Key result
A mapping model incorporating age, gender, disease stage, and Seattle Angina Questionnaire scales explained 48% of the variance in the EQ-5D index, but prediction accuracy was poor for index values below 0.4.
Why the study?
Can demographic and clinical outcome variables, including the Seattle Angina Questionnaire (SAQ), be used to accurately predict the EQ-5D index in patients with coronary heart disease?
Observational (n=4,742)
Yes
Can demographic and clinical outcome variables, including the Seattle Angina Questionnaire (SAQ), be used to accurately predict the EQ-5D index in patients with coronary heart disease?
Mapping the EQ-5D index from demographic and clinical variables in cardiac patients is feasible and explains nearly half of the variance, but prediction accuracy is poor for patients in severe health states.
Answers are grounded in this paper and cite their sources.
Mapping from SAQ may support CHD cost-effectiveness analyses but not in severe states; leaves open refined algorithms for low utilities.
BACKGROUND: The EuroQoL 5D (EQ-5D) is a questionnaire that provides a measure of utility for cost-effectiveness analysis. The EQ-5D has been widely used in many patient groups, including those with coronary heart disease. Studies often require patients to complete many questionnaires and the EQ-5D may not be gathered. This study aimed to assess whether demographic and clinical outcome variables, including scores from a disease specific measure, the Seattle Angina Questionnaire (SAQ), could be used to predict, or map, the EQ-5D index value where it is not available. METHODS: Patient-level data from 5 studies of cardiac interventions were used. The data were split into two groups - approximately 60% of the data were used as an estimation dataset for building models, and 40% were used as a validation dataset. Forward ordinary least squares linear regression methods and measures of prediction error were used to build a model to map to the EQ-5D index. Age, sex, a proxy measure of disease stage, Canadian Cardiovascular Society (CCS) angina severity class, treadmill exercise time (ETT) and scales of the SAQ were examined. RESULTS: The exertional capacity (ECS), disease perception (DPS) and anginal frequency scales (AFS) of the SAQ were the strongest predictors of the EQ-5D index and gave the smallest root mean square errors. A final model was chosen with age, gender, disease stage and the ECS, DPS and AFS scales of the SAQ. ETT and CCS did not improve prediction in the presence of the SAQ scales. Bland-Altman agreement between predicted and observed EQ-5D index values was reasonable for values greater than 0.4, but below this level predicted values were higher than observed. The 95% limits of agreement were wide (-0.34, 0.33). CONCLUSIONS: Mapping of the EQ-5D index in cardiac patients from demographics and commonly measured cardiac outcome variables is possible; however, prediction for values of the EQ-5D index below 0.4 was not accurate. The newly designed 5-level version of the EQ-5D with its increased ability to discriminate health states may improve prediction of EQ-5D index values.
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Goldsmith et al. (2010) conducted an observational in Coronary heart disease (n=4,742). Demographic and clinical outcome variables (SAQ scales, disease stage) was evaluated on Prediction of EQ-5D index (Model 11) (Adjusted R2 0.48, RMSE 0.170). A mapping model incorporating age, gender, disease stage, and Seattle Angina Questionnaire scales explained 48% of the variance in the EQ-5D index, but prediction accuracy was poor for index values below 0.4.
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