Ashley E Rosko1, Ying Huang2, Don M Benson2, Yvonne A Efebera2, Craig Hofmeister3, Samantha Jaglowski2, Steven Devine2, Geetika Bhatt2, Tanya M Wildes4, Alanna Dyko5, Desirée Jones2, Michelle J Naughton6, John C Byrd7, Christin E Burd8. 1. Division of Hematology, The Ohio State University, Columbus, OH, United States. Electronic address: Ashley.Rosko@osumc.edu. 2. Division of Hematology, The Ohio State University, Columbus, OH, United States. 3. Department of Hematology and Oncology, Emory University School of Medicine, Atlanta, GA, United States. 4. Division of Oncology, Washington University School of Medicine, Saint Louis, MO, United States. 5. Division of Medicinal Chemistry, College of Pharmacy, The Ohio State University, Columbus, OH, United States. 6. Cancer Prevention and Control, The Ohio State University, Columbus, OH, United States. 7. Division of Hematology, The Ohio State University, Columbus, OH, United States; Division of Medicinal Chemistry, College of Pharmacy, The Ohio State University, Columbus, OH, United States. 8. Departments of Molecular Genetics and Cancer Biology and Genetics, The Ohio State University, Columbus, OH, United States.
Abstract
Multiple myeloma (MM) is a disease of aging adults and autologous stem cell transplant (ASCT) is considered the standard of care. As the population ages a growing number of older adults will undergo ASCT and an objective approach to estimate physiologic reserve and transplant morbidity risk is warranted. Here, we evaluate assess p16INK4a (p16), a molecular aging biomarker, along with geriatric metrics to determine risk of transplant toxicity. METHODS: We prospectively evaluated 100 MM patients for frailty before and after ASCT using a Geriatric Assessment (GA) and collected T-cells for analysis of p16 using a custom nanostring codeset. RESULTS: Pre-transplant physical function was predicative of hospital length of stay (LOS). Each one-unit increase in physical function score, the average LOS decreased by 0.52 days (95% CI, -1.03-0.02); p = .04). Similarly, higher self-report of ADL/IADL (Human Activity Profile was associated with shorter LOS (0.65 less days (95% CI -1.15 to -0.15), p = .01). Patients with anxiety/depression (OR = 1.10 (95% CI 1.00-1.22), p = .056), lower handgrip strength (OR = 0.90 (95% CI 0.82-0.98), p = .02), falls (OR = 1.60 (95% CI 1.07-2.38), p = .02), or weight loss (OR = 5.65 (95% CI 1.17-25.24), p = .03) were more likely to be re-admitted. The estimated EFS at 1-year was 85% (95% CI, 75-91) with median follow-up of 15.7 months. Weight loss was a significant predictor of EFS (HR = 3.13 (95% CI 1.15-8.50), p = .03). Frailty assessment by self-reported fatigue minimally correlated with T-cell p16 expression (r = 0.28; p = .02). Age, Karnofsky Performance Status (KPS), or Hematopoietic cell transplantation-specific Co-Morbidity Index (HCT-CI) did not predict hospital LOS or readmissions. CONCLUSIONS: Our data illustrate that a GA can identify individuals with MM who are at greater risk for morbidity following ASCT.
Multiple myeloma (MM) is a disease of aging adults and autologous stem cell transplant (ASCT) is considered the standard of care. As the population ages a growing number of older adults will undergo ASCT and an objective approach to estimate physiologic reserve and transplant morbidity risk is warranted. Here, we evaluate assess p16INK4a (p16), a molecular aging biomarker, along with geriatric metrics to determine risk of transplant toxicity. METHODS: We prospectively evaluated 100 MMpatients for frailty before and after ASCT using a Geriatric Assessment (GA) and collected T-cells for analysis of p16 using a custom nanostring codeset. RESULTS: Pre-transplant physical function was predicative of hospital length of stay (LOS). Each one-unit increase in physical function score, the average LOS decreased by 0.52 days (95% CI, -1.03-0.02); p = .04). Similarly, higher self-report of ADL/IADL (Human Activity Profile was associated with shorter LOS (0.65 less days (95% CI -1.15 to -0.15), p = .01). Patients with anxiety/depression (OR = 1.10 (95% CI 1.00-1.22), p = .056), lower handgrip strength (OR = 0.90 (95% CI 0.82-0.98), p = .02), falls (OR = 1.60 (95% CI 1.07-2.38), p = .02), or weight loss (OR = 5.65 (95% CI 1.17-25.24), p = .03) were more likely to be re-admitted. The estimated EFS at 1-year was 85% (95% CI, 75-91) with median follow-up of 15.7 months. Weight loss was a significant predictor of EFS (HR = 3.13 (95% CI 1.15-8.50), p = .03). Frailty assessment by self-reported fatigue minimally correlated with T-cell p16 expression (r = 0.28; p = .02). Age, Karnofsky Performance Status (KPS), or Hematopoietic cell transplantation-specific Co-Morbidity Index (HCT-CI) did not predict hospital LOS or readmissions. CONCLUSIONS: Our data illustrate that a GA can identify individuals with MM who are at greater risk for morbidity following ASCT.
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