• Am J Hosp Palliat Care · Nov 2018

    The Optimal Cutoff Point for Expressing Revised Edmonton Symptom Assessment System Scores as Binary Data Indicating the Presence or Absence of Symptoms.

    • Jun Kako, Masamitsu Kobayashi, Yusuke Kanno, Asao Ogawa, Tomofumi Miura, and Yoshihisa Matsumoto.
    • 1 Division of Nursing Science, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
    • Am J Hosp Palliat Care. 2018 Nov 1; 35 (11): 1390-1393.

    ContextTerminally ill patients with cancer experience various physical and emotional symptoms that have a negative impact on quality of life and activities of daily living. Recently, revised Edmonton Symptom Assessment System (ESAS-r) scores have been proposed for assessing symptoms in terminally ill patients with cancer.ObjectiveTo determine the optimal cutoff point for expressing ESAS-r scores as binary data, indicating the presence or absence of symptoms.MethodsWe conducted a retrospective study of patients hospitalized in the palliative care unit of our hospital between September 1, 2014 and May 31, 2015. To determine the optimal cutoff point for expressing ESAS-r scores as binary data, indicating the presence or absence of 6 physical symptoms ("pain," "tiredness," "drowsiness," "nausea," "lack of appetite," and "dyspnea"), the sensitivity and specificity of each measurement were calculated. Cutoff points were estimated using receiver operating characteristic curve analysis.ResultsData from 157 patients who performed the self-assessment in ESAS-r scores were analyzed. The mean age was 66.5 years. Approximately 60.0% of patients were male. The optimal cutoff point for pain, tiredness, drowsiness, nausea, lack of appetite, and dyspnea was 4, 4, 4, 2, 5, and 4, respectively. The area under the curve for tiredness, nausea, and dyspnea was >0.70, followed in order by pain, lack of appetite, and drowsiness. The area under the curve for drowsiness was 0.55.ConclusionOur results suggest that physical symptoms other than drowsiness could potentially predict ESAS-r score severity.

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