Symptom Clusters in Cancer Patients with Brain Metastases

Rapid Response Radiotherapy Program, Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Ontario, Canada.
Clinical Oncology (Impact Factor: 3.4). 03/2008; 20(1):76-82. DOI: 10.1016/j.clon.2007.09.007
Source: PubMed


To explore the presence of symptom clusters in patients with brain metastases.
Patients with brain metastases referred to an outpatient palliative radiotherapy clinic were asked to rate their symptom distress using the Edmonton Symptom Assessment Scale (ESAS). Baseline demographic data were obtained. To determine interrelationships between symptoms, a principal component analysis with 'varimax rotation' was carried out on the nine ESAS items. Follow-up was carried out by telephone 1, 2, 4, 8 and 12 weeks after radiation.
Between January 1999 and January 2002, 170 patients with brain metastases provided complete baseline data on the ESAS. The most common primary cancer sites were lung, breast and gastrointestinal. Fatigue was the highest scored symptom, followed by a poor sense of well-being, anxiety, drowsiness and poor appetite. The four most prevalent symptoms were fatigue (91.7%), a poor sense of well-being (88.1%), drowsiness (82.2%) and anxiety (82.1%). Three symptom clusters were found at baseline. Cluster 1 included fatigue, drowsiness, shortness of breath and pain. Cluster 2 included anxiety and depression. Cluster 3 included poor appetite, nausea and a poor sense of well-being. Fatigue, nausea, drowsiness and poor appetite showed an overall increase in symptom severity over time; whereas fatigue, drowsiness and poor appetite were experienced to some extent by a greater proportion of patients at week 12 compared with baseline. Symptom clusters emerged in all weeks of follow-up, but consisted of different symptoms in each week.
Symptom clusters seemed to exist in patients with brain metastases before and after whole brain radiotherapy. However, different symptoms clustered at various time points. The effectiveness of whole brain radiotherapy in providing palliative relief to patients with brain metastases needs to be explored with regards to symptom clusters.

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    • "Recently, a multivariate projection technique , known as principal component (PC) analysis, has been employed to investigate the relationships among multiple variables for various medical purposes, such as in the interpretation of repetitive nerve stimulation results (Cengiz and Kuruo lu, 2006). This technique has also become popular in studies dealing with cancer, such as evaluating various symptom clusters in patients suffering from brain and bone metastases (Chow et al., 2008; Hadi et al., 2008). "
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    ABSTRACT: Many risk factors exist for chemotherapy-induced nausea and vomiting (CINV). This study utilized a multivariate projection technique to identify which risk factors were predictive of CINV in clinical practice. A single-centre, prospective, observational study was conducted from January 2007~July 2010 in Singapore. Patients were on highly (HECs) and moderately emetogenic chemotherapies with/without radiotherapy. Patient demographics and CINV risk factors were documented. Daily recording of CINV events was done using a standardized diary. Principal component (PC) analysis was performed to identify which risk factors could differentiate patients with and without CINV. A total of 710 patients were recruited. Majority were females (67%) and Chinese (84%). Five risk factors were potential CINV predictors: histories of alcohol drinking, chemotherapy-induced nausea, chemotherapy-induced vomiting, fatigue and gender. Period (ex-/current drinkers) and frequency of drinking (social/chronic drinkers) differentiated the CINV endpoints in patients on HECs and anthracycline-based, and XELOX regimens, respectively. Fatigue interference and severity were predictive of CINV in anthracycline-based populations, while the former was predictive in HEC and XELOX populations. PC analysis is a potential technique in analyzing clinical population data, and can provide clinicians with an insight as to what predictors to look out for in the clinical assessment of CINV. We hope that our results will increase the awareness among clinician-scientists regarding the usefulness of this technique in the analysis of clinical data, so that appropriate preventive measures can be taken to improve patients' quality of life.
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    • "Most studies of symptom clusters have grouped symptoms using symptom inventory types of instruments (e.g., MD Anderson Symptom Inventory) with factor analysis and cluster analysis. (Cleeland, Mendoza et al. 2000; Gift, Stommel et al. 2003; Wang, Tang et al. 2003; Gift, Jablonski et al. 2004; Wang, Wang et al. 2004; Chen and Tseng 2006; Wang, Laudico et al. 2006; Fan, Hadi et al. 2007; Gleason, Case et al. 2007; Chow, Fan et al. 2008; Hadi, Fan et al. 2008; Kim, Barsevick et al. 2008; Tseng, Cleeland et al. 2008; Wang, Tsai et al. 2008; Cheung, Le et al. 2009). A challenge with this approach is that it does not allow one to distinguish among patient subgroups on symptom severity scores or on different patterns of low and high symptom severity across subgroups. "
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    ABSTRACT: The purposes of this study of women with breast cancer receiving chemotherapy with/without radiation therapy were to determine whether: (1) subgroups of oncology outpatients can be identified based on a specific symptom cluster (i.e., pain, fatigue, sleep disturbances, depression); (2) these subgroups differ on outcomes (i.e., functional status, quality of life); (3) subgroup membership changes over time. A secondary data analysis using data collected from 112 women at initial chemotherapy. Symptom and outcome measures were completed at three time points: baseline (i.e., the week before cycle two - T1); end of cancer treatment (T2), end of the study (approximately one year after the start of chemotherapy - T3). Cluster analysis identified patient subgroups based on symptom severity scores. At T1 and T2, four patient subgroups were identified: ALL LOW (one or no symptom greater than the cut score), MILD (two symptoms), MODERATE (three or four symptoms), and ALL HIGH (four symptoms). At T3, three subgroups were identified: MILD, MODERATE and ALL HIGH. Subgroups with high severity levels of all four symptoms had poorer functional status and QOL at each time point than other subgroups (p<0.001). Group membership changed over time. Subgroups of patients with different symptom experiences were identified. For some patients severity of all four symptoms persisted months after cancer treatment. Initial and ongoing assessment to identify those patients in the ALL HIGH patient subgroup is important so that appropriate interventions to improve functional status and quality of life can be offered.
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    • "In fact, lack of energy, feeling drowsy, and difficulty sleeping were present at all three time points. The stability of these three symptoms is consistent with two previous longitudinal RT studies [7, 24] and suggests that these symptoms require systematic assessment and management in patients who undergo RT. Of note, the internal consistency coefficients for this symptom cluster were consistently high across the three time points (i.e., Cronbach's alphas ranged from 0.68 to 0.70) in this study as well as in the study by Chow and colleagues [7] (Cronbach's alphas ranged from 0.65 to 0.77). "
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