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Identifying Severe Adverse Event Clusters Using the National Cancer Institute's Common Terminology Criteria for
Xiaobo Zhong1, Emerson A Lim1, Dawn L Hershman1
1Columbia University, New York, NY; and Fred Hutchinson Cancer Research Center, Seattle, WA.
Purpose:
Exploring the relationships among adverse events is important because those that arise from a common mechanism are amenable to a common intervention, which can improve symptom management, quality of life, and treatment adherence. To date, symptom cluster studies have used patient-reported data, which are not always available in clinical trials. In this study, we proposed using the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) to identify adverse event clusters because the CTCAE data are collected as standard practice and can therefore be used when patient-reported outcomes are unavailable.
Methods:
The CTCAE data from a randomized clinical trial conducted by SWOG that compared docetaxel plus estramustine versus mitoxantrone plus predinsone in patients with advanced prostate cancer were used to identify severe adverse event clusters. A variable based hierarchical cluster analysis was conducted using the CTCAE for the 323 patients who experienced at least one grade 3 or higher adverse event.
Results:
A total of 109 adverse event types were captured using the CTCAE. Four clusters had moderate associations: nausea, vomiting, and anorexia (n = 35, r = 0.45); joint/bone(myalgia, arthralgia, and arthritis) and muscle weakness (n = 26, r = 0.29); anemia and transfusion (n = 20, r = 0.38); and neutrophils/granulocytes, febrile neutropenia, and leukocytes/lymphopenia (n = 114, r = 0.29). Two clusters had weak associations: fatigue/malaise/lethargy and dehydration (n = 66, r = 0.12); and constipation, infection without neutropenia, and abdominal pain/cramping (n = 35, r = 0.13).
Conclusion:
Several severe adverse event clusters were identified in patients with advanced prostate cancer. Identifying adverse event clusters using CTCAE data from clinical trials is feasible.
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