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验证成人脊柱患者中患者报告结果测量信息系统-通用健康量表(PROMIS-GH)向欧洲五维度健康量表(EQ-5D)的转换

Validating the Transformation of PROMIS-GH to EQ-5D in Adult Spine Patients.

作者信息

Panchagnula Shreyas, Sun Xin, Montejo Julio D, Nouri Aria, Kolb Luis, Virojanapa Justin, Camara-Quintana Joaquin Q, Sommaruga Samuel, Patel Kishan, Lakomkin Nikita, Abbed Khalid, Cheng Joseph S

机构信息

Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06511, USA.

Dartmouth Hitchcock Medical Center, Lebanon, NH 03756, USA.

出版信息

J Clin Med. 2019 Sep 20;8(10):1506. doi: 10.3390/jcm8101506.

Abstract

Spinal disorders and associated interventions are costly in the United States, putting them in the limelight of economic analyses. The Patient-Reported Outcomes Measurement Information System Global Health Survey (PROMIS-GHS) requires mapping to other surveys for economic investigation. Previous studies have proposed transformations of PROMIS-GHS to EuroQol 5-Dimension (EQ-5D) health index scores. These models require validation in adult spine patients. In our study, PROMIS-GHS and EQ-5D were randomly administered to 121 adult spine patients. The actual health index scores were calculated from the EQ-5D instrument and estimated scores were calculated from the PROMIS-GHS responses with six models. Goodness-of-fit for each model was determined using the coefficient of determination (), mean squared error (MSE), and mean absolute error (MAE). Among the models, the model treating the eight PROMIS-GHS items as categorical variables (CAT) was the optimal model with the highest (0.59) and lowest MSE (0.02) and MAE (0.11) in our spine sample population. Subgroup analysis showed good predictions of the mean EQ-5D by gender, age groups, education levels, etc. The transformation from PROMIS-GHS to EQ-5D had a high accuracy of mean estimate on a group level, but not at the individual level.

摘要

在美国,脊柱疾病及相关治疗成本高昂,这使其成为经济分析的焦点。患者报告结果测量信息系统全球健康调查(PROMIS-GHS)需要映射到其他调查以进行经济研究。以往研究已提出将PROMIS-GHS转换为欧洲五维健康量表(EQ-5D)健康指数得分的方法。这些模型需要在成年脊柱患者中进行验证。在我们的研究中,对121名成年脊柱患者随机进行了PROMIS-GHS和EQ-5D调查。根据EQ-5D工具计算实际健康指数得分,并使用六种模型根据PROMIS-GHS的回答计算估计得分。使用决定系数()、均方误差(MSE)和平均绝对误差(MAE)确定每个模型的拟合优度。在这些模型中,将八个PROMIS-GHS项目视为分类变量的模型(CAT)是最优模型,在我们的脊柱样本人群中具有最高的(0.59)、最低的MSE(0.02)和MAE(0.11)。亚组分析显示,按性别、年龄组、教育水平等对EQ-5D均值有良好预测。从PROMIS-GHS到EQ-5D的转换在组水平上对均值估计具有较高准确性,但在个体水平上则不然。

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