2.50
Hdl Handle:
http://hdl.handle.net/10755/165293
Category:
Abstract
Type:
Presentation
Title:
SYMPTOM CLUSTERS IN ONCOLOGY OUTPATIENTS EFFECT PATIENT OUTCOMES
Author(s):
Miaskowski, Chrisitine; Dodd, Marylin; Lee, Kathryn; West, Claudia; Cooper, Bruce; Paul, Steven; Aouizerat, Brad
Author Details:
Christine Miaskowski, RN, PhD, FAAN, School of Nursing, San Francisco, California, USA; Marylin Dodd, RN, PhD, FAAN; Kathryn Lee, RN, PhD, FAAN; Claudia West, RN, MS; Bruce Cooper, PhD; Steven Paul, PhD; Brad Aouizerat, PhD
Abstract:
Recent evidence suggests that multiple symptoms can have a negative effect on patient outcomes. However, no studies of oncology outpatients have attempted to cluster patients based on reports of symptom intensity and to evaluate for differences in patient outcomes based on cluster group membership. The purposes of this study, with a sample of oncology outpatients who were receiving active treatment for their cancer (n=191) were to determine cluster membership based on self-reports of pain, fatigue, sleep disturbance, and depression and to evaluate for differences in functional status and quality of life among the different cluster groups. The UCSF Symptom Management Model served as the theoretical framework for this study as well as the conceptualization of symptom clusters reported by Dodd, Miaskowski, and Paul, 2001. A cross-sectional sample of oncology patients was recruited from four outpatient settings. Patients completed the Lee Fatigue Scale, General Sleep Disturbance Scale, Center for Epidemiological Studies Depression Scale, Karnofsky Performance Status Score (KPS) and Multidimensional Quality of Life Scale-Cancer. If patients were experiencing pain, they rated their worst pain using a 0 to 10 numeric rating scale for pain intensity. Standardized scores for each of the symptoms were derived and were used as the dependent variables in the weighted average linkage hierarchical cluster analysis. The analysis revealed a four cluster solution (i.e., LOW on all symptoms (n=46), no pain and moderate levels of fatigue, sleep disturbance, and depression (n=68), high pain and moderate levels of fatigue, sleep disturbance, and depression (n=54), and HIGH on all symptoms (n=23)). Patients who were clustered in the LOW on all symptoms group had the best outcomes (i.e., highest KPS score and highest quality of life score). Patients who were clustered in the HIGH on all symptoms group had the worst outcomes. This study is the first to use cluster analysis to determine groupings of oncology outpatients based on self-reports of symptom intensity and to evaluate for differences in patient outcomes based on these cluster groupings. These data suggest that patients with high levels of symptoms experience poorer outcomes and may warrant different types of nursing interventions.
Repository Posting Date:
27-Oct-2011
Date of Publication:
27-Oct-2011
Conference Date:
2005
Conference Name:
30th Annual Oncology Nursing Society Congress
Conference Host:
Oncology Nursing Society
Conference Location:
Orlando, Florida, USA
Sponsors:
Funding Sources: National Institute of Nursing Research and National Cancer Institute.
Note:
This is an abstract-only submission. If the author has submitted a full-text item based on this abstract, you may find it by browsing the Virginia Henderson Global Nursing e-Repository by author. If author contact information is available in this abstract, please feel free to contact him or her with your queries regarding this submission. Alternatively, please contact the conference host, journal, or publisher (according to the circumstance) for further details regarding this item. If a citation is listed in this record, the item has been published and is available via open-access avenues or a journal/database subscription. Contact your library for assistance in obtaining the as-published article.

Full metadata record

DC FieldValue Language
dc.type.categoryAbstracten_US
dc.typePresentationen_GB
dc.titleSYMPTOM CLUSTERS IN ONCOLOGY OUTPATIENTS EFFECT PATIENT OUTCOMESen_GB
dc.contributor.authorMiaskowski, Chrisitineen_US
dc.contributor.authorDodd, Marylinen_US
dc.contributor.authorLee, Kathrynen_US
dc.contributor.authorWest, Claudiaen_US
dc.contributor.authorCooper, Bruceen_US
dc.contributor.authorPaul, Stevenen_US
dc.contributor.authorAouizerat, Braden_US
dc.author.detailsChristine Miaskowski, RN, PhD, FAAN, School of Nursing, San Francisco, California, USA; Marylin Dodd, RN, PhD, FAAN; Kathryn Lee, RN, PhD, FAAN; Claudia West, RN, MS; Bruce Cooper, PhD; Steven Paul, PhD; Brad Aouizerat, PhDen_US
dc.identifier.urihttp://hdl.handle.net/10755/165293-
dc.description.abstractRecent evidence suggests that multiple symptoms can have a negative effect on patient outcomes. However, no studies of oncology outpatients have attempted to cluster patients based on reports of symptom intensity and to evaluate for differences in patient outcomes based on cluster group membership. The purposes of this study, with a sample of oncology outpatients who were receiving active treatment for their cancer (n=191) were to determine cluster membership based on self-reports of pain, fatigue, sleep disturbance, and depression and to evaluate for differences in functional status and quality of life among the different cluster groups. The UCSF Symptom Management Model served as the theoretical framework for this study as well as the conceptualization of symptom clusters reported by Dodd, Miaskowski, and Paul, 2001. A cross-sectional sample of oncology patients was recruited from four outpatient settings. Patients completed the Lee Fatigue Scale, General Sleep Disturbance Scale, Center for Epidemiological Studies Depression Scale, Karnofsky Performance Status Score (KPS) and Multidimensional Quality of Life Scale-Cancer. If patients were experiencing pain, they rated their worst pain using a 0 to 10 numeric rating scale for pain intensity. Standardized scores for each of the symptoms were derived and were used as the dependent variables in the weighted average linkage hierarchical cluster analysis. The analysis revealed a four cluster solution (i.e., LOW on all symptoms (n=46), no pain and moderate levels of fatigue, sleep disturbance, and depression (n=68), high pain and moderate levels of fatigue, sleep disturbance, and depression (n=54), and HIGH on all symptoms (n=23)). Patients who were clustered in the LOW on all symptoms group had the best outcomes (i.e., highest KPS score and highest quality of life score). Patients who were clustered in the HIGH on all symptoms group had the worst outcomes. This study is the first to use cluster analysis to determine groupings of oncology outpatients based on self-reports of symptom intensity and to evaluate for differences in patient outcomes based on these cluster groupings. These data suggest that patients with high levels of symptoms experience poorer outcomes and may warrant different types of nursing interventions.en_GB
dc.date.available2011-10-27T12:15:57Z-
dc.date.issued2011-10-27en_GB
dc.date.accessioned2011-10-27T12:15:57Z-
dc.conference.date2005en_US
dc.conference.name30th Annual Oncology Nursing Society Congressen_US
dc.conference.hostOncology Nursing Societyen_US
dc.conference.locationOrlando, Florida, USAen_US
dc.description.sponsorshipFunding Sources: National Institute of Nursing Research and National Cancer Institute.-
dc.description.noteThis is an abstract-only submission. If the author has submitted a full-text item based on this abstract, you may find it by browsing the Virginia Henderson Global Nursing e-Repository by author. If author contact information is available in this abstract, please feel free to contact him or her with your queries regarding this submission. Alternatively, please contact the conference host, journal, or publisher (according to the circumstance) for further details regarding this item. If a citation is listed in this record, the item has been published and is available via open-access avenues or a journal/database subscription. Contact your library for assistance in obtaining the as-published article.-
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