Mapping Kansas City cardiomyopathy, Seattle Angina, and minnesota living with heart failure to the MacNew-7D in patients with heart disease

Abstract

Introduction: The Kansas City Cardiomyopathy Questionnaire (KCCQ), Seattle Angina Questionnaire (SAQ), and Minnesota Living with Heart Failure Questionnaire (MLHFQ) are widely used non-preference-based instruments that measure health-related quality of life (QOL) in people with heart disease. However, currently it is not possible to estimate quality-adjusted life-years (QALYs) for economic evaluation using these instruments as the summary scores produced are not preference-based. The MacNew-7D is a heart disease-specific preference-based instrument. This study provides different mapping algorithms for allocating utility scores to KCCQ, MLHFQ, and SAQ from MacNew-7D to calculate QALYs for economic evaluations.

Methods: The study included 493 participants with heart failure or angina who completed the KCCQ, MLHFQ, SAQ, and MacNew-7D questionnaires. Regression techniques, namely, Gamma Generalized Linear Model (GLM), Bayesian GLM, Linear regression with stepwise selection and Random Forest were used to develop direct mapping algorithms. Cross-validation was employed due to the absence of an external validation dataset. The study followed the Mapping onto Preference-based measures reporting Standards checklist.

Results: The best models to predict MacNew-7D utility scores were determined using KCCQ, MLHFQ, and SAQ item and domain scores. Random Forest performed well for item scores for all questionnaires and domain score for KCCQ, while Bayesian GLM and Linear Regression were best for MLHFQ and SAQ domain scores. However, models tended to over-predict severe health states.

Conclusion: The three cardiac-specific non-preference-based QOL instruments can be mapped onto MacNew-7D utilities with good predictive accuracy using both direct response mapping techniques. The reported mapping algorithms may facilitate estimation of health utility for economic evaluations that have used these QOL instruments.

Keywords: Cross walk; MacNew-7D; Mapping; Preference-based instruments; Utility.

Fig. 1

Scatter plot of observed versus predicted Mac New 7D. Line of equality between observed and predicted values (solid line). RF = Random Forest; BGLM = Bayesian GLM; LR = Linear Regression

Fig. 2

Bland and Altman plot of differences between the actual and the predicted Mac New 7D utility scores. RF = Random Forest; BGLM = Bayesian GLM; LR = Linear Regression

Fig. 3

Cumulative distribution function of observed and simulated MacNew-7D values of the best performing models