Aynur Aktas

Review: Symptom clusters: myth or reality?

Clinical experience suggests that many symptoms occur together. In this paper, we examine the rationale and evidence base for symptom clusters in different medical fields, particularly the cluster phenomenon in cancer. Cancer symptom clusters are a reality. Various symptoms that cluster clinically have also been verified statistically. Specific clusters such as nausea—vomiting, anxiety—depression, and cough—dyspnea are evident on both clinical observation and in research investigation. Fatigue—pain and fatigue—insomnia—pain have also been demonstrated statistically as clusters. Another proposed cluster ‘depression—fatigue—pain’ seems relevant to clinical practice. Other clusters may serve only as theoretical models that illustrate possible common biological etiologies in cancer; they need to be validated in future research. Analysis of the literature is complicated by considerable inconsistencies across studies. Discrepancies between clinically defined and statistically obtained clusters raise important questions. We must consider the analytical techniques used, and how methodology might influence cluster occurrence and composition. Further research is warranted to establish universally accepted statistical methods and assessment tools for symptom cluster research.

Cancer Symptom Clusters: Clinical and Research Methodology

INTRODUCTION Patients with cancer experience multiple symptoms that frequently appear in groups or clusters. We conducted a comprehensive clinical review of cancer symptom cluster studies to identify common symptom clusters (SC), explore their clinical relevance, and examine their research importance. METHODS Published studies and review articles on cancer SC were obtained through a literature search. We identified 65 reports. These varied in assessment instruments, outcomes, design, population characteristics, and study methods. RESULTS Two main approaches to symptom cluster identification were found: clinical and statistical. Clinically determined SC were based upon observations of symptom co-occurrence, associations, or interrelations. These included fatigue-pain, fatigue-insomnia, fatigue-insomnia-pain, depression-fatigue, and depression-pain. They were analyzed by multivariate analysis. They had low to moderate statistical correlations. Disease- or treatment-related SC were influenced by primary cancer site, disease stage, or antitumor treatment. SC determined by statistical analysis were identified by factor and cluster analysis through nonrandom symptom distribution. Nausea-vomiting, anxiety-depression, fatigue-drowsiness, and pain-constipation consistently clustered by either or both of these statistical methods. The individual symptoms of pain, insomnia, and fatigue often appeared in different clusters. A consensus about standard criteria and methodological techniques for cluster analysis should be established. CONCLUSIONS Several important cancer SC have been identified. Nausea-vomiting, anxiety-depression, and dyspnea-cough clusters were consistently reported. The techniques of symptom cluster identification remain a research tool, but one with considerable potential clinical importance. Further research should validate our analytical techniques, and expand our knowledge about SC and their clinical importance.

Cancer symptom clusters: old concept but new data.

Individuals with cancer have multiple symptoms, which frequently co-occur. A nonrandom distribution of symptoms suggests a common mechanism. Symptom clusters (SCs) were considered part of various syndromes in the early years of medicine. The SC concept in clinical medicine is old. Symptom clusters were commonly described in the psychology/psychiatry and neurology literature. Symptom cluster may be defined either clinically or statistically. Statistically derived clusters can differ from clinically defined clusters. The clinical importance of statistically derived clusters is unclear. Pain-insomnia-fatigue and pain-depression-fatigue are commonly recognized clinical clusters. Nausea-vomiting and anxiety-depression are also statistically observed clusters. The longitudinal stability of clusters is unknown. Certain SCs, appear to have a greater adverse influence on outcomes (such as performance status and survival) than others. Comorbidities probably influence symptoms at different levels, but their effect on cancer clusters is unknown. Comprehensive symptom assessment is crucial to cluster identification. The potential use of the cluster concept to abbreviate symptom assessment tools needs validation. Symptom cluster can be disease and/or treatment related and may change as individuals undergo antitumor therapies. Polypharmacy in symptom management is frequent but could be minimized if 1 drug could be used to treat cluster symptoms. Symptom cluster appears to vary with the assessment tool, disease stage, symptom domain used to cluster, cluster methodology, and number of symptoms assessed. The validity and reliability of SCs need universally accepted statistical methods, assessment tools, and symptom domains. For now, nausea-vomiting is recognized as a consistent cluster across multiple studies. Pain-depression-fatigue and pain-insomnia-fatigue are also well recognized. Symptom clusters may help in cancer diagnosis, symptom management, and prognostication. However, the cluster method, reliability, and validity need to be established before assessment or treatment guidelines are established. Symptom clusters require further research before becoming part of routine medical symptom assessment and management.

Symptom severity and distress in advanced cancer

We determined the relationship between symptom severity and distress for multiple cancer symptoms, and examined patient demographic influences on severity and distress in advanced cancer. A Cochran—Armitage trend test determined whether symptom distress increased with severity. Chi-square, Fisher’s exact test and logistic regression analysis examined moderate/severe (‘clinically important’) and distressful symptoms by age (≤65 versus >65), gender, primary site group, and ECOG performance status. Forty-six symptoms were analyzed in 181 individuals. More than 50% of individuals with clinically important symptoms rated them as distressful. The median percentage of individuals with mild but still distressful symptoms was 25%, with a range of 0% (bad dreams) to 73% (sore mouth). In both univariate and multivariate analysis, younger (≤65 years) patients, females, and those with poor performance status had more clinically important and a higher prevalence of distressful symptoms (only anxiety was more frequently distressful to older individuals). Clinically important symptoms and two of those considered distressful varied by primary site group. After control for severity, symptom distress did not differ by primary site group. The prevalence of distress increased with greater symptom severity. Younger individuals, those with poor performance status, and females had greater symptom severity and distress. Mild symptoms were often distressful. After adjustment for severity, age, gender, and performance status all influenced symptom distress.

Symptom prevalence in advanced cancer: age, gender, and performance status interactions.

Age, gender, and performance status (PS) are important patient characteristics which might influence to cancer symptom profile. We conducted a secondary analysis of a symptom database to examine any interaction of these factors on symptom prevalence. 38 symptoms were assessed in 1000 consecutive patients with advanced cancer. The association of the three demographic factors with each symptom was examined using logistic regression analysis. Eight symptoms were associated with more than one of the three factors. Model-based estimates of symptom prevalence were calculated for 30 groups based on combinations of age, gender, and ECOG PS (0-4). Prevalence differences between various groups >10% were empirically classified as clinically relevant. The frequency of all eight symptoms (pain, constipation, sleep problems, nausea, anxiety, vomiting, sedation, and blackouts) was associated with more than one of the demographic characteristics of age, gender, and PS level. The prevalence of all eight decreased with older age. Females had more nausea, anxiety, and vomiting than males; males greater sleep problems. The prevalence of constipation, sedation, and blackouts was higher with worse PS, whereas pain and anxiety became less common with worse PS. Age, gender, and PS appeared to be associated with variations in the prevalence of eight gastrointestinal and neuropsychological symptoms in cancer patients. They should be included as important variables in clinical practice symptom research data.

Consistency of symptom clusters in advanced cancer.

Background: The reproducibility of symptom clusters (SCs) in different populations would support the validity of the cluster concept. Ideal approaches to cluster identification are unknown. The presence of a sentinel (most prevalent) symptom may reduce the number of symptoms in a comprehensive symptom assessment tool. The primary purpose was to assess consistency of SCs between 2 independent data sets. A secondary aim was to evaluate whether use of a sentinel symptom might abbreviate assessment but retain acceptable accuracy. Methods: An agglomerative hierarchical cluster analysis in 922 patients with advanced cancer identified 7 SCs. We conducted the same analysis on an additional 181 cancer patients to assess cluster consistency. The most prevalent symptom within each cluster was defined as the ‘‘sentinel’’ symptom. Positive predictive value (PPV) and negative predictive value (NPV) were calculated to assess ability of the sentinel symptom to predict other symptoms in the cluster. Results: Similar clusters were identified in both data sets, which included nausea/vomiting, neuropsychologic, and aerodigestive clusters. When the sentinel symptom was present, >50% nonsentinel symptoms in a cluster were present; when absent, <50% nonsentinel symptoms were identified. However, the range for PPV and NPV of the sentinel symptom to identify other symptoms in the cluster was 19% to 72% and 41% to 95%, respectively. Conclusions: Consistent SCs were found in 2 separate data sets with the same assessment tool and statistical analysis. These findings support the statistical and clinical validity of the cluster concept through consistency between different populations. The nausea/vomiting, neuropsychologic, and aerodigestive clusters may be reliable for use in assessment. The presence or absence of a sentinel symptom in each cluster did not predict the presence or absence of other symptoms in the cluster. Sentinel symptoms are inadequate to assess symptom burden.

Symptom clusters and prognosis in advanced cancer.

In the USA in 2009, an estimated 1,479,350 new cases at all cancer sites are expected; 562,340 people are expected to die of cancer [1]. Cancer is the second most frequent cause of death in the USA. Life expectancy (survival) is an important predicted outcome of the natural history of an illness. As the disease progresses, survival prediction is particularly important to appropriate decision making and advance care planning. Clinical and biologic predictors of survival in cancer patients vary by the disease stage [2]. For instance, performance status, quality of life (QoL), and some biochemical parameters have been used to develop prognostic scores in advanced cancer patients [3–7]. Individual symptoms of cancer anorexia–cachexia syndrome (e.g., anorexia, weight loss, and dysphagia), delirium, dyspnea, and fatigue have also been reported to be independent predictors of survival in persons receiving palliative care [8–10]. Such well-known associations between specific symptoms and clinical outcomes might be more pronounced in those who present with symptom clusters (SC). People with advanced cancer experience multiple concurrent symptoms which may have different clinical outcomes than those with individual symptoms. Functional status and QoL were major clinical outcomes negatively correlated with the number or severity of certain symptoms within the clusters [11]. Only one study [12] has investigated the prognostic importance of SC in cancer patients. However, how clusters might affect prognosis and their potential predictability is not fully understood. Currently, none of the cancer prognostication models [5, 6, 13–16] consider whether SC help predict survival. In this paper, we first examined whether seven SC identified in a prior analysis [17] were prognostic for survival in advanced cancer. Secondly, we investigated whether the number of these SC present was related to prognosis.

The relationship between symptom prevalence and severity and cancer primary site in 796 patients with advanced cancer.

Knowledge of differences in symptom experience between cancer sites may help better understand symptom pathophysiology. A total of 38 symptoms in 796 consecutive patients with advanced cancer were retrospectively analyzed. Symptom prevalence and severity were compared among the 12 primary site groups (PSGs) by the chi-square test. Pairwise comparisons determined which sites differed. Pain, fatigue, weakness, lack of energy, and anorexia had the highest overall prevalence but did not differ among PSGs. The 3 most common neuropsychological symptoms (insomnia, depression, and anxiety) also did not vary among PSGs. Nineteen (50%) symptoms varied significantly between PSGs, in prevalence (17), severity (14), or both (12). Nine of 17, 6 of 14, and 6 of 12 were gastrointestinal symptoms. Symptoms which varied by PSGs can be included in cancer site-specific symptom assessment instruments.

The psychometric properties of cancer multisymptom assessment instruments: a clinical review

PurposeVarious instruments are used to assess both individual and multiple cancer symptoms. We evaluated the psychometric properties of cancer multisymptom assessment instruments.MethodsAn Ovid MEDLINE search was done. All searches were limited to adults and in English. All instruments published from 2005 to 2014 (and with at least one validity test) were included. We excluded those who only reported content validity. Instruments were categorized by the three major types of symptom measurement scales employed as follows: visual analogue (VAS), verbal rating (VRS), and numerical rating (NRS) scales. They were then examined in two areas: (1) psychometric thoroughness (number of tests) and (2) psychometric strength of evidence (validity, reliability, generalizability). We also assigned an empirical global psychometric quality score (which combined the concepts of thoroughness and strength of evidence) to rank the instruments.ResultsWe analyzed 57 instruments (17 original, 40 modifications). They varied in types of scales used, symptom dimensions measured, and time frames evaluated. Of the 57, 10 used VAS, 28 VRS, and 19 NRS. The Edmonton Symptom Assessment System (ESAS), ESAS-Spanish, Hospital Anxiety and Depression Scale (HADS), Profile of Mood States (POMS), Symptom Distress Scale (SDS), M.D. Anderson Symptom Inventory (MDASI)-Russian, and MDASI-Taiwanese were the most comprehensively tested for validity and reliability. The ESAS, ESAS-Spanish, ASDS-2, Memorial Symptom Assessment Scale (MSAS)-SF, POMS, SDS, MDASI (and some translations), and MDASI-Heart Failure all showed good validity and reliability.ConclusionsThe MDASI appeared to be the best overall from a psychometric perspective. This was followed by the ESAS, ESAS-Spanish, POMS, SDS, and some MDASI translations. VRS-based instruments were most common. There was a wide range of psychometric rigor in validation. Consequently, meta-analysis was not possible. Most cancer multisymptom assessment instruments need further extensive validation to establish the excellent reliability and validity required for clinical utility and meaningful research.

Connected health: cancer symptom and quality-of-life assessment using a tablet computer: a pilot study.

Incorporation of tablet computers (TCs) into patient assessment may facilitate safe and secure data collection. We evaluated the usefulness and acceptability of a TC as an electronic self-report symptom assessment instrument. Research Electronic Data Capture Web-based application supported data capture. Information was collected and disseminated in real time and a structured format. Completed questionnaires were printed and given to the physician before the patient visit. Most participants completed the survey without assistance. Completion rate was 100%. The median global quality of life was high for all. More than half reported pain. Based on Edmonton Symptom Assessment System, the top 3 most common symptoms were tiredness, anxiety, and decreased well-being. Patient and physician acceptability for these quick and useful TC-based surveys was excellent.