Predicting SF-6D utility scores from the neck disability index and numeric rating scales for neck and arm pain.
Predicting SF-6D utility scores from the neck disability index and numeric rating scales for neck and arm pain.
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DOI：
10.1097/brs.0b013e3181d323f3
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发表时间：
2011-03-15
期刊：
影响因子：
3
通讯作者：
Glassman SD
中科院分区：
文献类型：
--
作者：
Carreon LY;Anderson PA;McDonough CM;Djurasovic M;Glassman SD
本研究旨在使用NDI、颈部疼痛和手臂疼痛评分的数据提供一种估计SF-6D效用的算法。虽然成本效用分析越来越多地用于提供有关替代干预措施的相对价值的信息，但很少从临床试验数据中获得健康状态值或效用。颈部残疾指数（NDI）和颈部和手臂疼痛的数字评级量表是颈椎退行性疾病患者广泛使用的症状、功能和残疾的疾病特异性指标。本研究的目的是提供一种算法，允许使用NDI的数据以及颈部和手臂疼痛的数字评定量表估计SF-6D效用。在2080例因退行性疾病接受颈椎融合术的患者中，前瞻性收集术前、术后12个月和24个月的SF-36、NDI、颈部和手臂疼痛评定量表评分。计算SF-6D效用，并计算NDI、颈部和手臂疼痛评分与SF-6D效用评分之间多个时间点配对观察结果的斯皮尔曼相关系数。使用线性回归模型，根据NDI、颈部和手臂疼痛评分估计SF-6D评分。使用396例患者的单独独立数据集（其中NDI评分可用），估计每例受试者的SF-6D，并与其实际SF-6D进行比较。开发样本中受试者的平均年龄为50.4 ± 11.0岁，33%为男性。在验证样本中，平均年龄为53.1 ± 9.9岁，35%为男性。SF-6D与NDI、颈部和手臂疼痛评分之间的相关性具有统计学显著性（p<0.0001），相关系数分别为0.82、0.62和0.50。仅使用NDI预测SF-6D的回归方程的R2为0.66，均方根误差（RMSE）为0.056。在验证分析中，使用NDI回归模型，实际平均SF-6D评分（0.49 ± 0.08）与估计平均SF-6D评分（0.49 ± 0.08）之间无统计学显著差异（p=0.961）。这种基于回归的算法可能是一个有用的工具，以预测SF-6D评分的颈椎退行性疾病的研究，收集了NDI，但没有效用评分。
Cross-sectional cohort This study aims to provide an algorithm estimate SF-6D utilities using data from the NDI, neck pain and arm pain scores. Although cost-utility analysis is increasingly used to provide information about the relative value of alternative interventions, health state values or utilities are rarely available from clinical trial data. The Neck Disability Index (NDI) and numeric rating scales for neck and arm pain, are widely used disease-specific measures of symptoms, function and disability in patients with cervical degenerative disorders. The purpose of this study is to provide an algorithm to allow estimation of SF-6D utilities using data from the NDI, and numeric rating scales for neck and arm pain. SF-36, NDI, neck and arm pain rating scale scores were prospectively collected pre-operatively, at 12 and 24 months post-operatively in 2080 patients undergoing cervical fusion for degenerative disorders. SF-6D utilities were computed and Spearman correlation coefficients were calculated for paired observations from multiple time points between NDI, neck and arm pain scores and SF-6D utility scores. SF-6D scores were estimated from the NDI, neck and arm pain scores using a linear regression model. Using a separate, independent dataset of 396 patients in which and NDI scores were available SF-6D was estimated for each subject and compared to their actual SF-6D. The mean age for those in the development sample, was 50.4 ± 11.0 years and 33% were male. In the validation sample the mean age was 53.1 ± 9.9 years and 35% were male. Correlations between the SF-6D and the NDI, neck and arm pain scores were statistically significant (p<0.0001) with correlation coefficients of 0.82, 0.62, and 0.50 respectively. The regression equation using NDI alone to predict SF-6D had an R2 of 0.66 and a root mean square error (RMSE) of 0.056. In the validation analysis, there was no statistically significant difference (p=0.961) between actual mean SF-6D (0.49 ± 0.08) and the estimated mean SF-6D score (0.49 ± 0.08) using the NDI regression model. This regression-based algorithm may be a useful tool to predict SF-6D scores in studies of cervical degenerative disease that have collected NDI but not utility scores.