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      Validation of a Prediction Tool for Chemotherapy Toxicity in Older Adults With Cancer

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          Abstract

          Purpose

          Older adults are at increased risk for chemotherapy toxicity, and standard oncology assessment measures cannot identify those at risk. A predictive model for chemotherapy toxicity was developed (N = 500) that consisted of geriatric assessment questions and other clinical variables. This study aims to externally validate this model in an independent cohort (N = 250).

          Patients and Methods

          Patients age ≥ 65 years with a solid tumor, fluent in English, and who were scheduled to receive a new chemotherapy regimen were recruited from eight institutions. Risk of chemotherapy toxicity was calculated (low, medium, or high risk) on the basis of the prediction model before the start of chemotherapy. Chemotherapy-related toxicity was captured (grade 3 [hospitalization indicated], grade 4 [life threatening], and grade 5 [treatment-related death]). Validation of the prediction model was performed by calculating the area under the receiver-operating characteristic curve.

          Results

          The study sample (N = 250) had a mean age of 73 years (range, 65 to 94 [standard deviation, 5.8]). More than one half of patients (58%) experienced grade ≥ 3 toxicity. Risk of toxicity increased with increasing risk score (36.7% low, 62.4% medium, 70.2% high risk; P < .001). The area under the curve of the receiver-operating characteristic curve was 0.65 (95% CI, 0.58 to 0.71), which was not statistically different from the development cohort (0.72; 95% CI, 0.68 to 0.77; P = .09). There was no association between Karnofsky Performance Status and chemotherapy toxicity ( P = .25).

          Conclusion

          This study externally validated a chemotherapy toxicity predictive model for older adults with cancer. This predictive model should be considered when discussing the risks and benefits of chemotherapy with older adults.

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          Most cited references27

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

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              Cancer statistics, 2015.

              Each year the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the National Cancer Institute (Surveillance, Epidemiology, and End Results [SEER] Program), the Centers for Disease Control and Prevention (National Program of Cancer Registries), and the North American Association of Central Cancer Registries. Mortality data were collected by the National Center for Health Statistics. A total of 1,658,370 new cancer cases and 589,430 cancer deaths are projected to occur in the United States in 2015. During the most recent 5 years for which there are data (2007-2011), delay-adjusted cancer incidence rates (13 oldest SEER registries) declined by 1.8% per year in men and were stable in women, while cancer death rates nationwide decreased by 1.8% per year in men and by 1.4% per year in women. The overall cancer death rate decreased from 215.1 (per 100,000 population) in 1991 to 168.7 in 2011, a total relative decline of 22%. However, the magnitude of the decline varied by state, and was generally lowest in the South (∼15%) and highest in the Northeast (≥20%). For example, there were declines of 25% to 30% in Maryland, New Jersey, Massachusetts, New York, and Delaware, which collectively averted 29,000 cancer deaths in 2011 as a result of this progress. Further gains can be accelerated by applying existing cancer control knowledge across all segments of the population. © 2015 American Cancer Society.
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                Author and article information

                Journal
                J Clin Oncol
                J. Clin. Oncol
                jco
                jco
                JCO
                Journal of Clinical Oncology
                American Society of Clinical Oncology
                0732-183X
                1527-7755
                10 July 2016
                16 May 2016
                10 December 2016
                : 34
                : 20
                : 2366-2371
                Affiliations
                [1]Arti Hurria, Tao Feng, David Smith, Can-Lan Sun, Vani Katheria, Caroline Doan, Laura Zavala, Abrahm Levi, and Chie Akiba, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA; Supriya Mohile, University of Rochester Medical Center, Rochester; Ajeet Gajra, Upstate Medical University and Syracuse VA Medical Center, Syracuse; William P. Tew, Memorial Sloan Kettering Cancer Center, New York, NY; Heidi Klepin, Wake Forest University School of Medicine, Winston Salem; Hyman Muss, University of North Carolina at Chapel Hill, Lineberger Comprehensive Cancer Center, Chapel Hill; Harvey Jay Cohen, Duke University, Durham, NC; Andrew Chapman, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA; and Nienke De Glas, Leiden University Medical Centre, Leiden, the Netherlands.
                Author notes
                Corresponding author: Arti Hurria, MD, City of Hope, 1500 E Duarte Rd, Duarte, CA 91010; e-mail: ahurria@ 123456coh.org .
                Article
                PMC5321104 PMC5321104 5321104 654327
                10.1200/JCO.2015.65.4327
                5321104
                27185838
                7f0fc934-2012-4444-bcad-2e7236c3479e
                © 2016 by American Society of Clinical Oncology
                History
                Page count
                Figures: 4, Tables: 6, Equations: 0, References: 27, Pages: 9
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