Asha Mathew1, Amit Jiwan Tirkey2, Hongjin Li3, Alana Steffen4, Mark B Lockwood4, Crystal L Patil4, Ardith Z Doorenbos5. 1. College of Nursing, University of Illinois, Chicago; Christian Medical College, Vellore, India. Electronic address: asolom8@uic.edu. 2. Christian Medical College, Vellore, India. 3. College of Nursing, University of Illinois, Chicago; Christian Medical College, Vellore, India. 4. College of Nursing, University of Illinois, Chicago. 5. College of Nursing, University of Illinois, Chicago; University of Illinois Cancer Center, Chicago.
Abstract
OBJECTIVE: The two approaches to symptom-cluster research include grouping symptoms and grouping patients. The objective of this systematic review was to examine the conceptual approaches and methodologies used in symptom-cluster research in patients with head and neck cancer. DATA SOURCES: Articles were retrieved from electronic databases (CINAHL, MEDLINE via Ovid, APA PsycINFO, Scopus, Embase, and Cochrane Central Register of Controlled Trials-CENTRAL), five grey literature portals, and Google Scholar. Seventeen studies met the eligibility criteria. Eight studies grouped symptoms to identify symptom clusters, of which two used qualitative methods. The number of symptom clusters ranged from two to five, and the number of symptoms in a cluster ranged from 2 to 11. Nine studies grouped patients based on their experiences with multiple symptoms. Cluster analysis and factor analysis were most commonly used. Despite variable names and composition of symptom clusters, synthesis revealed three prominent symptom clusters: general, head and neck cancer-specific, and gastrointestinal. Being female and quality of life were significantly associated with high symptom group or cluster severity. Biological mechanisms were sparsely examined. CONCLUSION: Symptom cluster research in head and neck cancer is emerging. Consensus on nomenclature of a symptom cluster will facilitate deduction of core clinically relevant symptom clusters in head and neck cancer. Further research is required on understanding patients' subjective experiences, identifying predictors and outcomes, and underlying mechanisms for symptom clusters. IMPLICATIONS FOR NURSING PRACTICE: Identification of clinically relevant symptom clusters would enable targeted symptom assessment and management strategies, thus improving treatment efficiencies and patient outcomes.
OBJECTIVE: The two approaches to symptom-cluster research include grouping symptoms and grouping patients. The objective of this systematic review was to examine the conceptual approaches and methodologies used in symptom-cluster research in patients with head and neck cancer. DATA SOURCES: Articles were retrieved from electronic databases (CINAHL, MEDLINE via Ovid, APA PsycINFO, Scopus, Embase, and Cochrane Central Register of Controlled Trials-CENTRAL), five grey literature portals, and Google Scholar. Seventeen studies met the eligibility criteria. Eight studies grouped symptoms to identify symptom clusters, of which two used qualitative methods. The number of symptom clusters ranged from two to five, and the number of symptoms in a cluster ranged from 2 to 11. Nine studies grouped patients based on their experiences with multiple symptoms. Cluster analysis and factor analysis were most commonly used. Despite variable names and composition of symptom clusters, synthesis revealed three prominent symptom clusters: general, head and neck cancer-specific, and gastrointestinal. Being female and quality of life were significantly associated with high symptom group or cluster severity. Biological mechanisms were sparsely examined. CONCLUSION: Symptom cluster research in head and neck cancer is emerging. Consensus on nomenclature of a symptom cluster will facilitate deduction of core clinically relevant symptom clusters in head and neck cancer. Further research is required on understanding patients' subjective experiences, identifying predictors and outcomes, and underlying mechanisms for symptom clusters. IMPLICATIONS FOR NURSING PRACTICE: Identification of clinically relevant symptom clusters would enable targeted symptom assessment and management strategies, thus improving treatment efficiencies and patient outcomes.
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