Correlation between medication regimen complexity and quality of life in patients with heart failure

Wilkening, G. Lucy; Brune, Sonja; Saenz, Pablo F.; Vega, Lourdes; Kalich, Bethany A.

doi:10.1016/j.sapharm.2020.01.003

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…The MLHFQ is a self-administered, 21-item diseasespecific instrument for patients with heart failure [29]. MLHFQ is an instrument which has been widely used to assess quality of life among heart failure patients [30][31][32]. Each item is scored in a 6-point Likert Scale (0 to 5), thus the total score could range from 0 to 105, with higher scores indicating more significant impairment in health-related quality of life.…”

Section: Euroqol Five-dimensional Questionnaire (Eq-5d)mentioning

confidence: 99%

Mapping the Minnesota living with heart failure questionnaire (MLHFQ) to EQ-5D-5L in patients with heart failure

Kularatna

Senanayake

Chen

et al. 2020

Health Qual Life Outcomes

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Background: Mapping algorithms can be used to convert scores from a non-preference based instrument to health state utilities. The objective of this study was to develop mapping algorithms which will enable the Minnesota Living with Heart Failure Questionnaire (MLHFQ) scores to be converted into EQ-5D-5L utility scores that can be used in heart failure related cost utility studies. Method: Patients diagnosed with heart failure were recruited from Australia. Mapping algorithms were developed using both direct and indirect response mapping approach. Three model specifications were considered to predict the EQ-5D-5 L utility score using MLHFQ total score (Model 1), MLHFQ domain scores (Model 2), or MLHFQ item scores (Model 3). Six regression techniques, each of which has the capability to cope with either skewness, heteroscedasticity, ceiling effects and/or the potential presence of outliers in the data set were used to identify the optimal mapping functions for each of the three models. Goodness-of-fit of the models were assessed using six indicators. In the absence of an external validation dataset, predictive performance of was assessed using threefold cross validation method. In the indirect response mapping, EQ. 5D 5 L responses were predicted separately using the MLHFQ item scores using ordered logit model. Results: A total of 141 patients participated in the study. The lowest mean absolute error (MAE) was recorded from the multivariable fractional polynomials (MFP) model in all three-model specifications. Regarding the indirect response mapping, results showed that the performance was comparable with the direct mapping approach based on root mean squared error (RMSE) but was worse based on MAE. Conclusion: The MLHFQ can be mapped onto EQ-5D-5 L utilities with good predictive accuracy using both direct and indirect response mapping techniques. The reported mapping algorithms would facilitate calculation of health utility for economic evaluations related to heart failure.

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