Coronary Heart Disease Quality Of Life Research Articles

PRM143 - Meta-Analysis of Quality of Life in Coronary Heart Disease

Objectives: A large number of quality of life (QoL) values for patients with coronary heart disease (CHD) are available in the literature. From the abundance of QoL values in CHD, only preference-based values can be directly applied in cost-utility analysis (CUA). In this study we performed a multivariate meta-regression analysis of the preference-based QoL values in CHD. Our aim was to obtain a summarized QoL estimate while accounting for study-level covariates and for the correlation between multiple outcomes both within and between studies. Methods: Studies were considered eligible for this analysis if they reported mean QoL values, standard deviations and the instrument for measuring QoL (e.g. EQ-5D, SF-6D). Additionally studies reporting separate values of SF-36 and SF-12 QoL instruments were included. The scores of the SF-36 and SF-12 QoL were mapped onto EQ-5D and SF-6D values. Mapped preference-based values were added to the base-case data set that consisted only of published preference-based QoL values. Finally, a multivariate metaregression model was used in order to estimate pooled, instrument-specific QoL in CHD. Results: Our systematic search identified 35 eligible studies reporting 43 preference-based QoL values in CHD as well as 46 studies reporting QoL scores of SF-36 and SF-12. Following instrument-specific QoL estimates were obtained when conducting the analysis on the base-case data set: 0.77 (EQ-5D UK), 0.79 (EQ-5D US), 0.65 (SF-6D), 0.85 (15D), 0.44 (HUI3) and 0.63 (QWB). Age was the only studylevel covariate that showed an influence on instrument-specific QoL estimates. Significant heterogeneity was observed when the analysis was conducted on the expanded data set. Conclusions: This study represents the first meta-analysis of QoL in CHD. Given the abundance of QoL values in CHD on the one hand and the requirement for single, accurate QoL values in CUA on the other hand, pooled estimates of QoL could be highly applicable in CUA.