Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, QLD, 4059, Australia.
Qual Life Res. 2024 Aug;33(8):2151-2163. doi: 10.1007/s11136-024-03676-2. Epub 2024 Jun 5.
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.
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.
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.
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.
堪萨斯城心肌病问卷(KCCQ)、西雅图心绞痛问卷(SAQ)和明尼苏达州心力衰竭生活质量问卷(MLHFQ)是广泛使用的非偏好基础工具,用于测量心脏病患者的健康相关生活质量(QOL)。然而,目前使用这些工具无法估计质量调整生命年(QALYs)进行经济评估,因为产生的综合评分不是偏好基础。MacNew-7D 是一种特定于心脏病的偏好基础工具。本研究提供了不同的映射算法,用于将效用评分从 MacNew-7D 分配给 KCCQ、MLHFQ 和 SAQ,以计算经济评估的 QALYs。
该研究纳入了 493 名心力衰竭或心绞痛患者,他们完成了 KCCQ、MLHFQ、SAQ 和 MacNew-7D 问卷。回归技术,即伽马广义线性模型(GLM)、贝叶斯 GLM、逐步选择的线性回归和随机森林,用于开发直接映射算法。由于没有外部验证数据集,因此使用了交叉验证。该研究遵循映射到偏好基础衡量报告标准清单。
使用 KCCQ、MLHFQ 和 SAQ 的项目和域评分确定了预测 MacNew-7D 效用评分的最佳模型。随机森林在所有问卷的项目评分和 KCCQ 的域评分方面表现良好,而贝叶斯 GLM 和线性回归在 MLHFQ 和 SAQ 的域评分方面表现最佳。然而,模型往往会过度预测严重的健康状况。
使用直接响应映射技术,可以使用三种心脏特异性非偏好基础 QOL 工具将其映射到 MacNew-7D 效用上,具有良好的预测准确性。报告的映射算法可能有助于使用这些 QOL 工具进行经济评估的健康效用估计。