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Mapping Quality of Life (EQ-5D) from DAPsA, Clinical DAPsA and HAQ in Psoriatic Arthritis
T. Mlcoch, J. Tuzil, L. Sedova, J. Stolfa, M. Urbanova, D. Suchy, A. Smrzova, J. Jircikova, T. Hrnciarova, K. Pavelka, T. Dolezal,
Language English Country New Zealand
Document type Journal Article, Multicenter Study
NLK
ProQuest Central
from 2008-01-01 to 1 year ago
Nursing & Allied Health Database (ProQuest)
from 2008-01-01 to 1 year ago
Health & Medicine (ProQuest)
from 2008-01-01 to 1 year ago
Family Health Database (ProQuest)
from 2008-01-01 to 1 year ago
Public Health Database (ProQuest)
from 2008-01-01 to 1 year ago
- MeSH
- Adult MeSH
- Quality of Life psychology MeSH
- Quality-Adjusted Life Years * MeSH
- Middle Aged MeSH
- Humans MeSH
- Disability Evaluation MeSH
- Prospective Studies MeSH
- Cross-Sectional Studies MeSH
- Arthritis, Psoriatic psychology MeSH
- Aged MeSH
- Severity of Illness Index MeSH
- Health Status * MeSH
- Health Surveys statistics & numerical data MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Multicenter Study MeSH
BACKGROUND: Clinical trials and observational studies lacking measures of health-related quality of life (QoL) are often inapplicable when conducting cost-effectiveness analyses using quality-adjusted life-years (QALYs). The only solution is to map QoL ex post from additionally collected clinical outcomes and generic QoL instruments. Nonetheless, mapping studies are absent in psoriatic arthritis (PsA). METHODS: In this 2-year, prospective, multicentre, non-interventional study of PsA patients, EQ-5D and key clinical parameters such as Disease Activity in PsA (DAPsA), clinical DAPsA (cDAPsA; DAPsA without C-reactive protein [CRP]), and Health Assessment Questionnaire disability index (HAQ) were collected. We employed a linear mixed-effect regression model (ME) of the longitudinal dataset to explore the best predictors of QoL. RESULTS: A total of 228 patients were followed over 873 appointments/observations. DAPsA, cDAPsA and HAQ were stable and highly significant predictors of EQ-5D utilities in both cross-sectional and longitudinal analyses. The best prediction was provided using a linear ME with HAQ and cDAPsA or DAPsA. A HAQ increase of 1 point represented a decrease in EQ-5D by -0.204 or -0.203 (p < 0.0001); a one-point increase in cDAPsA or DAPsA dropped EQ-5D equally by -0.005 (p < 0.0001). The ME revealed steeper and more accurate association compared with cross-sectional regressions or non-linear models/transformations. CONCLUSIONS: This is the first mapping study conducted in PsA and we hope that our study will encourage further mapping studies in PsA. The results showed that in cases where CRP is absent, cDAPsA provides similar results to DAPsA in predicting QoL.
3rd Internal Clinic University Hospital Olomouc Olomouc Czech Republic
Department of Clinical Pharmacology Rheumatology University Hospital Plzen Plzen Czech Republic
References provided by Crossref.org
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- $a BACKGROUND: Clinical trials and observational studies lacking measures of health-related quality of life (QoL) are often inapplicable when conducting cost-effectiveness analyses using quality-adjusted life-years (QALYs). The only solution is to map QoL ex post from additionally collected clinical outcomes and generic QoL instruments. Nonetheless, mapping studies are absent in psoriatic arthritis (PsA). METHODS: In this 2-year, prospective, multicentre, non-interventional study of PsA patients, EQ-5D and key clinical parameters such as Disease Activity in PsA (DAPsA), clinical DAPsA (cDAPsA; DAPsA without C-reactive protein [CRP]), and Health Assessment Questionnaire disability index (HAQ) were collected. We employed a linear mixed-effect regression model (ME) of the longitudinal dataset to explore the best predictors of QoL. RESULTS: A total of 228 patients were followed over 873 appointments/observations. DAPsA, cDAPsA and HAQ were stable and highly significant predictors of EQ-5D utilities in both cross-sectional and longitudinal analyses. The best prediction was provided using a linear ME with HAQ and cDAPsA or DAPsA. A HAQ increase of 1 point represented a decrease in EQ-5D by -0.204 or -0.203 (p < 0.0001); a one-point increase in cDAPsA or DAPsA dropped EQ-5D equally by -0.005 (p < 0.0001). The ME revealed steeper and more accurate association compared with cross-sectional regressions or non-linear models/transformations. CONCLUSIONS: This is the first mapping study conducted in PsA and we hope that our study will encourage further mapping studies in PsA. The results showed that in cases where CRP is absent, cDAPsA provides similar results to DAPsA in predicting QoL.
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