{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T07:54:09Z","timestamp":1790409249515,"version":"4.1.0"},"reference-count":120,"publisher":"American Society of Hematology","issue":"1","content-domain":{"domain":["ashpublications.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,12,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>As the aging population grows, so too does the number of well-tolerated antimyeloma therapies. Physicians will see an increasing volume of patients for subsequent lines of therapy, which could now extend this relationship for over a decade. For younger patients, treatment choices are infrequently impacted by concerns of fitness, but instead about effecting the deepest, most durable response. Older adults, in contrast, are more likely to experience under- than overtreatment, and therefore more objective (and ideally straightforward) ways to evaluate their fitness and ability to tolerate therapy will increasingly assist in decision-making. Post hoc analyses categorizing the fitness of trial patients in the modern treatment era globally demonstrate that even in highly selected populations, those that are recategorized as less fit or frail are consistently at higher risk of inferior outcomes and increased toxicities. Real-world data are comparatively lacking but do demonstrate that most patients with myeloma are not representative of those enrolled on clinical trials, generally more heavily burdened by comorbidities and more likely to be categorized as \u201cless than fit.\u201d Simultaneously, the number of therapeutic options open to patients in the relapsed setting continues to grow, now including T-cell engagers and cellular therapies, with their unique toxicity profiles. The aim of this review is to summarize the available data, highlight some of the approaches possible to easily assess fitness and how results might inform treatment selection, and illustrate ways that patients' condition can be optimized rather than lead to exclusion from the more complex therapies newly available.<\/jats:p>","DOI":"10.1182\/hematology.2022000346","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T16:10:33Z","timestamp":1670602233000},"page":"337-348","update-policy":"https:\/\/doi.org\/10.1182\/blood.2019cm0000","source":"Crossref","is-referenced-by-count":14,"title":["Fitness and frailty in myeloma"],"prefix":"10.1182","volume":"2022","author":[{"given":"Charlotte","family":"Pawlyn","sequence":"first","affiliation":[{"name":"1 The Institute of Cancer Research, London, UK"},{"name":"2 The Royal Marsden NHS Foundation Trust, London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah M.","family":"Khan","sequence":"additional","affiliation":[{"name":"3 The Ohio State University Comprehensive Cancer Center, Columbus, OH"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ciara L.","family":"Freeman","sequence":"additional","affiliation":[{"name":"4 H. Lee Moffitt Cancer Centre & Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"234","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"2022121313383413800_hem2022000346C1","unstructured":"SEER: Surveillance, Epidemiology, and End Results Program. Cancerstat facts: myeloma. https:\/\/seer.cancer.gov\/explorer\/application.html?site=89&data_type=1&graph_type=3&compareBy=sex&chk_sex_1=1&rate_type=2&race=1&advopt_precision=1&advopt_show_ci=on. 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