.0001 (figure 1). The estimated 5-year OS by facility volume was: Q1: 56%, Q2: 61%, Q3: 64%, Q4: 71%, p<0.0001.

Multivariate analysis including all variables showed in Table 1 demonstrated that facility volume was independently associated with all-cause mortality. Compared to patients treated at Q4 facilities, patients treated at lower-quartiles facilities had a higher incremental risk of death (Q3 hazard ratio [HR], 1.07 [95% CI: 0.88 to 1.29] p=0.46; Q2 HR, 1.34 [95% CI: 1.11 to 1.63] p=0.002; Q1 HR, 1.52 [95% CI: 1.23 to 1.88] p=<0.0001).

Conclusion

Our results suggest that a volume-outcome relationship exits in WM as patients treated initially at higher-volume facilities had a lower risk of mortality. Although differences in the underlying disease biology, referral patterns after initial therapy, or cumulative treating-physician experience could not be assessed, these potential biases would only underestimate the magnitude of the volume-outcome relationship reported.

Disclosures: Ansell: Celldex: Research Funding; Pfizer: Research Funding; Merck & Co: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Takeda: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding. Gertz: Medscape: Consultancy; janssen: Consultancy; Alnylam: Honoraria; Ionis: Honoraria; Physicians Education Resource: Consultancy; Prothena: Honoraria; Teva: Consultancy; Research to Practice: Consultancy; spectrum: Consultancy, Honoraria; celgene: Consultancy; Amgen: Consultancy; Abbvie: Consultancy; annexon: Consultancy; Apellis: Consultancy. Kapoor: Takeda: Research Funding; Celgene: Research Funding. Ailawadhi: Amgen: Consultancy; Janssen: Consultancy; Pharmacyclics: Research Funding; Celgene: Consultancy; Takeda: Consultancy. Reeder: Affimed: Research Funding. Witzig: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Dispenzieri: Celgene, Takeda, Prothena, Jannsen, Pfizer, Alnylam, GSK: Research Funding. Lacy: Celgene: Research Funding. Kumar: KITE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding.

*signifies non-member of ASH