Symptom Clusters in Breast Cancer Survivors: A Latent Class Profile Analysis.
OBJECTIVES: To identify symptom clusters in breast cancer survivors and to determine sociodemographic and clinical characteristics influencing symptom cluster membership. SAMPLE & SETTING: The authors performed a crosssectional secondary analysis of data obtained from a community-based cancer regist...
| Publicado en: | Oncology Nursing Forum Vol. 47; no. 1; pp. 89 - 101 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oncology Nursing Society
Jan2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=140438863&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140438863 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0190535X 4F0 jtl: Oncology Nursing Forum issn: 0190535X maglogo: N pubinfo: dt: Jan2020 vid: 47 iid: 1 pid: 12496 pub: Oncology Nursing Society place: Pittsburgh, Pennsylvania artinfo: ui: 140438863 140438863 140438863 10.1188/20.ONF.89-100 140438863 ppf: 89 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Symptom Clusters in Breast Cancer Survivors: A Latent Class Profile Analysis. aug: au: Lee, Lena J. Ross, Alyson Griffith, Kathleen Jensen, Roxanne E. Wallen, Gwenyth R. affil: Nurse scientists, National Institutes of Health Clinical Center in Bethesda, MD sug: subj: Breast Neoplasms Symptoms Cancer Survivors Psychosocial Factors Socioeconomic Factors Quality of Life Human Secondary Analysis Cross Sectional Studies Pain Fatigue Sleep Disorders Depression Cluster Analysis Structural Equation Modeling Coefficient alpha Descriptive Statistics Data Analysis Software Severity of Illness Comorbidity Confidence Intervals Odds Ratio Comparative Studies Factor Analysis Regression Patient-Reported Outcomes Clinical Assessment Tools ab: OBJECTIVES: To identify symptom clusters in breast cancer survivors and to determine sociodemographic and clinical characteristics influencing symptom cluster membership. SAMPLE & SETTING: The authors performed a crosssectional secondary analysis of data obtained from a community-based cancer registry-linked survey with 1,500 breast cancer survivors 6-13 months following a breast cancer diagnosis. METHODS & VARIABLES: Symptom clusters were identified using latent class profile analysis of four patient-reported symptoms (pain, fatigue, sleep disturbance, and depression) with custom PROMIS® short forms. RESULTS: Four distinct classes were identified: symptoms within normal limits (class 1), pain with fatigue and sleep disturbance (class 2), depression with fatigue and sleep disturbance (class 3), and all high symptom burden (class 4). The authors identified four clinically relevant and actionable symptom clusters in early-stage breast cancer survivorship. Certain sociodemographic and clinical characteristics place patients at risk for physical late effects and mental health issues. IMPLICATIONS FOR NURSING: Common symptom clusters may lead to better prevention and treatment strategies that target a group of symptoms. Results also suggest that certain factors place patients at high risk for symptom burden, which can guide tailored interventions. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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