Mapping Minnesota Living with Heart Failure Questionnaire Onto EQ-5D-5L and SF-6Dv2 in Iranian Patients With Ischemic...

Disease-specific instruments such as the Minnesota Living with Heart Failure Questionnaire (MLHFQ) have high sensitivity, but general instruments, such as EuroQol 5-Dimension 5-Level (EQ-5D-5L) and Short-Form 6 Dimension (SF-6D_V2) are essential for calculating quality-adjusted life-years. This study was conducted to develop mapping algorithms for Iranian patients with ischemic heart failure.

In this cross-sectional study, 205 patients with ischemic heart failure from Tehran Heart Center were selected with inclusion criteria (age over 18 years, Iranian nationality, ability to complete the questionnaire). Data included MLHFQ scores, EQ-5D-5L, SF-6D_V2 utility values, and factors affecting health-related quality of life. Direct (ordinary least squares, generalized linear model, multivariate fractional polynomial (MFP), Tobit, and censored least absolute deviations) and indirect (Ordered Logit) mapping models with additional explanatory variables (eg, region of residence, disease duration) were compared. Evaluation criteria included mean absolute error, root mean square error, Akaike information criterion, Bayesian information criterion, average ranking value, and intraclass correlation coefficient, with 3-fold cross-validation.

The mean MLHFQ score was 35.82 (SD 22.34), EQ-5D-5L was 0.409 (SD 0.316), and SF-6D_V2 was 0.453 (SD 0.318). Strong negative Spearman correlations were found between MLHFQ scores and EQ-5D-5L (-0.819) and SF-6D_V2 (-0.842). The MFP model with physical/emotional dimensions and additional variables performed best (average ranking value =1 for EQ-5D-5L; 1.25 for SF-6D_V2). Among the direct mapping models, the MFP model including physical and emotional dimensions along with additional covariates performed best, outperforming indirect mapping.

The developed algorithms allow for the generation of accurate utility values from the MLHFQ and facilitate economic evaluations in Iran. This study addresses a cultural gap by using Iranian population data and value set, providing a foundation for future health economics evaluations in Iran.