Symptom clusters and their effect on the functional status of patients with cancer.
Symptom clusters and their effect on the functional status of patients with cancer.
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DOI：
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发表时间：
2001-04
影响因子：
1.9
通讯作者：
M. Dodd;C. Miaskowski;S. Paul
中科院分区：
文献类型：
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作者：
M. Dodd;C. Miaskowski;S. Paul
目的/方法确定疼痛、疲劳和睡眠不足的症状群在三个化疗周期中对功能状态的影响。设计前瞻性、纵向。设置23个门诊部和诊所。样本93例癌症患者。典型的参与者是女性（72%），已婚/伴侣（65%），白色（87%）和中年（55.4岁），平均受教育年限为14.8年。方法采用癌症患者生活质量量表（QOL-CA）和卡氏行为量表（KPS）对93例接受化疗的患者在化疗前（第1周期）和第3周期末（第2周期）进行调查。使用QOL-CA问卷中的三个项目（疼痛、容易疲劳、睡眠足以满足需求）来测量症状群。主要研究变量：症状群、结果、功能状态、化疗。发现层次多元回归模型解释了48.4%的功能状态的方差。时间1的KPS解释了时间2的KPS方差的30.8%（p < 0.001）。从时间2的KPS中分离出时间1的KPS后，认为在下一步中输入的四个自变量是时间1和时间2之间功能状态变化的预测因子。年龄解释了11.8%的变化（p = 0.001），疼痛解释了10.7%的变化（p = 0.002），疲劳解释了7.3%的变化（p = 0.011）。睡眠不足在统计学上不显著，仅解释了1%的变化（p = 0.344）。结论本研究为症状群对患者功能状态的影响提供了初步的见解。医疗保健专业人员需要意识到症状群的存在及其对患者未来发病率可能产生的协同不良影响。
PURPOSE/OBJECTIVES To determine the effect of the symptom cluster of pain, fatigue, and sleep insufficiency on functional status during three cycles of chemotherapy. DESIGN Prospective, longitudinal. SETTING 23 outpatient offices and clinics. SAMPLE 93 patients with cancer. The typical participant was female (72%), married/partnered (65%), white (87%), and middle-aged (55.4 years), with an average of 14.8 years of education. METHODS The Quality of Life-Cancer (QOL-CA) version instrument and the Karnofsky Performance Scale (KPS) were completed by 93 outpatients receiving chemotherapy at baseline (Time 1) and at the end of the third cycle (Time 2). Three items (pain, tires easily, sleeps enough to meet needs) from the QOL-CA questionnaire were used to measure the symptom cluster. MAIN RESEARCH VARIABLES Symptom cluster, outcome, functional status, chemotherapy. FINDINGS A hierarchical multiple regression model explained 48.4% of the variance in functional status. The KPS at Time 1 explained 30.8% of the variance in KPS at Time 2 (p < 0.001). After KPS at Time 1 was partialled out from KPS at Time 2, the four independent variables entered in the next step were considered predictors of the change in functional status between Time 1 and Time 2. Age explained 11.8% of the change (p = 0.001), pain explained 10.7% of the change (p = 0.002), and fatigue explained 7.3% of the change (p = 0.011). Sleep insufficiency statistically was not significant, only explaining 1% of the change (p = 0.344). CONCLUSION This study provides beginning insights into the effect of a symptom cluster on patients' functional status. IMPLICATIONS FOR NURSING PRACTICE Healthcare professionals need to be aware of the presence of symptom clusters and their possible synergistic adverse effect on patients' future morbidity.