将堪萨斯城心肌病、西雅图心绞痛和明尼苏达州心力衰竭患者的生活与心脏病患者的 MacNew-7D 映射。

IF 3.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Quality of Life Research Pub Date : 2024-08-01 Epub Date: 2024-06-05 DOI:10.1007/s11136-024-03676-2
Sameera Senanayake, Rithika Uchil, Pakhi Sharma, William Parsonage, Sanjeewa Kularatna
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引用次数: 0

摘要

导言:堪萨斯城心肌病问卷 (KCCQ)、西雅图心绞痛问卷 (SAQ) 和明尼苏达心力衰竭患者生活问卷 (MLHFQ) 是广泛使用的非偏好型工具,用于测量心脏病患者与健康相关的生活质量 (QOL)。然而,目前还无法使用这些工具估算出用于经济评估的质量调整生命年(QALYs),因为得出的汇总分数并非基于偏好。MacNew-7D 是一种基于偏好的特定心脏病工具。本研究提供了不同的映射算法,用于将效用分数分配给 MacNew-7D 中的 KCCQ、MLHFQ 和 SAQ,以计算用于经济评估的 QALYs:研究纳入了 493 名心衰或心绞痛患者,他们填写了 KCCQ、MLHFQ、SAQ 和 MacNew-7D 问卷。采用伽马广义线性模型(GLM)、贝叶斯 GLM、逐步选择线性回归和随机森林等回归技术来开发直接映射算法。由于缺乏外部验证数据集,因此采用了交叉验证。这项研究遵循了 "基于偏好的测量方法映射报告标准核对表":使用 KCCQ、MLHFQ 和 SAQ 项目和领域得分确定了预测 MacNew-7D 实用性得分的最佳模型。随机森林模型在所有问卷的项目得分和 KCCQ 的领域得分方面表现良好,而贝叶斯 GLM 和线性回归模型在 MLHFQ 和 SAQ 领域得分方面表现最佳。然而,模型往往会过度预测严重的健康状况:结论:使用两种直接反应映射技术,可以将三种心脏特异性非偏好型 QOL 工具映射到 MacNew-7D 工具上,并具有良好的预测准确性。所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算。
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Mapping Kansas City cardiomyopathy, Seattle Angina, and minnesota living with heart failure to the MacNew-7D in patients with heart disease.

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.

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来源期刊
Quality of Life Research
Quality of Life Research 医学-公共卫生、环境卫生与职业卫生
CiteScore
6.50
自引率
8.60%
发文量
224
审稿时长
3-8 weeks
期刊介绍: Quality of Life Research is an international, multidisciplinary journal devoted to the rapid communication of original research, theoretical articles and methodological reports related to the field of quality of life, in all the health sciences. The journal also offers editorials, literature, book and software reviews, correspondence and abstracts of conferences. Quality of life has become a prominent issue in biometry, philosophy, social science, clinical medicine, health services and outcomes research. The journal''s scope reflects the wide application of quality of life assessment and research in the biological and social sciences. All original work is subject to peer review for originality, scientific quality and relevance to a broad readership. This is an official journal of the International Society of Quality of Life Research.
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