Identification Of Symptom Clusters Research Articles

Identification of symptom clusters and change trajectories in patients with acute exacerbation of chronic obstructive pulmonary disease

Aim and objectivesThis study aimed to identify symptom cluster (SC) patterns and change trajectories in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), the correlation of the SCs with laboratory and imaging indicators, and the intrinsic association of the SCs with prognostic outcomes and disease burden. MethodSymptom information was collected using a digital evaluation scoring system at the time of admission, on the third day after admission, and upon discharge. Laboratory and imaging examination data were compiled simultaneously. Exploratory factor analysis was used to identify the AECOPD SCs. The number of factors (clusters) was determined by examining factors with eigenvalues ≥1.0, using 0.50 for factor loadings as the minimum cut-off value. Spearman's correlation analysis was used to explore the link between the SCs and laboratory and imaging indicators, as well as the relationship between the severity of the symptoms in different clusters, prognostic outcomes, and disease burden. ResultsThis study included 148 patients. Three SCs were identified: activity-nutrition SC, breath-sleep SC and respiratory SC. Correlation analysis indicated a connection between the activity-nutrition SC and the white blood cell count, and serum sodium and potassium levels, whereas the breath-sleep SC was correlated with white blood cells and eosinophil counts, serum potassium level, and pleural effusion. Additionally, the respiratory SC was associated with serum calcium and magnesium levels, the partial pressure of carbon dioxide, and C-reactive protein (CRP) level. There was a positive correlation between the activity-nutrition SC and hospitalization cost, as well as between the breath-sleep SC and both the hospitalization length and cost. ConclusionPatients with AECOPD presented three SCs that affected the length and cost of hospitalization. Concurrently, the severity of the symptoms in the clusters was related to white blood cell and eosinophil counts; serum sodium, potassium, calcium, and magnesium levels; CRP level; the partial pressure of carbon dioxide; and pleural effusion, indicating that the symptoms in each clusters may share related physiological mechanisms. An in-depth exploration of the pathogenesis and intervention paths of health problems is of great significance for promoting precision nursing.

Identification of symptom clusters and sentinel symptoms during the first cycle of chemotherapy in patients with lung cancer

PurposeTo identify symptom clusters (SCs) in patients with lung cancer who are undergoing initial chemotherapy and to identify the sentinel symptoms of each SC.MethodsA convenience sampling method was used to recruit patients with lung cancer who were undergoing their initial chemotherapy treatment. Patient information was collected using the General Demographic Questionnaire, MD Anderson Symptom Inventory (including the lung cancer module) and a schedule documenting the initial occurrence of symptoms. The Walktrap algorithm was employed to identify SCs, while sentinel symptoms within each SC were identified using the Apriori algorithm in conjunction with the initial occurrence time of symptoms.ResultsA total of 169 patients with lung cancer participated in this study, and four SCs were identified: the psychological SC (difficulty remembering, sadness, dry mouth, numbness or tingling, and distress), somatic SC (pain, fatigue, sleep disturbance, and drowsiness), respiratory SC (coughing, expectoration, chest tightness, and shortness of breath), and digestive SC (nausea, poor appetite, constipation, vomiting, and weight loss). Sadness, fatigue, and coughing were identified as sentinel symptoms of the psychological, somatic, and respiratory SCs, respectively. However, no sentinel symptom was identified for the digestive SC.ConclusionPatients with lung cancer who are undergoing chemotherapy encounter a spectrum of symptoms, often presenting as SCs. The sentinel symptom of each SC emerges earlier than the other symptoms and is characterized by its sensitivity, significance, and driving force. It serves as a vital indicator of the SC and assumes a sentry role. Targeting sentinel symptoms might be a promising strategy for determining the optimal timing of interventions and for mitigating or decelerating the progression of the other symptoms within the SC.

Health-Related Quality of Life for Patients with Post-Acute COVID-19 Syndrome: Identification of Symptom Clusters and Predictors of Long-Term Outcomes

BackgroundFollowing COVID-19 infection, as many as a third of patients have long-term symptoms, known as post-acute sequelae (PASC). The mechanisms contributing to PASC remain largely unknown and, due to the heterogeneity of symptoms, treating PASC provides unique challenges.ObjectiveOur study sought to (1) identify clinical symptom profiles based on PROMIS Global Health (GH) items, (2) evaluate demographic and clinical differences across profiles, and (3) identify predictors of change in health-related quality of life (HRQL) over time.DesignThis was an observational cohort study of patients with PASC who completed PROMIS-GH between 2/11/21 and 12/3/21 as part of routine care, with data extracted from the electronic health record.ParticipantsThere were 1407 adult patients (mean age 49.6 ± 13.7, 73% female, 81% White race) with PASC seen in the recovery clinic between 2/11/21 and 12/3/21, with 1129 (80.2%) completing PROMIS-GH as routine care.Main MeasuresHRQL was measured with PROMIS-GH at initial visit and after 12 months.Key ResultsLatent profile analysis identified symptom classes based on five PROMIS-GH items (mental health, ability to carry out physical activities, pain, fatigue, and emotional problems). Four latent profiles were identified: (1) “Poor HRQL” (n = 346), (2) “Mixed HRQL: good mental/poor physical” (n = 232), (3) “Mixed HRQL: poor mental/good physical” (n = 324), and (4) “Good HRQL” (n = 227). Demographics and comorbidities varied significantly across profile with patients with more severe COVID-19 infection more likely to be in profiles 1 and 2. Overall, patients improved 2 T-score points on PROMIS-GH after 12 months, with differences by profile. Predictors of improved HRQL included profile, lower body mass index, and fewer COVID symptoms.ConclusionsPatients with PASC have distinct HRQL symptom profiles which were able to differentiate across COVID-19 severity and symptoms. Improvement over 12 months differed by profile. These profiles may be used to better understand the mechanisms behind PASC. Future research should evaluate their ability to guide treatment decisions to improve HRQL.

Identification of Core Symptom Cluster in Patients With Digestive Cancer: A Network Analysis.

A lack of identified core symptom clusters in digestive cancer patients hinders achieving precision symptom intervention. There are few studies on identifying digestive cancer symptom clusters based on network analysis. The aims of this study were to construct the symptom network of digestive cancer patients and identify the core symptom cluster. A cross-sectional study was conducted among 202 digestive cancer patients. The Chinese version of the MD Anderson Symptom Inventory for gastrointestinal cancer scale was used to assess the symptoms by convenience sampling. R software was used to construct a symptom network and identify core symptom clusters. Edge weight and centrality difference tests were used to test the accuracy of core symptom cluster identification. The most common symptoms were distress, poor appetite, and sadness. The most serious symptoms were poor appetite, disturbed sleep, and fatigue. The core symptom cluster of the psychoemotional symptom group was distress, sadness, and numbness. The centrality index showed that the top 3 in strength were distress (Rs = 1.11), fatigue (Rs = 1.09), and sadness (Rs = 1.04). The edge weight difference test showed that the psychoemotional symptom group had high stability. The psychoemotional symptoms of digestive cancer patients should be given priority for intervention. Network analysis must be extended to the symptom research of cancer patients as soon as possible to provide a scientific basis for symptom management. Nurses must perform comprehensive psychological and emotional assessments, initiate referrals for psychoemotional symptom management and psychological services, and administer pharmacologic and nonpharmacologic interventions to improve appetite loss in digestive cancer patients.

Identification of symptom clusters in patients with ankylosing spondylitis

Symptom cluster refers to a group of 3 or more related symptoms that occur together. Our objectives were to: (1) investigate the frequency, severity and gender difference of symptoms in patients with ankylosing spondylitis (AS); and (2) identify symptom clusters in AS patients. A cross-sectional questionnaire-based survey was conducted using the Patient Health Questionnaire Symptoms Group combined with self-designed variables. Demographic and symptom variables between male and female patients in terms of C-reactive protein (CRP) and human leukocyte antigen (HLA)-B27 status were analyzed with 2-tailed independent t test for continuous variables and Chi-square or Fisher's exact test for categorical variables. Symptom clusters were extracted and analyzed by principal component analysis. There were 122 AS patients included in the study. The most severe symptoms included severe back pain, extremity or joint pain, difficulty in breathing, constipation, intestine discomfort and diarrhea. Stomachache was more prevalent in male patients in which odds ratio was 4.60 (CI 1.59-12.97) (P=.006). Patients with HLA-B27 negativity or a higher CRP value were more likely to have dry mouth. Four symptom clusters were classified, which explained 58.4% of the total variation. They were named as the gastrointestinal-cardiac cluster, the fatigue-sleep disturbance cluster, the headache-chest pain cluster, and the mouth-eye cluster. The symptoms appeared to cluster into 4 groups in AS patients, which should be noticed in clinical care.

Feature Engineering: Toward Identification of Symptom Clusters of Mental Disorders

Feature Engineering: Toward Identification of Symptom Clusters of Mental Disorders

Identification of Symptom Clusters Among Midlife Menopausal Women with Metabolic Syndrome.

The aim of this study was to identify and compare symptom clusters in midlife menopausal women with and without metabolic syndrome based on symptom occurrence and severity dimension through secondary data analysis of the Study of Women's Health Across the Nation. Exploratory factor analysis was used to group symptoms that are highly correlated with each other and verified by confirmatory factor analysis. Midlife menopausal women with metabolic syndrome (n = 424) experienced mental health, vasomotor, and somatic cluster across both symptom dimensions. In contrast, midlife menopausal women without metabolic syndrome (n = 1022) experienced mental health/sleep/urinary, vasomotor, and somatic cluster for symptom occurrence dimension and mental health/sleep, vasomotor/genital, and somatic cluster for symptom severity dimension. This is the first study to identify symptom clusters in midlife menopausal women with metabolic syndrome, who are at risk for experiencing complex symptoms associated with menopause transition and metabolic syndrome, and to compare symptom clusters to those without metabolic syndrome.

Symptom clusters experienced by breast cancer patients at various treatment stages: A systematic review.

Breast cancer patients often experience symptoms that adversely affect their quality of life. It is understood that many of these symptoms tend to cluster together: while they might have different manifestations and occur during different phases of the disease trajectory, the symptoms often have a common aetiology that is a potential target for intervention. Understanding the symptom clusters associated with breast cancer might usefully inform the development of effective care plans for affected patients. The aim of this paper is to provide an updated systematic review of the known symptom clusters among breast cancer patients during and/or after cancer treatment. A search was conducted using five databases for studies reporting symptom clusters among breast cancer patients. The search yielded 32 studies for inclusion. The findings suggest that fatigue‐sleep disturbance and psychological symptom cluster (including anxiety, depression, nervousness, irritability, sadness, worry) are the most commonly‐reported symptom clusters among breast cancer patients. Further, the composition of symptom clusters tends to change across various stages of cancer treatment. While this review identified some commonalities, the different methodologies used to identify symptom clusters resulted in inconsistencies in symptom cluster identification. It would be useful if future studies could separately examine the symptom clusters that occur in breast cancer patients undergoing a particular treatment type, and use standardised instruments across studies to assess symptoms. The review concludes that further studies could usefully determine the biological pathways associated with various symptom clusters, which would inform the development of effective and efficient symptom management strategies.

Oral Presentation

Oral Presentation

Identification of Symptom Clusters and Their Influencing Factors in Subgroups of Chinese Patients With Acute Exacerbation of Chronic Obstructive Pulmonary Disease

ContextLimited studies have identified symptom clusters (SCs) and their risk factors and the relationships with inflammatory biomarkers in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). ObjectivesIn this study, we aimed to investigate SCs in patients with AECOPD and explore their influencing factors and relationships with inflammatory biomarkers. MethodsData were collected with sociodemographic and disease information questionnaires, and symptoms were measured with the revised Memorial Symptom Assessment Scale. SCs were extracted through exploratory factor analysis. Logistic regression analysis was conducted to explore the risk factors of SCs. ResultsA total of 151 patients were recruited. Two SCs, namely, emotional and respiratory functional SCs, were identified. Logistic regression analysis showed that individuals with high C-reactive protein level, Charlson Comorbidity Index score, and high modified Medical Research Council Dyspnea Scale score were more likely to belong to the high-severity symptom subgroup than to the low-severity symptom group in the emotional SC. The patients with a low body mass index and without or lax inhaled drug therapy exhibited highly prominent predictors of membership in the high-severity symptom group of the respiratory functional SC. ConclusionSymptoms experienced by patients with AECOPD were grouped into specific clusters. Targeted interventions should be performed based on SCs, and influencing factors and biological mechanisms should be considered when providing individualized approaches and interventions.

Symptom Clusters and Influencing Factors in Children With Acute Leukemia During Chemotherapy.

Acute leukemia is the most common malignancy in childhood. Identification of symptom clusters and their influencing factors in children with acute leukemia may improve the management of symptoms. The aims of this study were to investigate symptom clusters in children with acute leukemia during chemotherapy and analyze the factors influencing their severity. A cross-sectional survey was administered to 159 Chinese children with acute leukemia during chemotherapy. A demographic questionnaire and the Memorial Symptom Assessment Scale 10-18 were completed by the patients and their parents. Exploratory factor analysis was used to identify symptom clusters. Univariate analyses and multiple linear regression analyses were performed to analyze the factors affecting symptom clusters. Six clusters were identified, including gastrointestinal cluster, emotional cluster, head-related cluster, self-image disorder cluster, skin itching/irritation cluster, and somatic cluster. Factors influencing the severity of symptom clusters included age, gender, stage of chemotherapy, and medical reimbursement. There are multiple symptom clusters in children with acute leukemia during chemotherapy. Medical care staff should manage symptoms from the perspective of symptom cluster and carry out personalized interventions according to factors influencing these clusters. The ability to predict symptom clusters in children with acute leukemia undergoing chemotherapy may assist with optimal clinical decision making in order to alleviate the symptom burden and improve children's quality of life.

Prevalence of Symptoms and Symptom Clusters of Patients on Dialysis in Uruguay

Patients on dialysis report high levels of symptom burden. The association of these symptoms may have an increased deleterious effect on the patients' well-being. This study aimed to assess the prevalence of symptoms, to identify symptom clusters, and to describe the impact of concurrent symptoms on physical and emotional well-being in a sample of dialysis patients. Data of the first assessment of a longitudinal study aimed to assess patient-reported outcomes in dialysis were included here. The KDQOL-36 PCS, MCS and Symptom Subscale, the Hospital Anxiety and Depression Scales and the Epworth Sleepiness Scale were analyzed. The ICLUST procedure was followed for hierarchical cluster analyses. Of the 512 eligible patients, 493 accepted to participate, 43.6 % were female, with mean age of 60.9 (SD=16.7). Treatment modality was HD in 87.6% of patients. Most prevalent and severe symptoms were muscle sores, cramps, "washed out", dry skin, and itchy skin, Moderate to severe pain was reported by 25%, and daily somnolence by 12.4% of the patients. Five first level symptom clusters were identified as cutaneous, cardiac, digestive, sensory-motor, energy. Both, the presence of any cluster and cluster scores were significantly associated with lower physical and mental quality of life and a higher psychological distress. Our study confirms the presence of high symptom burden in dialysis patients in Uruguay. Several symptom clusters were identified having significant impact on the patients' well-being. The identification of symptom clusters can help to understand common underlying pathways. It is possible that the management of symptom clusters may reduce symptom burden in these patients.

Identification of symptom clusters and their synergistic effects on quality of life in rheumatoid arthritis patients.

To examine the presence of symptom clusters and synergistic effects of symptom clusters on quality of life in rheumatoid arthritis patients. Rheumatoid arthritis patients frequently experience multiple concurrent symptoms of pain, fatigue, and depression. A nonexperimental, cross-sectional correlation design. The study participants were 179 rheumatoid arthritis patients. Data were collected between August and December 2016. A hypothetical model was developed based on the Theory of Unpleasant Symptoms Model: physiological antecedents included disease activity and obesity; symptoms of pain, fatigue, and depression were hypothesized as being clustered, and quality of life was taken as the outcome variable. Disease activity had significant direct effects on pain, fatigue, and depression and indirect effects on fatigue and depression, whereas obesity had a significant direct effect on fatigue alone. Three symptom clusters, namely, pain fatigue, fatigue depression, and pain-fatigue depression were identified and found to have significant synergistic effects on quality of life. Our findings support the importance of managing clusters of symptoms simultaneously, that is, collective symptom management. Inter-cluster dynamics between symptoms should be considered when nurses develop symptom management strategies or self-management programs to improve the quality of life of rheumatoid arthritis patients.

Symptom Clusters and Their Impacts on Hospital Readmission in Patients With Heart Failure: A Cross-Sectional Study.

Readmissions after hospitalization due to multiple symptoms in heart failure (HF) are common and costly. Patients have difficulty differentiating HF symptoms from comorbid illness or aging. Therefore, early identification of symptom clusters could improve symptom recognition and reduce hospital readmission. However, little is known about the relationship between symptom clusters and readmission in HF patients. This study aimed to identify symptom clusters among Korean patients with HF and the relationship between symptom clusters and hospital readmission. This cross-sectional study included 306 HF outpatients within 12 months after discharge. Exploratory factor analysis was used to identify the symptom clusters. Multiple logistic regression analysis was used to examine the effect of symptom clusters on readmission, after adjusting for sociodemographic and clinical characteristics. Three symptom clusters were identified in HF patients: the "respiratory distress" cluster, "bodily pain and energy insufficiency" cluster, and "circulatory and gastrointestinal distress" cluster. Patients with class III or IV of HF functional class experienced three symptom clusters at a higher level. This study showed that the "bodily pain and energy insufficiency" cluster was the strongest predictor of hospital readmission in HF patients (adjusted odds ratio = 6.59, 95% confidence interval (CI) [1.29, 32.79]). A higher level of "bodily pain and energy insufficiency" cluster was associated with hospital readmission in Korean HF patients. Health-care providers should be encouraged to consider patients' cultural backgrounds to recognize differences in symptom clusters. Further studies are needed to evaluate symptom clusters across international cohorts and their impacts on patients' outcomes.

Vanderbilt head and neck symptom survey, version 2.0: Clinical and research utility for identification of symptom clusters and changes in symptoms over time

ObjectivesThe symptoms and functional defects following treatment for head and neck cancer (HNC) have been poorly defined. The purpose of this study was to examine the utility of the Vanderbilt Head and Neck Symptom Survey (VHNSS) version 2.0 to identify symptom clusters experienced by patients with HNC as well as assess reliability and sensitivity to change. Materials and methodsThe VHNSS 2.0 questionnaire was completed by 150 patients over three studies. Two studies utilized the survey at multiple time points. ResultsCluster analysis identified ten multi-item clusters and three single items. The internal consistency was good to excellent, with Cronbach’s alpha coefficient above 0.90 in five symptom clusters and above 0.70 in remaining clusters. Clusters demonstrated convergent and divergent validity with other measures. Symptom burden was lowest at baseline, peaked at the end of treatment then subsided over the following months. ConclusionsThe VHNSS 2.0 is a reliable and valid measure of acute and late toxicities in patients treated for HNC. The tool may be used in research and clinical practice to screen, to evaluate treatments, and to compare side effects of treatment regimens.

Identification of symptom clusters in cancer patients at palliative care clinic

Objective:Cancer patients often experience a large number of symptoms together. The aim of this study is to determine the symptom clusters in cancer patients at palliative care clinic.Methods:Hundred and seventy consecutive patients were enrolled in the study. Memorial Symptom Assessment Scale was used for symptom assessment of the patients.Results:The most experienced symptoms by the patients during the past week before hospitalization in palliative care clinic were lack of energy (95.4%), weight loss (91.2%), lack of appetite (89.4%), pain (88.2%), dry mouth (87.6%), feeling sad (87.6%), feeling nervous (82.9%), worrying (81.2%), and feeling irritable (80.6%). Five symptom clusters were defined. First cluster: pain, feeling nervous, dry mouth, worrying, feeling irritable, weight loss; second cluster: feeling drowsy, numbness/tingling in hands/feet, difficulty in sleeping, dizziness, constipation, I do not look like myself; third cluster: nausea, vomiting; fourth cluster: shortness of breath, difficulty in swallowing, cough, change in the way food tastes; and fifth cluster: feeling bloated, problems with urination, diarrhea, itching, mouth sores, hair loss, swelling of arm or legs, change in the skin.Conclusions:We encountered various symptom clusters in advanced cancer patients. Identification of symptom clusters and knowledge of cluster composition in oncological population may particularly contribute individualization of the treatment.

Differences in symptom clusters identified using symptom occurrence rates versus severity ratings in patients with breast cancer undergoing chemotherapy.

One of the unanswered questions in symptom clusters research is whether the number and types of symptom clusters vary based on the dimension of the symptom experience used to create the clusters. Given that patients with breast cancer receiving chemotherapy (CTX), report between 10 and 32 concurrent symptoms and studies of symptom clusters in these patients are limited, the purpose of this study, in breast cancer patients undergoing CTX (n=515), was to identify whether the number and types of symptom clusters differed based on whether symptom occurrence rates or symptom severity ratings were used to create the clusters. A modified version of the Memorial Symptom Assessment Scale was used to assess for the occurrence and severity of 38 symptoms, one week after the administration of CTX. Exploratory factor analysis was used to extract the symptom clusters. Both the number and types of symptom clusters were similar using symptom occurrence rates or symptom severity ratings. Five symptom clusters were identified using symptom occurrence rates (i.e., psychological, hormonal, nutritional, gastrointestinal, epithelial). Six symptom clusters (i.e., psychological, hormonal, nutritional, gastrointestinal, epithelial, chemotherapy neuropathy) were identified using symptom severity ratings. Across the two dimensions, the specific symptoms within each of the symptom clusters were similar. Identification of symptom clusters in patients with breast cancer may be useful in guiding symptom management interventions. Future studies are warranted to determine if symptom clusters remain stable over a cycle of CTX in patients with breast cancer.

Symptom clusters during palliative chemotherapy and their influence on functioning and quality of life.

Symptom management in patients undergoing palliative chemotherapy is challenging, as patients may suffer from cancer symptoms as well as symptoms related to chemotherapy. Symptom clusters are interrelated symptoms occurring simultaneously that exert a negative impact on survival. Identification of symptom clusters and effective symptom management for patients receiving palliative chemotherapy is crucial. The purpose of this study was to identify symptom clusters among cancer patients undergoing palliative chemotherapy and examine the relationship between symptom clusters and functioning and quality of life (QOL) in these patients. A total of 300 patients undergoing palliative chemotherapy participated in the study. Symptom experience during previous palliative chemotherapy was evaluated using a symptom questionnaire including 20 symptoms. The European Organization for Research and Treatment of Cancer Quality of Life-C30 was used to measure patients' symptoms, functioning and QOL. Factor and hierarchical cluster analyses were performed to identify symptom clusters. Structural equation modeling was used to identify relationships between symptom clusters, functioning and QOL in patients. Four symptoms clusters including 14 symptoms were identified of the 20 symptoms experienced by patients undergoing palliative chemotherapy. The emotional cluster had negative influence on role and social functioning. The nausea and vomiting/appetite/taste change cluster had a negative impact on role functioning. The fatigue/cognitive and other cluster (dyspnea, pain, constipation, neuropathy, and sleep disturbance) negatively influenced physical functioning. Among the functioning subscales, only role functioning had a direct influence on QOL. Indirect relationships between symptom clusters and QOL were mediated by physical and role functioning. The final model, which consisted of four symptom clusters and three functioning subscales, accounted for 23.3% of the variance in the QOL. Four symptom clusters occurring during palliative chemotherapy demonstrated negative influences on functioning and QOL. Differential contributions of symptom clusters to functioning were identified that eventually contributed to QOL through role functioning. The identified symptom clusters and their relationships with functioning and QOL may help guide approaches to symptom management. Implementation of interventions targeting symptom clusters would contribute to improving functioning and QOL in patients.

Cancer Symptom Cluster Management.

To discuss the importance of cancer symptom clusters in clinical practice, review evidence for symptom cluster interventions, and make recommendations for symptom cluster identification, patient education, and management in clinical practice. Primary research and review articles identified through CINAHL, PubMed, and PsycINFO databases. Several studies have investigated interventions for multi-symptom management or have evaluated the secondary effects of a single-symptom intervention on related symptoms. To date, only five studies have tested an intervention designed to manage a specific cancer symptom cluster. Those studies used nonpharmacologic approaches (psycho-education, cognitive-behavioral strategies, and acupressure) to address the pain, fatigue, and sleep disturbance symptom cluster, or the respiratory distress symptom cluster with some initial evidence of success. Further development and efficacy testing of symptom cluster interventions is needed. Clinical practice can be guided by knowledge of individual and multi-symptom management, and clinical judgment regarding possible etiologies of cancer symptom clusters. Clinicians should be aware of co-occurring symptoms in their patients, educate and involve patients in identifying symptom clusters and aggravating/alleviating factors, and coordinate treatment recommendations using strategies that are likely to be beneficial across symptoms.

Future Directions in Symptom Cluster Research.

To consider future directions for symptom cluster research. These considerations are organized using the two main conceptual approaches for symptom cluster research (ie, "de novo" identification of symptom clusters, and identification of subgroups of patients with distinct symptom experiences with a prespecified symptom cluster). Peer-reviewed journals and guidelines from professional organizations. Research on symptom clusters in oncology patients is growing at an exponential rate. However, numerous studies are needed to increase the growth of symptom management research. Findings from this area of symptom management science have the potential to improve the management of multiple co-occurring symptoms in oncology patients. In addition, mechanistic-based studies hold great promise to identify new biological and behavioral targets that will form the basis for subsequent intervention studies.