Defining Multimorbidity and Its Impact in Older United States Veterans Newly Treated for Multiple Myeloma

Author:

Fillmore Nathanael R12345ORCID,DuMontier Clark567ORCID,Yildirim Cenk123,La Jennifer123,Epstein Mara M8,Cheng David9,Cirstea Diana45ORCID,Yellapragada Sarvari10,Abel Gregory A11ORCID,Gaziano J Michael2356,Do Nhan1212,Brophy Mary1212,Kim Dae H51314ORCID,Munshi Nikhil C345,Driver Jane A567

Affiliation:

1. VA Boston CSP Center, Boston, MA, USA

2. Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Boston, MA, USA

3. VA Boston Healthcare System, Boston, MA, USA

4. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

5. Harvard Medical School, Boston, MA, USA

6. Division of Aging, Brigham and Women’s Hospital, Boston, MA, USA

7. New England GRECC (Geriatrics Research, Education and Clinical Center), VA Boston Healthcare System, Boston, MA, USA

8. The Meyers Primary Care Institute and the Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA

9. Massachusetts General Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

10. Michael E Debakey VA Medical Center and Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA

11. Divisions of Hematologic Malignancy and Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA

12. Boston University School of Medicine, Boston, MA, USA

13. Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA

14. Division of Gerontology, Beth Israel Deaconess Medical Center, Boston, MA, USA

Abstract

Abstract Background  Traditional count-based measures of comorbidity are unlikely to capture the complexity of multiple chronic conditions (multimorbidity) in older adults with cancer. We aimed to define patterns of multimorbidity and their impact in older United States veterans with multiple myeloma (MM). Methods  We measured 66 chronic conditions in 5076 veterans aged 65 years and older newly treated for MM in the national Veterans Affairs health-care system from 2004 to 2017. Latent class analysis was used to identify patterns of multimorbidity among these conditions. These patterns were then assessed for their association with overall survival, our primary outcome. Secondary outcomes included emergency department visits and hospitalizations. Results Five patterns of multimorbidity emerged from the latent class analysis, and survival varied across these patterns (log-rank 2-sided P < .001). Older veterans with cardiovascular and metabolic disease (30.9%, hazard ratio [HR] = 1.33, 95% confidence interval [CI] = 1.21 to 1.45), psychiatric and substance use disorders (9.7%, HR = 1.58, 95% CI = 1.39 to 1.79), chronic lung disease (15.9%, HR = 1.69, 95% CI = 1.53 to 1.87), and multisystem impairment (13.8%, HR = 2.25, 95% CI = 2.03 to 2.50) had higher mortality compared with veterans with minimal comorbidity (29.7%, reference). Associations with mortality were maintained after adjustment for sociodemographic variables, measures of disease risk, and the count-based Charlson Comorbidity Index. Multimorbidity patterns were also associated with emergency department visits and hospitalizations. Conclusions Our findings demonstrate the need to move beyond count-based measures of comorbidity and consider cancer in the context of multiple chronic conditions.

Funder

VA Office of Research and Development

Cooperative Studies Program

Harvard Translational Research in Aging Training Program

National Institute on Aging

National Institutes of Health

VA Merit Review Award

NIH

Department of Veterans Affairs

VA Health Services Research and Development Service

VA Information Resource Center

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Oncology

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