Cindy Kenis1,2, Abdelbari Baitar3, Lore Decoster4, Jacques De Grève4, Jean-Pierre Lobelle5, Johan Flamaing2,6, Koen Milisen2,7, Hans Wildiers1,8. 1. Department of General Medical Oncology, University Hospitals Leuven, Leuven, Belgium. 2. Department of Geriatric Medicine, University Hospitals Leuven, Leuven, Belgium. 3. Department of Medical Oncology, ZNA Middelheim, Antwerp, Belgium. 4. Department of Medical Oncology, Oncology Center, University Hospital Brussels, Vrije Universiteit Brussel, Brussels, Belgium. 5. Consultant in Statistics, Beernem, Belgium. 6. Department of Chronic Diseases, Metabolism, and Aging, Catholic University of Leuven, Leuven, Belgium. 7. Department of Public Health and Primary Care, Health Services and Nursing Research, Catholic University of Leuven, Leuven, Belgium. 8. Department of Oncology, Catholic University of Leuven, Leuven, Belgium.
Abstract
BACKGROUND: The aim of this study was to determine and compare the added prognostic value of screening tools, geriatric assessment (GA) components, and GA summaries to clinical information for overall survival (OS) in older patients with cancer. METHODS: A screening and a 10-item GA were systematically performed in patients ≥70 years old with cancer. Cox regression analyses were conducted to evaluate the added prognostic value for OS of screening tools, GA, and GA summaries to clinical information (age, stage, and tumor type) in 2 cohorts (A and B). Cox models were compared on the basis of the Akaike information criterion and the concordance probability estimate. The 2 cohorts for the analyses were similar but independent. RESULTS: A complete case analysis was available for 763 patients (median age, 76 years) in cohort A and for 402 patients (median age, 77 years) in cohort B. In both cohorts, most individual GA components were independent prognostic factors for OS. Nutritional status (assessed with the Mini Nutritional Assessment Short Form) and functional status (assessed with the Instrumental Activities of Daily Living) consistently displayed a strong capacity to predict OS. Less consistent results were found for screening tools. GA summaries performed the best in comparison with the screening tools and the individual GA components. CONCLUSIONS: Most individual GA components, especially nutritional status and functional status, are prognostic factors for OS in older patients with cancer. GA summaries provide more prognostic information than individual GA components but only moderately improve the prognostic baseline model with clinical information.
BACKGROUND: The aim of this study was to determine and compare the added prognostic value of screening tools, geriatric assessment (GA) components, and GA summaries to clinical information for overall survival (OS) in older patients with cancer. METHODS: A screening and a 10-item GA were systematically performed in patients ≥70 years old with cancer. Cox regression analyses were conducted to evaluate the added prognostic value for OS of screening tools, GA, and GA summaries to clinical information (age, stage, and tumor type) in 2 cohorts (A and B). Cox models were compared on the basis of the Akaike information criterion and the concordance probability estimate. The 2 cohorts for the analyses were similar but independent. RESULTS: A complete case analysis was available for 763 patients (median age, 76 years) in cohort A and for 402 patients (median age, 77 years) in cohort B. In both cohorts, most individual GA components were independent prognostic factors for OS. Nutritional status (assessed with the Mini Nutritional Assessment Short Form) and functional status (assessed with the Instrumental Activities of Daily Living) consistently displayed a strong capacity to predict OS. Less consistent results were found for screening tools. GA summaries performed the best in comparison with the screening tools and the individual GA components. CONCLUSIONS: Most individual GA components, especially nutritional status and functional status, are prognostic factors for OS in older patients with cancer. GA summaries provide more prognostic information than individual GA components but only moderately improve the prognostic baseline model with clinical information.
Authors: Clark DuMontier; Mina S Sedrak; Wee Kheng Soo; Cindy Kenis; Grant R Williams; Kristen Haase; Magnus Harneshaug; Hira Mian; Kah Poh Loh; Siri Rostoft; William Dale; Harvey Jay Cohen Journal: J Geriatr Oncol Date: 2019-08-23 Impact factor: 3.599
Authors: Jennifer L Lund; Paul R Duberstein; Kah Poh Loh; Nikesha Gilmore; Sandy Plumb; Lianlian Lei; Alexander P Keil; Jessica Y Islam; Laura C Hanson; Jeffrey K Giguere; Victor G Vogel; Brian L Burnette; Supriya G Mohile Journal: J Geriatr Oncol Date: 2021-09-02 Impact factor: 3.599
Authors: Lauren Brown; Samer A Naffouje; Christine Sam; Christine Laronga; M Catherine Lee Journal: Breast Cancer Res Treat Date: 2022-10-08 Impact factor: 4.624