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Mapping the Minnesota Living with Heart Failure Questionnaire (MLHFQ) onto the Assessment of Quality of Life 8D (AQoL-8D) utility scores

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Abstract

Purpose

The Minnesota Living with Heart Failure Questionnaire (MLHFQ) is a widely used condition-specific measure of quality of life (QoL) in patients with heart failure. To use information from the MLHFQ in an economic evaluation, the MLHFQ must be mapped onto a preference-based measure of QoL. This study aims to develop a mapping algorithm between the MLHFQ and the Assessment of Quality of Life (AQoL) 8D utility instrument in patients with dilated cardiomyopathy (DCM).

Methods

MLHFQ and AQoL-8D data were collected on 61 Australian adults with idiopathic DCM or other non-hypertrophic cardiomyopathies. Three statistical methods were used as follows: ordinary least squares (OLS) regression, the robust MM estimator, and the generalised linear models (GLM). Each included a range of explanatory variables. Model performance was assessed using key goodness-of-fit measures, the mean absolute error (MAE), and the root-mean-square error (RMSE).

Results

The MLHFQ summary score and AQoL-8D utility scores were strongly correlated (r =  − 0.83, p < 0.0001) and the two subscales of the MLHFQ were correlated with the eight dimensions of the AQoL-8D. Utility scores were predicted with acceptable precision based on responses to the MLHFQ physical, emotional, social, and other subscales. OLS and GLM performed similarly with MAE and RMSE ranging 0.086–0.106 and 0.114–0.130, respectively.

Conclusion

The mapping algorithm developed in this study allows the derivation of AQoL-8D utilities from MLHFQ scores for use in cost-effectiveness analyses and most importantly, enables the economic evaluation of alternative heart failure therapy options when only the MLHFQ has been collected.

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Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

DCM:

Dilated cardiomyopathy

QoL:

Quality of life

QALY:

Quality adjusted life year

MLHFQ:

Minnesota Living with Heart Failure Questionnaire

AQoL-8D:

Assessment of Quality of Life 8 Dimension

ICD:

Implantable cardioverter defibrillator

OLS:

Ordinary least squares

GLM:

Generalised linear models

MAE:

Mean absolute error

RMSE:

Root-mean-square error

References

  1. Hershberger, R. E., Givertz, M. M., Ho, C. Y., Judge, D. P., Kantor, P. F., McBride, K. L., et al. (2018). Genetic evaluation of cardiomyopathy: A clinical practice resource of the American College of Medical Genetics and Genomics (ACMG). Genet Med, 20(9), 899–909.

    Article  Google Scholar 

  2. Maron, B. J., Towbin, J. A., Thiene, G., Antzelevitch, C., Corrado, D., Arnett, D., Moss, A. J., Seidman, C. E., Young, J. B., American Heart Association; Council on Clinical Cardiology, Heart Failure and Transplantation Committee; Quality of Care and Outcomes Research and Functional Genomics and Translational Biology Interdisciplinary Working Groups; Council on Epidemiology and Prevention. (2006). Contemporary definitions and classification of the cardiomyopathies: an American Heart Association Scientific Statement from the Council on Clinical Cardiology, Heart Failure and Transplantation Committee; Quality of Care and Outcomes Research and Functional Genomics and Translational Biology Interdisciplinary Working Groups; & Council on Epidemiology and Prevention. Circulation, 113(14), 1807–1816.

  3. Petretta, M., Pirozzi, F., Sasso, L., Paglia, A., & Bonaduce, D. (2011). Review and metaanalysis of the frequency of familial dilated cardiomyopathy. The American Journal of Cardiology, 108(8), 1171–1176.

    Article  Google Scholar 

  4. Gigli, M., Stolfo, D., Merlo, M., Barbati, G., Ramani, F., Brun, F., et al. (2017). Insights into mildly dilated cardiomyopathy: Temporal evolution and long-term prognosis. European Journal of Heart Failure, 19(4), 531–539.

    Article  Google Scholar 

  5. Brazier, J., Ratcliffe, J., Saloman, J., & Tsuchiya, A. (2017). Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press.

    Google Scholar 

  6. Riegel, B., Moser, D. K., Glaser, D., Carlson, B., Deaton, C., Armola, R., et al. (2002). The Minnesota Living With Heart Failure Questionnaire: sensitivity to differences and responsiveness to intervention intensity in a clinical population. Nursing Research, 51(4), 209–218.

    Article  Google Scholar 

  7. Heo, S., Moser, D. K., Riegel, B., Hall, L. A., & Christman, N. (2005). Testing the psychometric properties of the Minnesota Living with Heart Failure questionnaire. Nursing Research, 54(4), 265–272.

    Article  Google Scholar 

  8. Rector, T. S., & Cohn, J. N. (1992). Assessment of patient outcome with the Minnesota Living with Heart Failure questionnaire: reliability and validity during a randomized, double-blind, placebo-controlled trial of pimobendan. American Heart Journal, 124(4), 1017–1025.

    Article  CAS  Google Scholar 

  9. Dakin, H., Abel, L., Burns, R., & Yang, Y. (2018). Review and critical appraisal of studies mapping from quality of life or clinical measures to EQ-5D: An online database and application of the MAPS statement. Health and Quality of Life Outcomes, 16(1), 31.

    Article  Google Scholar 

  10. Gaff, C. L., Winship, I. M., Forrest, S. M., Hansen, D. P., Clark, J., Waring, P. M., et al. (2017). Preparing for genomic medicine: A real world demonstration of health system change. NPJ Genomic Medicine, 2(1), 16.

    Article  Google Scholar 

  11. Yang, Y., Muzny, D. M., Reid, J. G., Bainbridge, M. N., Willis, A., Ward, P. A., et al. (2013). Clinical whole-exome sequencing for the diagnosis of Mendelian disorders. New England Journal of Medicine, 369(16), 1502–1511.

    Article  CAS  Google Scholar 

  12. Ramchand, J., Wallis, M., Macciocca, I., Lynch, E., Farouque, O., Martyn, M., et al. (2020). Prospective evaluation of the utility of whole exome sequencing in dilated cardiomyopathy. Journal of American Heart Association, 9(2), e013346.

    Article  CAS  Google Scholar 

  13. Catchpool, M., Ramchand, J., Martyn, M., Hare, D. L., James, P. A., Trainer, A. H., et al. (2019). A cost-effectiveness model of genetic testing and periodical clinical screening for the evaluation of families with dilated cardiomyopathy. Genetic Medicine, 21(12), 2815–2822.

    Article  Google Scholar 

  14. Munyombwe, T., Höfer, S., Fitzsimons, D., Thompson, D. R., Lane, D., Smith, K., et al. (2014). An evaluation of the Minnesota Living with Heart Failure Questionnaire using Rasch analysis. Quality of Life Research, 23(6), 1753–1765.

    Article  Google Scholar 

  15. Bilbao, A., Escobar, A., García-Perez, L., Navarro, G., & Quirós, R. (2016). The Minnesota living with heart failure questionnaire: Comparison of different factor structures. Health and Quality of Life Outcomes, 14(1), 23.

    Article  Google Scholar 

  16. Richardson, J., Iezzi, A., Khan, M. A., & Maxwell, A. (2014). Validity and reliability of the Assessment of Quality of Life (AQoL)-8D multi-attribute utility instrument. The Patient-Patient-Centered Outcomes Research, 7(1), 85–96.

    Article  Google Scholar 

  17. Coelho, R., Ramos, S., Prata, J., Bettencourt, P., Ferreira, A., & Cerqueira-Gomes, M. (2005). Heart failure and health related quality of life. Clinical Practice and Epidemiology in Mental Health, 1(1), 19.

    Article  Google Scholar 

  18. Payakachat, N., Ali, M. M., & Tilford, J. M. (2015). A systematic review. Pharmacoeconomics, 33(11), 1137–1154.

    Article  Google Scholar 

  19. Richardson, J., Khan, M. A., Chen, G., Iezzi, A., & Maxwell, A. (2012). Population norms and Australian profile using the Assessment of Quality of Life (AQoL) 8D utility instrument. Centre for Health Economics Research Paper.

  20. Gottlieb, S. S., Khatta, M., Friedmann, E., Einbinder, L., Katzen, S., Baker, B., et al. (2004). The influence of age, gender, and race on the prevalence of depression in heart failure patients. Journal of the American College of Cardiology, 43(9), 1542–1549.

    Article  Google Scholar 

  21. Wailoo, A. J., Hernandez-Alava, M., Manca, A., Mejia, A., Ray, J., Crawford, B., et al. (2017). Mapping to estimate health-state utility from non-preference-based outcome measures: An ISPOR good practices for outcomes research task force report. Value in Health, 20(1), 18–27.

    Article  Google Scholar 

  22. Rivero-Arias, O., Ouellet, M., Gray, A., Wolstenholme, J., Rothwell, P. M., & Luengo-Fernandez, R. (2010). Mapping the Modified Rankin Scale (mRS) measurement into the generic EuroQol (EQ-5D) health outcome. Medical Decision Making, 30(3), 341–354.

    Article  Google Scholar 

  23. Hutcheson, G. D. (2011). Ordinary least-squares regression. In L. Moutinho & G. D. Hutcheson (Eds.), The SAGE dictionary of quantitative management research (pp. 224–228). Thousand Oaks: SAGE Publications.

    Google Scholar 

  24. Ingles, J., Yeates, L., Hunt, L., McGaughran, J., Scuffham, P. A., Atherton, J., et al. (2013). Health status of cardiac genetic disease patients and their at-risk relatives. International Journal of Cardiology, 165(3), 448–453.

    Article  Google Scholar 

  25. Ingles, J., McGaughran, J., Scuffham, P. A., Atherton, J., & Semsarian, C. (2012). A cost-effectiveness model of genetic testing for the evaluation of families with hypertrophic cardiomyopathy. Heart, 98(8), 625–630.

    Article  Google Scholar 

  26. Kularatna, S., Byrnes, J., Chan, Y. K., Carrington, M. J., Stewart, S., & Scuffham, P. A. (2017). Comparison of contemporaneous responses for EQ-5D-3L and Minnesota Living with Heart Failure: A case for disease specific multiattribute utility instrument in cardiovascular conditions. International Journal of Cardiology, 227, 172–176.

    Article  Google Scholar 

  27. Engel, L., Mortimer, D., Bryan, S., Lear, S. A., & Whitehurst, D. G. (2017). An investigation of the overlap between the ICECAP-A and five preference-based health-related quality of life instruments. PharmacoEconomics, 35(7), 741–753.

    Article  Google Scholar 

  28. Kontodimopoulos, N., Aletras, V. H., Paliouras, D., & Niakas, D. (2009). Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ-5D, SF-6D, and 15D instruments. Value in Health, 12(8), 1151–1157.

    Article  Google Scholar 

  29. Hollingworth, W., Campbell, J. D., Kowalski, J., Ravelo, A., Girod, I., Briggs, A., et al. (2010). Exploring the impact of changes in neurogenic urinary incontinence frequency and condition-specific quality of life on preference-based outcomes. Quality of Life Research, 19(3), 323–331.

    Article  Google Scholar 

  30. Mukuria, C., Rowen, D., Harnan, S., Rawdin, A., Wong, R., Ara, R., et al. (2019). An updated systematic review of studies mapping (or cross-walking) measures of health-related quality of life to generic preference-based measures to generate utility values. Applied Health Economics and Health Policy, 17(3), 295–313.

    Article  Google Scholar 

  31. Jiang, W., Alexander, J., Christopher, E., Kuchibhatla, M., Gaulden, L. H., Cuffe, M. S., et al. (2001). Relationship of depression to increased risk of mortality and rehospitalization in patients with congestive heart failure. Archives of Internal Medicine, 161(15), 1849–1856.

    Article  CAS  Google Scholar 

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Funding

The study was funded by the members of the Melbourne Genomics Health Alliance and the State Government of Victoria (Department of Health and Human Services).

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Authors and Affiliations

Authors

Contributions

MC led on the analysis of the data with support from IG. JR is the clinical lead for the project. MM led design of the patient surveys used and supervised data entry and cleaning. JR, DH, MM contributed to the design of the study. All authors have contributed to the manuscript and have read and approved the final version.

Corresponding author

Correspondence to Ilias Goranitis.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study received Human Research Ethics Committee approval (HREC/ 13/MH/326) and complied with the Declaration of Helsinki. Written, informed consent to participate in the study was obtained from all participants.

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Catchpool, M., Ramchand, J., Hare, D.L. et al. Mapping the Minnesota Living with Heart Failure Questionnaire (MLHFQ) onto the Assessment of Quality of Life 8D (AQoL-8D) utility scores. Qual Life Res 29, 2815–2822 (2020). https://doi.org/10.1007/s11136-020-02531-4

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