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

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-...

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Autores principales: Kularatna, Sanjeewa, Senanayake, Sameera, Chen, Gang, Parsonage, William
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7189529/
https://www.ncbi.nlm.nih.gov/pubmed/32349782
http://dx.doi.org/10.1186/s12955-020-01368-2
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author Kularatna, Sanjeewa
Senanayake, Sameera
Chen, Gang
Parsonage, William
author_facet Kularatna, Sanjeewa
Senanayake, Sameera
Chen, Gang
Parsonage, William
author_sort Kularatna, Sanjeewa
collection PubMed
description 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 three-fold 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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spelling pubmed-71895292020-05-04 Mapping the Minnesota living with heart failure questionnaire (MLHFQ) to EQ-5D-5L in patients with heart failure Kularatna, Sanjeewa Senanayake, Sameera Chen, Gang Parsonage, William Health Qual Life Outcomes Research 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 three-fold 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. BioMed Central 2020-04-29 /pmc/articles/PMC7189529/ /pubmed/32349782 http://dx.doi.org/10.1186/s12955-020-01368-2 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Kularatna, Sanjeewa
Senanayake, Sameera
Chen, Gang
Parsonage, William
Mapping the Minnesota living with heart failure questionnaire (MLHFQ) to EQ-5D-5L in patients with heart failure
title Mapping the Minnesota living with heart failure questionnaire (MLHFQ) to EQ-5D-5L in patients with heart failure
title_full Mapping the Minnesota living with heart failure questionnaire (MLHFQ) to EQ-5D-5L in patients with heart failure
title_fullStr Mapping the Minnesota living with heart failure questionnaire (MLHFQ) to EQ-5D-5L in patients with heart failure
title_full_unstemmed Mapping the Minnesota living with heart failure questionnaire (MLHFQ) to EQ-5D-5L in patients with heart failure
title_short Mapping the Minnesota living with heart failure questionnaire (MLHFQ) to EQ-5D-5L in patients with heart failure
title_sort mapping the minnesota living with heart failure questionnaire (mlhfq) to eq-5d-5l in patients with heart failure
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7189529/
https://www.ncbi.nlm.nih.gov/pubmed/32349782
http://dx.doi.org/10.1186/s12955-020-01368-2
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