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.

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.

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.

IntroductionPrognostication in cancer is challenging and inaccurate. C-Reactive Protein (CRP), a cheap and sensitive marker of inflammation may help. This study investigated the relationship between CRP and prognosis in a large cohort of solid tumors with mixed cancer diagnoses and stages.MethodsElectronic medical records of 4931 adults with solid tumors who attended the Taussig Cancer Institute from 2006–2012 were reviewed. Demographic and clinical characteristics were recorded. Maximum CRP (mCRP) was identified for each individual. CRP was analysed as a time-dependent, continuous and categorical variable for association with survival.ResultsTwo thirds of patients had a high mCRP. This was consistently associated with shorter survival, even after correction for time from diagnosis, and when analysed as a continuous or a categorical variable. When mCRP values above 10 mg/L were subcategorized, a higher mCRP was always worse. Even among those with normal values, statistically and clinically significant shorter survival was noted at mCRP levels >5 mg/L.ConclusionsIn a large representative cohort of consecutive solid tumor patients the risk of death was clinically and statistically significantly greater with a high mCRP. This was independent of other variables and regardless of statistical method from both dates of diagnosis and test. CRP appeared to be underutilized. Our results support the routine use of CRP as a universal cost-effective independent prognostic indicator in most solid tumors.

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