Nathanael R Fillmore1,2,3,4,5, Clark DuMontier5,6,7, Cenk Yildirim1,2,3, Jennifer La1,2,3, Mara M Epstein8, David Cheng9, Diana Cirstea4,5, Sarvari Yellapragada10, Gregory A Abel11, J Michael Gaziano2,3,5,6, Nhan Do1,2,12, Mary Brophy1,2,12, Dae H Kim5,13,14, Nikhil C Munshi3,4,5, Jane A Driver5,6,7. 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
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. Published by Oxford University Press 2021.
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. Published by Oxford University Press 2021.
Authors: Jennifer Schuster Wachen; Seema M Patidar; Elizabeth A Mulligan; Aanand D Naik; Jennifer Moye Journal: Psychooncology Date: 2014-02-11 Impact factor: 3.894
Authors: Monika Engelhardt; Anne-Saskia Domm; Sandra Maria Dold; Gabriele Ihorst; Heike Reinhardt; Alexander Zober; Stefanie Hieke; Corine Baayen; Stefan Jürgen Müller; Hermann Einsele; Pieter Sonneveld; Ola Landgren; Martin Schumacher; Ralph Wäsch Journal: Haematologica Date: 2017-02-02 Impact factor: 9.941