Mapping the EQ-5D index by UPDRS and PDQ-8 in patients with Parkinson's disease

• Published in: Health and quality of life outcomes 2013-03, Vol.11 (1), p.35-35, Article 35
• Main Authors: Dams, Judith, Klotsche, Jens, Bornschein, Bernhard, Reese, Jens P, Balzer-Geldsetzer, Monika, Winter, Yaroslav, Schrag, Anette, Siderowf, Andrew, Oertel, Wolfgang H, Deuschl, Günther, Siebert, Uwe, Dodel, Richard
• Format: Article
• Language: English
• Subjects: Diagnosis; Female; Humans; Male; Measurement; Medical economics; Medical research; Medicine, Experimental; Parkinson Disease - diagnosis; Parkinson's disease; Quality of Life; Regression analysis; Severity of Illness Index; Studies; Surveys and Questionnaires - standards
• ISSN: 1477-7525; 1477-7525
• DOI: 10.1186/1477-7525-11-35

Clinical studies employ the Unified Parkinson's Disease Rating Scale (UPDRS) to measure the severity of Parkinson's disease. Evaluations often fail to consider the health-related quality of life (HrQoL) or apply disease-specific instruments. Health-economic studies normally use estimates of utilities to calculate quality-adjusted life years. We aimed to develop an estimation algorithm for EuroQol- 5 dimensions (EQ-5D)-based utilities from the clinical UPDRS or disease-specific HrQoL data in the absence of original utilities estimates. Linear and fractional polynomial regression analyses were performed with data from a study of Parkinson's disease patients (n=138) to predict the EQ-5D index values from UPDRS and Parkinson's disease questionnaire eight dimensions (PDQ-8) data. German and European weights were used to calculate the EQ-5D index. The models were compared by R(2), the root mean square error (RMS), the Bayesian information criterion, and Pregibon's link test. Three independent data sets validated the models. The regression analyses resulted in a single best prediction model (R(2): 0.713 and 0.684, RMS: 0.139 and 13.78 for indices with German and European weights, respectively) consisting of UPDRS subscores II, III, IVa-c as predictors. When the PDQ-8 items were utilised as independent variables, the model resulted in an R2 of 0.60 and 0.67. The independent data confirmed the prediction models. The best results were obtained from a model consisting of UPDRS subscores II, III, IVa-c. Although a good model fit was observed, primary EQ-5D data are always preferable. Further validation of the prediction algorithm within large, independent studies is necessary prior to its generalised use.